Understanding the Role of Virtual Outreach and Programming for LGBT Individuals in Later Life

Due to health disparities LGBT older adults may have more health care needs, but they are likely to have less informal sources of support. While efforts have been made to serve LGBT older adults, traditional forms of in person outreach and service may still be inaccessible to those living in rural areas, with restricted mobility, due to lack of transportation, during inclement weather, or in public health situations as the Covid-19 pandemic. We conducted focus group discussions to understand the role of virtual outreach in serving LGBT individuals' needs in their later years of life. Study participants expressed a desire for dating, community, aging in place, and affirming health care. However, their experience of internalized and institutional homophobia and ageism may act as barriers in fulfilling those needs. A dedicated virtual space has the potential to overcome these barriers by facilitating online get-togethers, support groups, dating events, having coming out resources, and exchanging information on LGBT friendly health services. Having a space to express their generativity may make such virtual services more empowering. Lack of technological access and privacy concerns may hinder the use of virtual services but can be overcome with training and education.

Neural Network-Based Algorithm for Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers: Retrospective Observation and Algorithm Development Study

Background

It is well established that lack of physical activity is detrimental to the overall health of an individual. Modern-day activity trackers enable individuals to monitor their daily activities to meet and maintain targets. This is expected to promote activity encouraging behavior, but the benefits of activity trackers attenuate over time due to waning adherence. One of the key approaches to improving adherence to goals is to motivate individuals to improve on their historic performance metrics.

Objective

The aim of this work was to build a machine learning model to predict an achievable weekly activity target by considering (1) patterns in the user’s activity tracker data in the previous week and (2) behavior and environment characteristics. By setting realistic goals, ones that are neither too easy nor too difficult to achieve, activity tracker users can be encouraged to continue to meet these goals, and at the same time, to find utility in their activity tracker.

Methods

We built a neural network model that prescribes a weekly activity target for an individual that can be realistically achieved. The inputs to the model were user-specific personal, social, and environmental factors, daily step count from the previous 7 days, and an entropy measure that characterized the pattern of daily step count. Data for training and evaluating the machine learning model were collected over a duration of 9 weeks.

Results

Of 30 individuals who were enrolled, data from 20 participants were used. The model predicted target daily count with a mean absolute error of 1545 (95% CI 1383-1706) steps for an 8-week period.

Conclusions

Artificial intelligence applied to physical activity data combined with behavioral data can be used to set personalized goals in accordance with the individual’s level of activity and thereby improve adherence to a fitness tracker; this could be used to increase engagement with activity trackers. A follow-up prospective study is ongoing to determine the performance of the engagement algorithm.

RECRUITING LGBT OLDER ADULTS FOR RESEARCH

LGBT older adults constitute a rare population and so are methodologically difficult to recruit. Due to stigma, many of them may not disclose their sexual/gender identity, which makes it challenging for researchers to reach out to them. Due to history of discrimination, LGBT older adults may not trust researchers. The purpose of this presentation is to discuss strategies used to recruit LGBT older adults to a study on exploring the idea of an online senior center for LGBT older adults in Massachusetts. Building a rapport with community stakeholders, developing trust and having LGBT older adults themselves as part of the research team were important tools to help overcome these challenges. LGBT older adults are very diverse and focused efforts should be made to recruit them from various racial/ethnic backgrounds, rural areas; also, those who are not publicly open about their identity, and who are home bound due to restricted mobility.

Factors Influencing Exercise Engagement When Using Activity Trackers: Nonrandomized Pilot Study

BACKGROUND

It is well reported that tracking physical activity can lead to sustained exercise routines, which can decrease disease risk. However, most stop using trackers within a couple months of initial use. The reasons people stop using activity trackers can be varied and personal. Understanding the reasons for discontinued use could lead to greater acceptance of tracking and more regular exercise engagement.

OBJECTIVE

The aim of this study was to determine the individualistic reasons for nonengagement with activity trackers.

METHODS

Overweight and obese participants (n=30) were enrolled and allowed to choose an activity tracker of their choice to use for 9 weeks. Questionnaires were administered at the beginning and end of the study to collect data on their technology use, as well as social, physiological, and psychological attributes that may influence tracker use. Closeout interviews were also conducted to further identify individual influencers and attributes. In addition, daily steps were collected from the activity tracker.

RESULTS

The results of the study indicate that participants typically valued the knowledge of their activity level the activity tracker provided, but it was not a sufficient motivator to overcome personal barriers to maintain or increase exercise engagement. Participants identified as extrinsically motivated were more influenced by wearing an activity tracker than those who were intrinsically motivated. During the study, participants who reported either owning multiple technology devices or knowing someone who used multiple devices were more likely to remain engaged with their activity tracker.

CONCLUSIONS

This study lays the foundation for developing a smart app that could promote individual engagement with activity trackers.

A Reinforcement Learning-Based Method for Management of Type 1 Diabetes: Exploratory Study

Background

Type 1 diabetes mellitus (T1DM) is characterized by chronic insulin deficiency and consequent hyperglycemia. Patients with T1DM require long-term exogenous insulin therapy to regulate blood glucose levels and prevent the long-term complications of the disease. Currently, there are no effective algorithms that consider the unique characteristics of T1DM patients to automatically recommend personalized insulin dosage levels.

Objective

The objective of this study was to develop and validate a general reinforcement learning (RL) framework for the personalized treatment of T1DM using clinical data.

Methods

This research presents a model-free data-driven RL algorithm, namely Q-learning, that recommends insulin doses to regulate the blood glucose level of a T1DM patient, considering his or her state defined by glycated hemoglobin (HbA_1c) levels, body mass index, engagement in physical activity, and alcohol usage. In this approach, the RL agent identifies the different states of the patient by exploring the patient’s responses when he or she is subjected to varying insulin doses. On the basis of the result of a treatment action at time step t, the RL agent receives a numeric reward, positive or negative. The reward is calculated as a function of the difference between the actual blood glucose level achieved in response to the insulin dose and the targeted HbA_1c level. The RL agent was trained on 10 years of clinical data of patients treated at the Mass General Hospital.

Results

A total of 87 patients were included in the training set. The mean age of these patients was 53 years, 59% (51/87) were male, 86% (75/87) were white, and 47% (41/87) were married. The performance of the RL agent was evaluated on 60 test cases. RL agent–recommended insulin dosage interval includes the actual dose prescribed by the physician in 53 out of 60 cases (53/60, 88%).

Conclusions

This exploratory study demonstrates that an RL algorithm can be used to recommend personalized insulin doses to achieve adequate glycemic control in patients with T1DM. However, further investigation in a larger sample of patients is needed to confirm these findings.

A randomized controlled trial of a novel artificial intelligence-based smartphone application to optimize the management of cancer-related pain

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Background: Cancer pain is a significant problem that impairs patient quality of life and increases healthcare utilization. ePAL is a smartphone application that utilizes patient-reported outcomes (PROs) and artificial intelligence (AI) to optimize cancer pain management. This randomized controlled trial examined the impact of ePAL on cancer pain severity, attitudes toward cancer pain, and healthcare utilization. Methods: Patients with pain from metastatic solid tumors (n = 112) undergoing treatment in a palliative care clinic were randomized to either a control group (n = 56) that received usual care or an intervention group (n = 56) that received ePAL in addition to usual care for 8 weeks. Measures of pain severity (Brief Pain Inventory), attitudes towards cancer treatment (Barriers Questionnaire II) and anxiety (General Anxiety Disorder-7) were assessed. We used repeated measures mixed modeling to assess change in outcome measures over time. We also conducted a chart review to identify pain-related hospital admissions and emergency department (ED) visits and compared risk between study groups. Results: Pain severity (BPI) and negative attitudes toward cancer treatment (BQ-II) decreased significantly for those assigned to ePAL compared to controls (ß = -0.09, p = 0.034 and ß = -0.037, p = 0.042, respectively). Patients assigned to ePAL reported higher anxiety scores compared to controls (ß = 0.21, p = 0.015). Patients assigned to ePAL had significantly fewer pain-related hospital admissions (n = 4 vs. n = 20, per patient risk ratio 0.31, p = 0.018) and fewer pain-related admissions through the ED (n = 2 vs. n = 14, per patient risk ratio 0.18, p = 0.008) compared to control group. Conclusions: To our knowledge, this is the first mobile app to utilize patient reported outcomes and artificial intelligence to significantly decrease pain scores and pain-related hospitalizations in patients with cancer-related pain. Future directions include examining the efficacy of ePAL in settings with limited access to palliative care.

Use of Electronic Health Records to Develop and Implement a Silent Best Practice Alert Notification System for Patient Recruitment in Clinical Research: Quality Improvement Initiative

BACKGROUND

Participant recruitment, especially for frail, elderly, hospitalized patients, remains one of the greatest challenges for many research groups. Traditional recruitment methods such as chart reviews are often inefficient, low-yielding, time consuming, and expensive. Best Practice Alert (BPA) systems have previously been used to improve clinical care and inform provider decision making, but the system has not been widely used in the setting of clinical research.

OBJECTIVE

The primary objective of this quality-improvement initiative was to develop, implement, and refine a silent Best Practice Alert (sBPA) system that could maximize recruitment efficiency.

METHODS

The captured duration of the screening sessions for both methods combined with the allotted research coordinator hours in the Emerald-COPD (chronic obstructive pulmonary disease) study budget enabled research coordinators to estimate the cost-efficiency.

RESULTS

Prior to implementation, the sBPA system underwent three primary stages of development. Ultimately, the final iteration produced a system that provided similar results as the manual Epic Reporting Workbench method of screening. A total of 559 potential participants who met the basic prescreen criteria were identified through the two screening methods. Of those, 418 potential participants were identified by both methods simultaneously, 99 were identified only by the Epic Reporting Workbench Method, and 42 were identified only by the sBPA method. Of those identified by the Epic Reporting Workbench, only 12 (of 99, 12.12%) were considered eligible. Of those identified by the sBPA method, 30 (of 42, 71.43%) were considered eligible. Using a side-by-side comparison of the sBPA and the traditional Epic Reporting Workbench method of screening, the sBPA screening method was shown to be approximately four times faster than our previous screening method and estimated a projected 442.5 hours saved over the duration of the study. Additionally, since implementation, the sBPA system identified the equivalent of three additional potential participants per week.

CONCLUSIONS

Automation of the recruitment process allowed us to identify potential participants in real time and find more potential participants who meet basic eligibility criteria. sBPA screening is a considerably faster method that allows for more efficient use of resources. This innovative and instrumental functionality can be modified to the needs of other research studies aiming to use the electronic medical records system for participant recruitment.

Provider- and Patient-Related Barriers to and Facilitators of Digital Health Technology Adoption for Hypertension Management: Scoping Review

Background

The uptake of digital health technology (DHT) has been surprisingly low in clinical practice. Despite showing great promise to improve patient outcomes and disease management, there is limited information on the factors that contribute to the limited adoption of DHT, particularly for hypertension management.

Objective

This scoping review provides a comprehensive summary of barriers to and facilitators of DHT adoption for hypertension management reported in the published literature with a focus on provider- and patient-related barriers and facilitators.

Methods

This review followed the methodological framework developed by Arskey and O’Malley. Systematic literature searches were conducted on PubMed or Medical Literature Analysis and Retrieval System Online, Cumulative Index to Nursing and Allied Health Literature, and Excerpta Medica database. Articles that reported on barriers to and/or facilitators of digital health adoption for hypertension management published in English between 2008 and 2017 were eligible. Studies not reporting on barriers or facilitators to DHT adoption for management of hypertension were excluded. A total of 2299 articles were identified based on the above criteria after removing duplicates, and they were assessed for eligibility. Of these, 2165 references did not meet the inclusion criteria. After assessing 134 studies in full text, 98 studies were excluded (full texts were either unavailable or studies did not fulfill the inclusion criteria), resulting in a final set of 32 articles. In addition, 4 handpicked articles were also included in the review, making it a total of 36 studies.

Results

A total of 36 studies were selected for data extraction after abstract and full-text screening by 2 independent reviewers. All conflicts were resolved by a third reviewer. Thematic analysis was conducted to identify major themes pertaining to barriers and facilitators of DHT from both provider and patient perspectives. The key facilitators of DHT adoption by physicians that were identified include ease of integration with clinical workflow, improvement in patient outcomes, and technology usability and technical support. Technology usability and timely technical support improved self-management and patient experience, and positive impact on patient-provider communication were most frequently reported facilitators for patients. Barriers to use of DHTs reported by physicians include lack of integration with clinical workflow, lack of validation of technology, and lack of technology usability and technical support. Finally, lack of technology usability and technical support, interference with patient-provider relationship, and lack of validation of technology were the most commonly reported barriers by patients.

Conclusions

Findings suggest the settings and context in which DHTs are implemented and individuals involved in implementation influence adoption. Finally, to fully realize the potential of digitally enabled hypertension management, there is a greater need to validate these technologies to provide patients and providers with reliable and accurate information on both clinical outcomes and cost effectiveness.

Corrigendum and Editorial Warning Regarding Use of the MMAS-8 Scale (A Remote Medication Monitoring System for Chronic Heart Failure Patients to Reduce Readmissions: A Two-Arm Randomized Pilot Study)

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Measuring instrumental activities of daily living in non-demented elderly: a comparison of the new performance-based Harvard Automated Phone Task with other functional assessments

BACKGROUND

Impairment in instrumental activities of daily living (IADL) may occur in the earliest stages of mild cognitive impairment (MCI). However, there are few reliable measures of IADL in MCI or that have a sufficient range of scores in clinically normal (CN) elderly. The objective of this pilot study was to examine the convergent validity of a phone performance-based IADL instrument, the Harvard Automated Phone Task (APT), designed to measure the earliest IADL changes in Alzheimer's disease (AD), with other sensitive performance-based and subjective measures of everyday functional capacity among CN and MCI participants.

METHODS

Twenty-nine CN and 17 MCI participants were administered the Harvard APT, the computer performance-based Czaja Functional Assessment Battery (CFAB), and the AD Cooperative Study ADL prevention instrument (ADCS ADL-PI) participant and study partner versions; in addition, 52 different CN and 7 MCI participants were administered the Harvard APT and the Subjective Study Partner and Participant-reported (SSPP) IADL scale. The Harvard APT was compared with the three other IADL assessments.

RESULTS

In both CN and MCI, better performance on the Harvard APT was associated with better performance on the CFAB. In CN, better performance on the Harvard APT was associated with better ADCS ADL-PI participant-reported IADL, while in MCI better performance on the Harvard APT was associated with better ADCS ADL-PI study partner-reported IADL. Furthermore, in CN better performance on the Harvard APT was associated with better SSPP-IADL participant and study partner-reported IADL.

CONCLUSIONS

In this small pilot study, the Harvard APT, a brief, self-administered, objective measure of IADL performance, appears to correlate well with other sensitive measures of everyday functioning, providing good preliminary convergent validity for this new measure. Moreover, it appears to perform well across both CN and MCI participants, which suggests that it is a promising measure of early, clinically meaningful functional change. This may not be the case as suggested in our small sample for subjective IADL scales that may perform differentially depending on the reporter (self vs. study partner) across the clinical spectrum possibly due to diminishing awareness of IADL difficulties in individuals who become cognitively impaired. Secondary prevention trials in AD have a great need for such ecologically valid and reliable measures of early IADL changes.

Assessing the Usability of an Automated Continuous Temperature Monitoring Device (iThermonitor) in Pediatric Patients: Non-Randomized Pilot Study

BACKGROUND

Fever is an important vital sign and often the first one to be assessed in a sick child. In acutely ill children, caregivers are expected to monitor a child's body temperature at home after an initial medical consult. Fever literacy of many caregivers is known to be poor, leading to fever phobia. In children with a serious illness, the responsibility of periodically monitoring temperature can add substantially to the already stressful experience of caring for a sick child.

OBJECTIVE

The objective of this pilot study was to assess the feasibility of using the iThermonitor, an automated temperature measurement device, for continuous temperature monitoring in postoperative and postchemotherapy pediatric patients.

METHODS

We recruited 25 patient-caregiver dyads from the Pediatric Surgery Department at the Massachusetts General Hospital (MGH) and the Pediatric Cancer Centers at the MGH and the Dana Farber Cancer Institute. Enrolled dyads were asked to use the iThermonitor device for continuous temperature monitoring over a 2-week period. Surveys were administered to caregivers at enrollment and at study closeout. Caregivers were also asked to complete a daily event-monitoring log. The Generalized Anxiety Disorder-7 item questionnaire was also used to assess caregiver anxiety at enrollment and closeout.

RESULTS

Overall, 19 participant dyads completed the study. All 19 caregivers reported to have viewed temperature data on the study-provided iPad tablet at least once per day, and more than a third caregivers did so six or more times per day. Of all participants, 74% (14/19) reported experiencing an out-of-range temperature alert at least once during the study. Majority of caregivers reported that it was easy to learn how to use the device and that they felt confident about monitoring their child's temperature with it. Only 21% (4/9) of caregivers reported concurrently using a device other than the iThermonitor to monitor their child's temperature during the study. Continuous temperature monitoring was not associated with an increase in caregiver anxiety.

CONCLUSIONS

The study results reveal that the iThermonitor is a highly feasible and easy-to-use device for continuous temperature monitoring in pediatric oncology and surgery patients.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02410252; https://clinicaltrials.gov/ct2/show/NCT02410252 (Archived by WebCite at http://www.webcitation.org/73LnO7hel).

A randomized controlled trial of a novel artificial-intelligence based smartphone application to optimize the management of cancer-related pain

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Background: Cancer pain affects 70-90% of advanced malignancy patients, resulting in impaired quality of life and increased healthcare utilization. Novel care delivery models are needed to optimize care for patients dealing with cancer-related pain in between clinic visits. ePAL is a smartphone application(app) that regularly monitors pain and uses artificial intelligence(AI) to differentiate urgent from non-urgent issues to intercede in real time. The purpose of this randomized controlled trial was to determine ePAL's impact on pain severity, attitudes toward cancer treatment, and healthcare utilization in patients with cancer pain. Methods: MGH Palliative Care Clinic Patients with pain from metastatic, solid-organ cancer (n=112) were recruited and randomized to either a control group (n=56) that received usual care or an intervention group (n=56) that used the ePAL app in addition to usual care for 8 weeks. The app assessed pain 3 times/week and questionnaires about pain (BPI), attitudes towards cancer treatment(BQ-II), and general anxiety(GAD-7) were given at 0, 4, and 8 weeks. A repeated measures mixed model approach assessed how outcome measures changed over time. Models controlled for baseline differences at enrollment and random slopes in addition to baseline depression score, age and sex(alpha=0.05). Results: Pain severity (BPI) and negative attitudes toward cancer pain treatment (BQ-II) decreased significantly for those using the app compared to controls(coeff. -0.09, 95% CI: -0.17, -0.007, p=0.034 and coeff. -0.037, 95% CI: -0.072, -0.001, p=0.042 respectively). Anxiety scores increased for those using ePAL compared to controls (coeff. 0.21, 95% CI: 0.039, 0.37, p=0.015). Over 8-weeks, ePAL users had 40% fewer inpatient hospital admissions compared to controls (n=15 vs. n=25, p=0.048). Conclusions: To our knowledge, this is the first mobile app to utilize AI and clinical algorithms to significantly decrease pain and reduce overall inpatient hospitalizations in patients with cancer-related pain. Clinical trial information: NCT02069743.

Predictive Modeling of 30-Day Emergency Hospital Transport of Patients Using a Personal Emergency Response System: Prognostic Retrospective Study

BACKGROUND

Telehealth programs have been successful in reducing 30-day readmissions and emergency department visits. However, such programs often focus on the costliest patients with multiple morbidities and last for only 30 to 60 days postdischarge. Inexpensive monitoring of elderly patients via a personal emergency response system (PERS) to identify those at high risk for emergency hospital transport could be used to target interventions and prevent avoidable use of costly readmissions and emergency department visits after 30 to 60 days of telehealth use.

OBJECTIVE

The objectives of this study were to (1) develop and validate a predictive model of 30-day emergency hospital transport based on PERS data; and (2) compare the model's predictions with clinical outcomes derived from the electronic health record (EHR).

METHODS

We used deidentified medical alert pattern data from 290,434 subscribers to a PERS service to build a gradient tree boosting-based predictive model of 30-day hospital transport, which included predictors derived from subscriber demographics, self-reported medical conditions, caregiver network information, and up to 2 years of retrospective PERS medical alert data. We evaluated the model's performance on an independent validation cohort (n=289,426). We linked EHR and PERS records for 1815 patients from a home health care program to compare PERS-based risk scores with rates of emergency encounters as recorded in the EHR.

RESULTS

In the validation cohort, 2.22% (6411/289,426) of patients had 1 or more emergency transports in 30 days. The performance of the predictive model of emergency hospital transport, as evaluated by the area under the receiver operating characteristic curve, was 0.779 (95% CI 0.774-0.785). Among the top 1% of predicted high-risk patients, 25.5% had 1 or more emergency hospital transports in the next 30 days. Comparison with clinical outcomes from the EHR showed 3.9 times more emergency encounters among predicted high-risk patients than low-risk patients in the year following the prediction date.

CONCLUSIONS

Patient data collected remotely via PERS can be used to reliably predict 30-day emergency hospital transport. Clinical observations from the EHR showed that predicted high-risk patients had nearly four times higher rates of emergency encounters than did low-risk patients. Health care providers could benefit from our validated predictive model by targeting timely preventive interventions to high-risk patients. This could lead to overall improved patient experience, higher quality of care, and more efficient resource utilization.

Validating a Machine Learning Algorithm to Predict 30-Day Re-Admissions in Patients With Heart Failure: Protocol for a Prospective Cohort Study

BACKGROUND

Big data solutions, particularly machine learning predictive algorithms, have demonstrated the ability to unlock value from data in real time in many settings outside of health care. Rapid growth in electronic medical record adoption and the shift from a volume-based to a value-based reimbursement structure in the US health care system has spurred investments in machine learning solutions. Machine learning methods can be used to build flexible, customized, and automated predictive models to optimize resource allocation and improve the efficiency and quality of health care. However, these models are prone to the problems of overfitting, confounding, and decay in predictive performance over time. It is, therefore, necessary to evaluate machine learning-based predictive models in an independent dataset before they can be adopted in the clinical practice. In this paper, we describe the protocol for independent, prospective validation of a machine learning-based model trained to predict the risk of 30-day re-admission in patients with heart failure.

OBJECTIVE

This study aims to prospectively validate a machine learning-based predictive model for inpatient admissions in patients with heart failure by comparing its predictions of risk for 30-day re-admissions against outcomes observed prospectively in an independent patient cohort.

METHODS

All adult patients with heart failure who are discharged alive from an inpatient admission will be prospectively monitored for 30-day re-admissions through reports generated by the electronic medical record system. Of these, patients who are part of the training dataset will be excluded to avoid information leakage to the algorithm. An expected sample size of 1228 index admissions will be required to observe a minimum of 100 30-day re-admission events. Deidentified structured and unstructured data will be fed to the algorithm, and its prediction will be recorded. The overall model performance will be assessed using the concordance statistic. Furthermore, multiple discrimination thresholds for screening high-risk patients will be evaluated according to the sensitivity, specificity, predictive values, and estimated cost savings to our health care system.

RESULTS

The project received funding in April 2017 and data collection began in June 2017. Enrollment was completed in July 2017. Data analysis is currently underway, and the first results are expected to be submitted for publication in October 2018.

CONCLUSIONS

To the best of our knowledge, this is one of the first studies to prospectively evaluate a predictive machine learning algorithm in a real-world setting. Findings from this study will help to measure the robustness of predictions made by machine learning algorithms and set a realistic benchmark for expectations of gains that can be made through its application to health care.

REGISTERED REPORT IDENTIFIER

RR1-10.2196/9466.

Use of user-centered design to create a smartphone application for patient-reported outcomes in atopic dermatitis

The ubiquity and convenience of smartphones carries great potential for collecting patient-reported data to address many gaps in research, especially those that rely on ongoing, real-time data collection. Health care apps have often suffered from low utility due to lack of consideration of the needs of multiple stakeholders. We employed an iterative user-centered design approach to create the myEczema smartphone application (app) to study the burden of disease of atopic dermatitis. We outline below the steps we took for developing myEczema for multiple stakeholders, including patients, clinicians, and researchers.

Health Care Cost Analyses for Exploring Cost Savings Opportunities in Older Patients: Longitudinal Retrospective Study

BACKGROUND

Half of Medicare reimbursement goes toward caring for the top 5% of the most expensive patients. However, little is known about these patients prior to reaching the top or how their costs change annually. To address these gaps, we analyzed patient flow and associated health care cost trends over 5 years.

OBJECTIVE

To evaluate the cost of health care utilization in older patients by analyzing changes in their long-term expenditures.

METHODS

This was a retrospective, longitudinal, multicenter study to evaluate health care costs of 2643 older patients from 2011 to 2015. All patients had at least one episode of home health care during the study period and used a personal emergency response service (PERS) at home for any length of time during the observation period. We segmented all patients into top (5%), middle (6%-50%), and bottom (51%-100%) segments by their annual expenditures and built cost pyramids based thereon. The longitudinal health care expenditure trends of the complete study population and each segment were assessed by linear regression models. Patient flows throughout the segments of the cost acuity pyramids from year to year were modeled by Markov chains.

RESULTS

Total health care costs of the study population nearly doubled from US $17.7M in 2011 to US $33.0M in 2015 with an expected annual cost increase of US $3.6M (P=.003). This growth was primarily driven by a significantly higher cost increases in the middle segment (US $2.3M, P=.003). The expected annual cost increases in the top and bottom segments were US $1.2M (P=.008) and US $0.1M (P=.004), respectively. Patient and cost flow analyses showed that 18% of patients moved up the cost acuity pyramid yearly, and their costs increased by 672%. This was in contrast to 22% of patients that moved down with a cost decrease of 86%. The remaining 60% of patients stayed in the same segment from year to year, though their costs also increased by 18%.

CONCLUSIONS

Although many health care organizations target intensive and costly interventions to their most expensive patients, this analysis unveiled potential cost savings opportunities by managing the patients in the lower cost segments that are at risk of moving up the cost acuity pyramid. To achieve this, data analytics integrating longitudinal data from electronic health records and home monitoring devices may help health care organizations optimize resources by enabling clinicians to proactively manage patients in their home or community environments beyond institutional settings and 30- and 60-day telehealth services.

A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data

BACKGROUND

Heart failure is one of the leading causes of hospitalization in the United States. Advances in big data solutions allow for storage, management, and mining of large volumes of structured and semi-structured data, such as complex healthcare data. Applying these advances to complex healthcare data has led to the development of risk prediction models to help identify patients who would benefit most from disease management programs in an effort to reduce readmissions and healthcare cost, but the results of these efforts have been varied. The primary aim of this study was to develop a 30-day readmission risk prediction model for heart failure patients discharged from a hospital admission.

METHODS

We used longitudinal electronic medical record data of heart failure patients admitted within a large healthcare system. Feature vectors included structured demographic, utilization, and clinical data, as well as selected extracts of un-structured data from clinician-authored notes. The risk prediction model was developed using deep unified networks (DUNs), a new mesh-like network structure of deep learning designed to avoid over-fitting. The model was validated with 10-fold cross-validation and results compared to models based on logistic regression, gradient boosting, and maxout networks. Overall model performance was assessed using concordance statistic. We also selected a discrimination threshold based on maximum projected cost saving to the Partners Healthcare system.

RESULTS

Data from 11,510 patients with 27,334 admissions and 6369 30-day readmissions were used to train the model. After data processing, the final model included 3512 variables. The DUNs model had the best performance after 10-fold cross-validation. AUCs for prediction models were 0.664 ± 0.015, 0.650 ± 0.011, 0.695 ± 0.016 and 0.705 ± 0.015 for logistic regression, gradient boosting, maxout networks, and DUNs respectively. The DUNs model had an accuracy of 76.4% at the classification threshold that corresponded with maximum cost saving to the hospital.

CONCLUSIONS

Deep learning techniques performed better than other traditional techniques in developing this EMR-based prediction model for 30-day readmissions in heart failure patients. Such models can be used to identify heart failure patients with impending hospitalization, enabling care teams to target interventions at their most high-risk patients and improving overall clinical outcomes.

Evaluating the Impact of a Web-Based Risk Assessment System (CareSage) and Tailored Interventions on Health Care Utilization: Protocol for a Randomized Controlled Trial

Background

Soaring health care costs and a rapidly aging population, with multiple comorbidities, necessitates the development of innovative strategies to deliver high-quality, value-based care.

Objective

The goal of this study is to evaluate the impact of a risk assessment system (CareSage) and targeted interventions on health care utilization.

Methods

This is a two-arm randomized controlled trial recruiting 370 participants from a pool of high-risk patients receiving care at a home health agency. CareSage is a risk assessment system that utilizes both real-time data collected via a Personal Emergency Response Service and historical patient data collected from the electronic medical records. All patients will first be observed for 3 months (observation period) to allow the CareSage algorithm to calibrate based on patient data. During the next 6 months (intervention period), CareSage will use a predictive algorithm to classify patients in the intervention group as “high” or “low” risk for emergency transport every 30 days. All patients flagged as “high risk” by CareSage will receive nurse triage calls to assess their needs and personalized interventions including patient education, home visits, and tele-monitoring. The primary outcome is the number of 180-day emergency department visits. Secondary outcomes include the number of 90-day emergency department visits, total medical expenses, 180-day mortality rates, time to first readmission, total number of readmissions and avoidable readmissions, 30-, 90-, and 180-day readmission rates, as well as cost of intervention per patient. The two study groups will be compared using the Student t test (two-tailed) for normally distributed and Mann Whitney U test for skewed continuous variables, respectively. The chi-square test will be used for categorical variables. Time to event (readmission) and 180-day mortality between the two study groups will be compared by using the Kaplan-Meier survival plots and the log-rank test. Cox proportional hazard regression will be used to compute hazard ratio and compare outcomes between the two groups.

Results

We are actively enrolling participants and the study is expected to be completed by end of 2018; results are expected to be published in early 2019.

Conclusions

Innovative solutions for identifying high-risk patients and personalizing interventions based on individual risk and needs may help facilitate the delivery of value-based care, improve long-term patient health outcomes and decrease health care costs.

Trial Registration

ClinicalTrials.gov NCT03126565; https://clinicaltrials.gov/ct2/show/NCT03126565 (Archived by WebCite at http://www.webcitation.org/6ymDuAwQA).

Evaluating the Usability and Usefulness of a Mobile App for Atrial Fibrillation Using Qualitative Methods: Exploratory Pilot Study

BACKGROUND

Atrial fibrillation (AFib) is the most common form of heart arrhythmia and a potent risk factor for stroke. Nonvitamin K antagonist oral anticoagulants (NOACs) are routinely prescribed to manage AFib stroke risk; however, nonadherence to treatment is a concern. Additional tools that support self-care and medication adherence may benefit patients with AFib.

OBJECTIVE

The aim of this study was to evaluate the perceived usability and usefulness of a mobile app designed to support self-care and treatment adherence for AFib patients who are prescribed NOACs.

METHODS

A mobile app to support AFib patients was previously developed based on early stage interview and usability test data from clinicians and patients. An exploratory pilot study consisting of naturalistic app use, surveys, and semistructured interviews was then conducted to examine patients' perceptions and everyday use of the app.

RESULTS

A total of 12 individuals with an existing diagnosis of nonvalvular AFib completed the 4-week study. The average age of participants was 59 years. All participants somewhat or strongly agreed that the app was easy to use, and 92% (11/12) reported being satisfied or very satisfied with the app. Participant feedback identified changes that may improve app usability and usefulness for patients with AFib. Areas of usability improvement were organized by three themes: app navigation, clarity of app instructions and design intent, and software bugs. Perceptions of app usefulness were grouped by three key variables: core needs of the patient segment, patient workflow while managing AFib, and the app's ability to support the patient's evolving needs.

CONCLUSIONS

The results of this study suggest that mobile tools that target self-care and treatment adherence may be helpful to AFib patients, particularly those who are newly diagnosed. Additionally, participant feedback provided insight into the varied needs and health experiences of AFib patients, which may improve the design and targeting of the intervention. Pilot studies that qualitatively examine patient perceptions of usability and usefulness are a valuable and often underutilized method for assessing the real-world acceptability of an intervention. Additional research evaluating the AFib Connect mobile app over a longer period, and including a larger, more diverse sample of AFib patients, will be helpful for understanding whether the app is perceived more broadly to be useful and effective in supporting patient self-care and medication adherence.

Designing Patient-Centered Text Messaging Interventions for Increasing Physical Activity Among Participants With Type 2 Diabetes: Qualitative Results From the Text to Move Intervention

Background

Type 2 diabetes mellitus (T2DM) is a disease affecting approximately 29.1 million people in the United States, and an additional 86 million adults have prediabetes. Diabetes self-management education, a complex health intervention composed of 7 behaviors, is effective at improving self-care behaviors and glycemic control. Studies have employed text messages for education, reminders, and motivational messaging that can serve as “cues to action,” aiming to improve glucose monitoring, self-care behaviors, appointment attendance, and medication adherence.

Objectives

The Text to Move (TTM) study was a 6-month 2-parallel group randomized controlled trial of individuals with T2DM to increase physical activity, measured by a pedometer. The intervention arm received text messages twice daily for 6 months that were tailored to the participant’s stage of behavior change as defined by the transtheoretical model of behavior change.

Methods

We assessed participants’ attitudes regarding their experience with text messaging, focusing on perceived barriers and facilitators, through two focus groups and telephone interviews. All interviews were audiorecorded, transcribed verbatim, coded, and analyzed using a grounded theory approach.

Results

The response rate was 67% (31/46 participants). The average age was 51.4 years and 61% (19/31 participants) were male. The majority of individuals were English speakers and married, had completed at least 12th grade and approximately half of the participants were employed full-time. Overall, participants were satisfied with the TTM program and recalled the text messages as educational, informational, and motivational. Program involvement increased the sense of connection with their health care center. The wearing of pedometers and daily step count information served as motivational reminders and created a sense of accountability through the sentinel effect. However, there was frustration concerning the automation of the text message program, including the repetitiveness, predictability of text time delivery, and lack of customization and interactivity of text message content. Participants recommended personalization of texting frequency as well as more contact time with personnel for a stronger sense of support, including greater surveillance and feedback based on their own results and comparison to other participants.

Conclusions

Participants in a theory-based text messaging intervention identified key facilitators and barriers to program efficacy that should be incorporated into future texting interventions to optimize participant satisfaction and outcomes.

Trial Registration

Clinicaltrials.gov NCT01569243; http://clinicaltrials.gov/ct2/show/NCT01569243 (Archived by Webcite at http://www.webcitation.org/6pfH6yXag)

Mobile Application to Promote Adherence to Oral Chemotherapy and Symptom Management: A Protocol for Design and Development

Background

Oral chemotherapy is increasingly used in place of traditional intravenous chemotherapy to treat patients with cancer. While oral chemotherapy includes benefits such as ease of administration, convenience, and minimization of invasive infusions, patients receive less oversight, support, and symptom monitoring from clinicians. Additionally, adherence is a well-documented challenge for patients with cancer prescribed oral chemotherapy regimens. With the ever-growing presence of smartphones and potential for efficacious behavioral intervention technology, we created a mobile health intervention for medication and symptom management.

Objective

The objective of this study was to develop and evaluate the usability and acceptability of a smartphone app to support adherence to oral chemotherapy and symptom management in patients with cancer.

Methods

We used a 5-step development model to create a comprehensive mobile app with theoretically informed content. The research and technical development team worked together to develop and iteratively test the app. In addition to the research team, key stakeholders including patients and family members, oncology clinicians, health care representatives, and practice administrators contributed to the content refinement of the intervention. Patient and family members also participated in alpha and beta testing of the final prototype to assess usability and acceptability before we began the randomized controlled trial.

Results

We incorporated app components based on the stakeholder feedback we received in focus groups and alpha and beta testing. App components included medication reminders, self-reporting of medication adherence and symptoms, an education library including nutritional information, Fitbit integration, social networking resources, and individually tailored symptom management feedback. We are conducting a randomized controlled trial to determine the effectiveness of the app in improving adherence to oral chemotherapy, quality of life, and burden of symptoms and side effects. At every stage in this trial, we are engaging stakeholders to solicit feedback on our progress and next steps.

Conclusions

To our knowledge, we are the first to describe the development of an app designed for people taking oral chemotherapy. The app addresses many concerns with oral chemotherapy, such as medication adherence and symptom management. Soliciting feedback from stakeholders with broad perspectives and expertise ensured that the app was acceptable and potentially beneficial for patients, caregivers, and clinicians. In our development process, we instantiated 7 of the 8 best practices proposed in a recent review of mobile health app development. Our process demonstrated the importance of effective communication between research groups and technical teams, as well as meticulous planning of technical specifications before development begins. Future efforts should consider incorporating other proven strategies in software, such as gamification, to bolster the impact of mobile health apps. Forthcoming results from our randomized controlled trial will provide key data on the effectiveness of this app in improving medication adherence and symptom management.

Trial Registration

ClinicalTrials.gov NCT02157519; https://clinicaltrials.gov/ct2/show/NCT02157519 (Archived by WebCite at http://www.webcitation.org/6prj3xfKA)

Healthcare utilization in older patients using personal emergency response systems: an analysis of electronic health records and medical alert data : Brief Description: A Longitudinal Retrospective Analyses of healthcare utilization rates in older patients using Personal Emergency Response Systems from 2011 to 2015

BACKGROUND

Personal Emergency Response Systems (PERS) are traditionally used as fall alert systems for older adults, a population that contributes an overwhelming proportion of healthcare costs in the United States. Previous studies focused mainly on qualitative evaluations of PERS without a longitudinal quantitative evaluation of healthcare utilization in users. To address this gap and better understand the needs of older patients on PERS, we analyzed longitudinal healthcare utilization trends in patients using PERS through the home care management service of a large healthcare organization.

METHODS

Retrospective, longitudinal analyses of healthcare and PERS utilization records of older patients over a 5-years period from 2011-2015. The primary outcome was to characterize the healthcare utilization of PERS patients. This outcome was assessed by 30-, 90-, and 180-day readmission rates, frequency of principal admitting diagnoses, and prevalence of conditions leading to potentially avoidable admissions based on Centers for Medicare and Medicaid Services classification criteria.

RESULTS

The overall 30-day readmission rate was 14.2%, 90-days readmission rate was 34.4%, and 180-days readmission rate was 42.2%. While 30-day readmission rates did not increase significantly (p = 0.16) over the study period, 90-days (p = 0.03) and 180-days (p = 0.04) readmission rates did increase significantly. The top 5 most frequent principal diagnoses for inpatient admissions included congestive heart failure (5.7%), chronic obstructive pulmonary disease (4.6%), dysrhythmias (4.3%), septicemia (4.1%), and pneumonia (4.1%). Additionally, 21% of all admissions were due to conditions leading to potentially avoidable admissions in either institutional or non-institutional settings (16% in institutional settings only).

CONCLUSIONS

Chronic medical conditions account for the majority of healthcare utilization in older patients using PERS. Results suggest that PERS data combined with electronic medical records data can provide useful insights that can be used to improve health outcomes in older patients.

Text to Move: A Randomized Controlled Trial of a Text-Messaging Program to Improve Physical Activity Behaviors in Patients With Type 2 Diabetes Mellitus

Background

Text messages are increasingly being used because of the low cost and the ubiquitous nature of mobile phones to engage patients in self-care behaviors. Self-care is particularly important in achieving treatment outcomes in type 2 diabetes mellitus (T2DM).

Objective

This study examined the effect of personalized text messages on physical activity, as measured by a pedometer, and clinical outcomes in a diverse population of patients with T2DM.

Methods

Text to Move (TTM) incorporates physical activity monitoring and coaching to provide automated and personalized text messages to help patients with T2DM achieve their physical activity goals. A total of 126 English- or Spanish-speaking patients with glycated hemoglobin A_1c (HbA_1c) >7 were enrolled in-person to participate in the study for 6 months and were randomized into either the intervention arm that received the full complement of the intervention or a control arm that received only pedometers. The primary outcome was change in physical activity. We also assessed the effect of the intervention on HbA_1c, weight, and participant engagement.

Results

All participants (intervention: n=64; control: n=62) were included in the analyses. The intervention group had significantly higher monthly step counts in the third (risk ratio [RR] 4.89, 95% CI 1.20 to 19.92, P=.03) and fourth (RR 6.88, 95% CI 1.21 to 39.00, P=.03) months of the study compared to the control group. However, over the 6-month follow-up period, monthly step counts did not differ statistically by group (intervention group: 9092 steps; control group: 3722 steps; RR 2.44, 95% CI 0.68 to 8.74, P=.17). HbA_1c decreased by 0.07% (95% CI –0.47 to 0.34, P=.75) in the TTM group compared to the control group. Within groups, HbA_1c decreased significantly from baseline in the TTM group by –0.43% (95% CI –0.75 to –0.12, P=.01), but nonsignificantly in the control group by –0.21% (95% CI –0.49 to 0.06, P=.13). Similar changes were observed for other secondary outcomes.

Conclusion

Personalized text messaging can be used to improve outcomes in patients with T2DM by employing optimal patient engagement measures.

Telemedical Education: Training Digital Natives in Telemedicine

Telemedicine plays an important role in the delivery of medical care, and will become increasingly prominent going forward. Current medical students are among the first generation of “digital natives” who are well versed in the incorporation of technology into social interaction. These students are well positioned to apply advances in communications to patient care. Even so, providers require training to effectively leverage these opportunities. Therefore, we recommend introducing telemedicine training into medical school curricula and propose a model for incorporation.

A Multimodal mHealth Intervention (FeatForward) to Improve Physical Activity Behavior in Patients with High Cardiometabolic Risk Factors: Rationale and Protocol for a Randomized Controlled Trial

Background

Physical inactivity is one of the leading risk factors contributing to the rising rates of chronic diseases and has been associated with deleterious health outcomes in patients with chronic disease conditions. We developed a mobile phone app, FeatForward, to increase the level of physical activity in patients with cardiometabolic risk (CMR) factors. This intervention is expected to result in an overall improvement in patient health outcomes.

Objective

The objective of this study is to evaluate the effect of a mobile phone–based app, FeatForward, on physical activity levels and other CMR factors in patients with chronic conditions.

Methods

The study will be implemented as a 2-arm randomized controlled trial with 300 adult patients with chronic conditions over a 6-month follow-up period. Participants will be assigned to either the intervention group receiving the FeatForward app and standard care versus a control group who will receive only usual care. The difference in physical activity levels between the control group and intervention group will be measured as the primary outcome. We will also evaluate the effect of this intervention on secondary measures including clinical outcome changes in global CMR factors (glycated hemoglobin, fasting blood glucose, blood pressure, waist circumference, Serum lipids, C-reactive protein), health-related quality of life, health care usage, including attendance of scheduled clinic visits and hospitalizations, usability, and satisfaction, participant engagement with the FeatForward app, physician engagement with physician portal, and willingness to engage in physical activity. Instruments that will be used in evaluating secondary outcomes include the Short-Form (SF)-12, app usability and satisfaction questionnaires, physician satisfaction questionnaire. The intention-to-treat approach will be used to evaluate outcomes. All outcomes will be measured longitudinally at baseline, midpoint (3 months), and 6 months. Our primary outcome, physical activity, will be assessed by mixed-model analysis of variance with intervention assignment as between-group factor and time as within-subject factor. A similar approach will be used to analyze continuous secondary outcomes while categorical outcomes will be analyzed by chi-square test.

Results

The study is still in progress and we hope to have the results by the end of 2016.

Conclusions

The mobile phone–based app, FeatForward, could lead to significant improvements in physical activity and other CMR factors in patients.

A Remote Medication Monitoring System for Chronic Heart Failure Patients to Reduce Readmissions: A Two-Arm Randomized Pilot Study

Background

Heart failure (HF) is a chronic condition affecting nearly 5.7 million Americans and is a leading cause of morbidity and mortality. With an aging population, the cost associated with managing HF is expected to more than double from US $31 billion in 2012 to US $70 billion by 2030. Readmission rates for HF patients are high—25% are readmitted at 30 days and nearly 50% at 6 months. Low medication adherence contributes to poor HF management and higher readmission rates. Remote telehealth monitoring programs aimed at improved medication management and adherence may improve HF management and reduce readmissions.

Objective

The primary goal of this randomized controlled pilot study is to compare the MedSentry remote medication monitoring system versus usual care in older HF adult patients who recently completed a HF telemonitoring program. We hypothesized that remote medication monitoring would be associated with fewer unplanned hospitalizations and emergency department (ED) visits, increased medication adherence, and improved health-related quality of life (HRQoL) compared to usual care.

Methods

Participants were randomized to usual care or use of the remote medication monitoring system for 90 days. Twenty-nine participants were enrolled and the final analytic sample consisted of 25 participants. Participants completed questionnaires at enrollment and closeout to gather data on medication adherence, health status, and HRQoL. Electronic medical records were reviewed for data on baseline classification of heart function and the number of unplanned hospitalizations and ED visits during the study period.

Results

Use of the medication monitoring system was associated with an 80% reduction in the risk of all-cause hospitalization and a significant decrease in the number of all-cause hospitalization length of stay in the intervention arm compared to usual care. Objective device data indicated high adherence rates (95%-99%) among intervention group participants despite finding no significant difference in self-reported adherence between study arms. The intervention group had poorer heart function and HRQoL at baseline, and HRQoL declined significantly in the intervention group compared to controls.

Conclusions

The MedSentry medication monitoring system is a promising technology that merits continued development and evaluation. The MedSentry medication monitoring system may be useful both as a standalone system for patients with complex medication regimens or used to complement existing HF telemonitoring interventions. We found significant reductions in risk of all-cause hospitalization and the number of all-cause length of stay in the intervention group compared to controls. Although HRQoL deteriorated significantly in the intervention group, this may have been due to the poorer HF-functioning at baseline in the intervention group compared to controls. Telehealth medication adherence technologies, such as the MedSentry medication monitoring system, are a promising method to improve patient self-management,the quality of patient care, and reduce health care utilization and expenditure for patients with HF and other chronic diseases that require complex medication regimens.

Trial Registration

ClinicalTrials.gov NCT01814696; https://clinicaltrials.gov/ct2/show/study/NCT01814696 (Archived by WebCite® at http://www.webcitation.org/6giqAVhno)

Personalized Telehealth in the Future: A Global Research Agenda

As telehealth plays an even greater role in global health care delivery, it will be increasingly important to develop a strong evidence base of successful, innovative telehealth solutions that can lead to scalable and sustainable telehealth programs. This paper has two aims: (1) to describe the challenges of promoting telehealth implementation to advance adoption and (2) to present a global research agenda for personalized telehealth within chronic disease management. Using evidence from the United States and the European Union, this paper provides a global overview of the current state of telehealth services and benefits, presents fundamental principles that must be addressed to advance the status quo, and provides a framework for current and future research initiatives within telehealth for personalized care, treatment, and prevention. A broad, multinational research agenda can provide a uniform framework for identifying and rapidly replicating best practices, while concurrently fostering global collaboration in the development and rigorous testing of new and emerging telehealth technologies. In this paper, the members of the Transatlantic Telehealth Research Network offer a 12-point research agenda for future telehealth applications within chronic disease management.

The Harvard Automated Phone Task: new performance-based activities of daily living tests for early Alzheimer's disease

BACKGROUND

Impairment in activities of daily living is a major burden for Alzheimer's disease dementia patients and caregivers. Multiple subjective scales and a few performance-based instruments have been validated and proven to be reliable in measuring instrumental activities of daily living in Alzheimer's disease dementia but less so in amnestic mild cognitive impairment and preclinical Alzheimer's disease.

OBJECTIVE

To validate the Harvard Automated Phone Task, a new performance-based activities of daily living test for early Alzheimer's disease, which assesses high level tasks that challenge seniors in daily life.

DESIGN

In a cross-sectional study, the Harvard Automated Phone Task was associated with demographics and cognitive measures through univariate and multivariate analyses; ability to discriminate across diagnostic groups was assessed; test-retest reliability with the same and alternate versions was assessed in a subset of participants; and the relationship with regional cortical thickness was assessed in a subset of participants.

SETTING

Academic clinical research center.

PARTICIPANTS

One hundred and eighty two participants were recruited from the community (127 clinically normal elderly and 45 young normal participants) and memory disorders clinics at Brigham and Women's Hospital and Massachusetts General Hospital (10 participants with mild cognitive impairment).

MEASUREMENTS

As part of the Harvard Automated Phone Task, participants navigated an interactive voice response system to refill a prescription (APT-Script), select a new primary care physician (APT-PCP), and make a bank account transfer and payment (APT-Bank). The 3 tasks were scored based on time, errors, and repetitions from which composite z-scores were derived, as well as a separate report of correct completion of the task.

RESULTS

We found that the Harvard Automated Phone Task discriminated well between diagnostic groups (APT-Script: p=0.002; APT-PCP: p<0.001; APT-Bank: p=0.02), had an incremental level of difficulty, and had excellent test-retest reliability (Cronbach's α values of 0.81 to 0.87). Within the clinically normal elderly, there were significant associations in multivariate models between performance on the Harvard Automated Phone Task and executive function (APT-PCP: p<0.001), processing speed (APT-Script: p=0.005), and regional cortical atrophy (APT-PCP: p=0.001; no significant association with APT-Script) independent of hearing acuity, motor speed, age, race, education, and premorbid intelligence.

CONCLUSIONS

Our initial experience with the Harvard Automated Phone Task, which consists of ecologically valid, easily-administered measures of daily activities, suggests that these tasks could be useful for screening and tracking the earliest functional alterations in preclinical and early prodromal AD.

Prescription Tablets in the Digital Age: A Cross-Sectional Study Exploring Patient and Physician Attitudes Toward the Use of Tablets for Clinic-Based Personalized Health Care Information Exchange

BACKGROUND

To reduce the cost of health care while increasing efficiency and quality, health systems are seeking innovative means to engage and empower patients. Improved use of information technology and electronic health record (EHR) infrastructure is essential, and required for "meaningful use" as mandated by the federal government. Providing personalized health information using tablets at the point of care could enhance the clinical experience and enable efficient collection of patient reported outcome measures to guide clinical decision making.

OBJECTIVE

The aim of this study is to explore patient and provider attitudes and interest in a proposed clinic-based tablet system for personal health information exchange. To provide a context to understand patients' use of tablets during their clinic visit, we also examine patients' current activities and time spent in the waiting room, and their use of health information resources.

METHODS

Surveys were administered to 84 patients in the waiting room of a community health center affiliated with Massachusetts General Hospital (MGH) in Boston, MA. This survey included a vignette and illustration describing a proposed tablet-based system in which the patient, upon sign in at the clinic, receives a tablet loaded with personalized information tailored to their specific medical conditions and preferences. Patients were queried about their interest in such a system in comparison to traditional forms of patient education as well as their current health information seeking behaviors and activities and time spent in the waiting room. Interviews with five MGH-affiliated health care providers were conducted to assess their opinions regarding the proposed tablet system.

RESULTS

The majority (>60%) of patients were "very" or "extremely" interested in the proposed tablet system and thought it would improve their knowledge about their medical condition (60%), assist them in making healthy choices (57%), and help them to feel more comfortable talking with their provider (55%). Patients thought the system would be more motivating, informative, and engaging than traditional printed health education materials. The tablet system was not considered more effective than face-to-face interaction with providers, though 44% thought it would improve their relationship with their physician. Overall, 91% of respondents were willing to learn how to use a tablet and 75% reported being "very" or "extremely" confident they could use one. Four of the five providers believed that the proposed tablet system would improve clinical workflow and patient education. Patients and providers were concerned about privacy and security of data collected using the tablets.

CONCLUSIONS

Both patients and providers were highly amenable to integrating tablets into the clinical experience, and tablets may be useful in improving patients' health knowledge, the collection of patient reported outcome measures, and improved patient-provider communication. Further research into operationalizing such systems and their validation is necessary before integration into standard clinical practice.

Development of a mobile application (app) for improving symptoms and adherence to oral chemotherapy in patients with cancer

189

Background: An advantage of oral chemotherapy is ease of administration, yet patients and family caregivers receive less support for adherence and monitoring of side effects. Effective interventions should support adherence to oral chemotherapy. We conducted qualitative interviews with patients, clinicians, and relevant stakeholders to inform the development of a proposed mobile app intervention to improve adherence and symptoms to oral chemotherapy. Methods: We conducted qualitative interviews and focus groups with multiple stakeholder groups (18 oncology physicians, 8 healthcare representatives, 8 cancer practice administrators, 18 patients and family members). Eligible patients had a diagnosis of cancer, a prescription for oral chemotherapy, cancer care at the MGH Cancer Center, and access to a smart phone. Stakeholders and clinicians evaluated the study approach, patient engagement, and intervention implementation. Patients reviewed wireframes of the mobile app to evaluate the components, usability, feasibility, and acceptability. Results: The app features include a treatment plan, medication reminders, symptom reporting, and cancer-specific resources. The themes from the qualitative interviews suggest that this mobile app would be helpful in improving adherence to oral chemotherapy and symptom management. Patients reported the wireframes to be acceptable and potentially user-friendly. Clinicians found that ongoing symptom reports would be helpful for proactively managing patients’ symptoms. Stakeholders recognized the importance of an app with functional tools to aid adherence and symptom management. All parties agreed that app features should remain simple, avoid undue burden, and thus enhance usability. Conclusions: Our findings suggest that a mobile app could be helpful for adherence and improving symptoms to oral chemotherapy in patients with cancer. Patients and clinicians rated the wireframe content favorably and commended the different features. We refined and finalized the mobile app based on feedback from the qualitative interviews. These findings support our plan to test the efficacy of this intervention in a randomized controlled trial. Clinical trial information: NCT02157519.

Heart failure remote monitoring: evidence from the retrospective evaluation of a real-world remote monitoring program

BACKGROUND

Given the magnitude of increasing heart failure mortality, multidisciplinary approaches, in the form of disease management programs and other integrative models of care, are recommended to optimize treatment outcomes. Remote monitoring, either as structured telephone support or telemonitoring or a combination of both, is fast becoming an integral part of many disease management programs. However, studies reporting on the evaluation of real-world heart failure remote monitoring programs are scarce.

OBJECTIVE

This study aims to evaluate the effect of a heart failure telemonitoring program, Connected Cardiac Care Program (CCCP), on hospitalization and mortality in a retrospective database review of medical records of patients with heart failure receiving care at the Massachusetts General Hospital.

METHODS

Patients enrolled in the CCCP heart failure monitoring program at the Massachusetts General Hospital were matched 1:1 with usual care patients. Control patients received care from similar clinical settings as CCCP patients and were identified from a large clinical data registry. The primary endpoint was all-cause mortality and hospitalizations assessed during the 4-month program duration. Secondary outcomes included hospitalization and mortality rates (obtained by following up on patients over an additional 8 months after program completion for a total duration of 1 year), risk for multiple hospitalizations and length of stay. The Cox proportional hazard model, stratified on the matched pairs, was used to assess primary outcomes.

RESULTS

A total of 348 patients were included in the time-to-event analyses. The baseline rates of hospitalizations prior to program enrollment did not differ significantly by group. Compared with controls, hospitalization rates decreased within the first 30 days of program enrollment: hazard ratio (HR)=0.52, 95% CI 0.31-0.86, P=.01). The differential effect on hospitalization rates remained consistent until the end of the 4-month program (HR=0.74, 95% CI 0.54-1.02, P=.06). The program was also associated with lower mortality rates at the end of the 4-month program: relative risk (RR)=0.33, 95% 0.11-0.97, P=.04). Additional 8-months follow-up following program completion did not show residual beneficial effects of the CCCP program on mortality (HR=0.64, 95% 0.34-1.21, P=.17) or hospitalizations (HR=1.12, 95% 0.90-1.41, P=.31).

CONCLUSIONS

CCCP was associated with significantly lower hospitalization rates up to 90 days and significantly lower mortality rates over 120 days of the program. However, these effects did not persist beyond the 120-day program duration.

Patient engagement with a mobile web-based telemonitoring system for heart failure self-management: a pilot study

Background

Intensive remote monitoring programs for congestive heart failure have been successful in reducing costly readmissions, but may not be appropriate for all patients. There is an opportunity to leverage the increasing accessibility of mobile technologies and consumer-facing digital devices to empower patients in monitoring their own health outside of the hospital setting. The iGetBetter system, a secure Web- and telephone-based heart failure remote monitoring program, which leverages mobile technology and portable digital devices, offers a creative solution at lower cost.

Objective

The objective of this pilot study was to evaluate the feasibility of using the iGetBetter system for disease self-management in patients with heart failure.

Methods

This was a single-arm prospective study in which 21 ambulatory, adult heart failure patients used the intervention for heart failure self-management over a 90-day study period. Patients were instructed to take their weight, blood pressure, and heart rate measurements each morning using a WS-30 bluetooth weight scale, a self-inflating blood pressure cuff (Withings LLC, Issy les Moulineaux, France), and an iPad Mini tablet computer (Apple Inc, Cupertino, CA, USA) equipped with cellular Internet connectivity to view their measurements on the Internet. Outcomes assessed included usability and satisfaction, engagement with the intervention, hospital resource utilization, and heart failure-related quality of life. Descriptive statistics were used to summarize data, and matched controls identified from the electronic medical record were used as comparison for evaluating hospitalizations.

Results

There were 20 participants (mean age 53 years) that completed the study. Almost all participants (19/20, 95%) reported feeling more connected to their health care team and more confident in performing care plan activities, and 18/20 (90%) felt better prepared to start discussions about their health with their doctor. Although heart failure-related quality of life improved from baseline, it was not statistically significant (P=.55). Over half of the participants had greater than 80% (72/90 days) weekly and overall engagement with the program, and 15% (3/20) used the interactive voice response telephone system exclusively for managing their care plan. Hospital utilization did not differ in the intervention group compared to the control group (planned hospitalizations P=.23, and unplanned hospitalizations P=.99). Intervention participants recorded shorter average length of hospital stay, but no significant differences were observed between intervention and control groups (P=.30).

Conclusions

This pilot study demonstrated the feasibility of a low-intensive remote monitoring program leveraging commonly used mobile and portable consumer devices in augmenting care for a fairly young population of ambulatory patients with heart failure. Further prospective studies with a larger sample size and within more diverse patient populations is necessary to determine the effect of mobile-based remote monitoring programs such as the iGetBetter system on clinical outcomes in heart failure.

The effect of technology-based interventions on pain, depression, and quality of life in patients with cancer: a systematic review of randomized controlled trials

Background

The burden of cancer is increasing; projections over the next 2 decades suggest that the annual cases of cancer will rise from 14 million in 2012 to 22 million. However, cancer patients in the 21st century are living longer due to the availability of novel therapeutic regimens, which has prompted a growing focus on maintaining patients’ health-related quality of life. Telehealth is increasingly being used to connect with patients outside of traditional clinical settings, and early work has shown its importance in improving quality of life and other clinical outcomes in cancer care.

Objective

The aim of this study was to systematically assess the literature for the effect of supportive telehealth interventions on pain, depression, and quality of life in cancer patients via a systematic review of clinical trials.

Methods

We searched PubMed, EMBASE, Google Scholar, CINAHL, and PsycINFO in July 2013 and updated the literature search again in January 2015 for prospective randomized trials evaluating the effect of telehealth interventions in cancer care with pain, depression, and quality of life as main outcomes. Two of the authors independently reviewed and extracted data from eligible randomized controlled trials, based on pre-determined selection criteria. Methodological quality of studies was assessed by the Cochrane Collaboration risk of bias tool.

Results

Of the 4929 articles retrieved from databases and relevant bibliographies, a total of 20 RCTs were included in the final review. The studies were largely heterogeneous in the type and duration of the intervention as well as in outcome assessments. A majority of the studies were telephone-based interventions that remotely connected patients with their health care provider or health coach. The intervention times ranged from 1 week to 12 months. In general, most of the studies had low risk of bias across the domains of the Cochrane Collaboration risk of bias tool, but most of the studies had insufficient information about the allocation concealment domain. Two of the three studies focused on pain control reported significant effects of the intervention; four of the nine studies focus on depression reported significant effects, while only the studies that were focused on quality of life reported significant effects.

Conclusions

This systematic review demonstrates the potential of telehealth interventions in improving outcomes in cancer care. However, more high-quality large-sized trials are needed to demonstrate cogent evidence of its effectiveness.

"Friending" teens: systematic review of social media in adolescent and young adult health care

Background

Social media has emerged as a potentially powerful medium for communication with adolescents and young adults around their health choices.

Objective

The goal of this systematic review is to identify research on the use of social media for interacting with adolescents and young adults in order to achieve positive health outcomes.

Methods

A MEDLINE/PubMed electronic database search was performed between January 1, 2002 and October 1, 2013, using terms to identify peer-reviewed research in which social media and other Web 2.0 technologies were an important feature. We used a systematic approach to retrieve papers and extract relevant data.

Results

We identified 288 studies involving social media, of which 87 met criteria for inclusion; 75 studies were purely observational and 12 were interventional. The ways in which social media was leveraged by these studies included (1) observing adolescent and young adult behavior (n=77), (2) providing health information (n=13), (3) engaging the adolescent and young adult community (n=17), and (4) recruiting research participants (n=23). Common health topics addressed included high-risk sexual behaviors (n=23), alcohol, tobacco, and other drug use (n=19), Internet safety (n=8), mental health issues (n=18), medical conditions (n=11), or other specified issues (n=12). Several studies used more than one social media platform and addressed more than one health-related topic.

Conclusions

Social media technologies offer an exciting new means for engaging and communicating with adolescents and young adults; it has been successfully used to engage this age group, identify behaviors, and provide appropriate intervention and education. Nevertheless, the majority of studies to date have been preliminary and limited in their methodologies, and mostly center around evaluating how adolescents and young adults use social media and the resulting implications on their health. Although these explorations are essential, further exploration and development of these strategies into building effective interventions is necessary.

Improving outcomes in cancer patients on oral anti-cancer medications using a novel mobile phone-based intervention: study design of a randomized controlled trial

Background

The widespread and increasing use of oral anti-cancer medications has been ushered in by a rapidly increasing understanding of cancer pathophysiology. Furthermore, their popular ease of administration and potential cost savings has highlighted their central position in the health care system as a whole. These facts have heightened appreciation of the unique challenges associated with the use of oral anti-cancer medications; especially in the long-term use of these medications and the associated side effects that may impede optimal adherence to their use. Therefore, we developed ChemOtheRapy Assistant, CORA, a personalized mobile phone–based self-management application to help cancer patients on oral anti-cancer medications.

Objective

Our objective is to evaluate the effect of CORA on adherence to oral anti-cancer medications and other clinically relevant outcomes in the management of patients with renal and prostate cancer.

Methods

The study will be implemented as a 2-parallel group randomized controlled trial in 104 patients with renal or prostate cancer on oral anti-cancer medications over a 3-month study period. The intervention group will use CORA in addition to usual care for self-management while the control group will continue care as usual. Medication adherence will be measured objectively by a Medication Event Monitoring System device and is defined as the percentage of prescribed doses taken. We will also assess the effect of the intervention on cancer-related symptoms measured by the MD Anderson Symptom Inventory and unplanned hospital utilizations. Other outcomes that will be measured at study start, midpoint, and endpoint are health-related quality of life, cancer-related fatigue, and anxiety. Group differences in medication adherence will be examined by t tests or by non-parametric Mann-Whitney tests if the data are not normally distributed. Logistic regression will be used to identify potential predictors of adherence.

Results

We expect to have results for this study before the end of 2016.

Conclusions

This novel mobile phone–enabled, multimodal self-management and educational intervention could lead to improvements in clinical outcomes and serve as a foundation for future mHealth research in improving outcomes for patients on oral anti-cancer medications.

"Real-world" practical evaluation strategies: a review of telehealth evaluation

Background

Currently, the increasing interest in telehealth and significant technological breakthroughs of the past decade create favorable conditions for the widespread adoption of telehealth services. Therefore, expectations are high that telehealth can help alleviate prevailing challenges in health care delivery. However, in order to translate current research to policy and facilitate adoption by patients and health care providers, there is need for compelling evidence of the effectiveness of telehealth interventions. Such evidence is gathered from rigorously designed research studies, which may not always be practical in many real-world settings.

Objective

Our aim was to summarize current telehealth evaluation strategies and challenges and to outline practical approaches to conduct evaluation in real-world settings using one of our previously reported telehealth initiatives, the Diabetes Connect program, as a case study.

Methods

We reviewed commonly used current evaluation frameworks and strategies, as well as best practices based on successful evaluative efforts to date to address commonly encountered challenges in telehealth evaluation. These challenges in telehealth evaluation and commonly used frameworks are described relevant to the evaluation of Diabetes Connect, a 12-month Web-based blood glucose monitoring program.

Results

Designers of telehealth evaluation frameworks must give careful consideration to the elements of planning, implementation, and impact assessment of interventions. Evaluating performance at each of these phases is critical to the overall success of an intervention. Although impact assessment occurs at the end of a program, our review shows that it should begin at the point of problem definition. Critical to the success of an evaluative strategy is early planning that involves all stakeholders to identify the overall goals of the program and key measures of success at each phase of the program life cycle. This strategy should enable selection of an appropriate evaluation strategy and measures to aid in the ongoing development and implementation of telehealth and provide better evidence of program impact.

Conclusions

We recommend a pragmatic, multi-method, multi-phase approach to telehealth evaluation that is flexible and can be adapted to the characteristics and challenges unique to each telehealth program.

Pain management in cancer patients using a mobile app: study design of a randomized controlled trial

BACKGROUND

Despite the availability of effective medications and clinical guidelines for pain management, pain control is suboptimal in a sizeable proportion of patients with cancer pain. The National Comprehensive Cancer Network guidelines recommend a comprehensive and multimodal approach for management of cancer pain. We developed a mobile phone application, ePAL, based on clinical guidelines to empower patients for cancer pain management by prompting regular pain assessments and coaching for self-management.

OBJECTIVE

The objective of this study is to evaluate the effect of a multidimensional mobile phone-based pain management application, ePAL, on controlling cancer pain and improving quality of life in patients with cancer pain being treated at an academic palliative care clinic.

METHODS

The study will be implemented as a 2-arm randomized controlled trial with 110 adult patients with CP who own a mobile phone over a follow-up period of two months. Participants will be randomized to either the intervention group receiving ePAL and usual care or to a control group receiving only usual care. The brief pain inventory will be used to assess our primary outcome which is pain intensity. We will also evaluate the effect of the intervention on secondary outcomes which include the effect of the intervention on hospital utilization for pain crisis, quality of life, adherence to analgesic medications, barriers to pain control, anxiety and patient engagement. Instruments that will be used in evaluating secondary outcomes include the Brief Pain Inventory, Morisky Medication Adherence Scale, Barriers Questionnaire-II, Functional Assessment of Cancer Therapy-General, Edmonton Symptom Assessment System, Generalized Anxiety Disorder 7-item scale, and the Functional Assessment of Chronic Illness Therapy-Fatigue. The intention-to-treat approach will be used to evaluate outcomes. Our primary outcome, pain intensity, measured longitudinally over eight weeks, will be assessed by mixed model repeated analysis. Effect sizes will be calculated as mean group differences with standard deviations.

RESULTS

The study is still in progress. We hope to have results by the end of 2015.

CONCLUSIONS

The multidimensional approach to pain management implemented on a mobile phone application could lead to significant improvements in patient outcomes.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02069743; https://clinicaltrials.gov/ct2/show/NCT02069743 (Archived by WebCite at http://www.webcitation.org/6Qb65XGGA).

Representation of health conditions on Facebook: content analysis and evaluation of user engagement

Background

A sizable majority of adult Internet users report looking for health information online. Social networking sites (SNS) like Facebook represent a common place to seek information, but very little is known about the representation and use of health content on SNS.

Objective

Our goal in this study was to understand the role of SNS in health information seeking. More specifically, we aimed to describe how health conditions are represented on Facebook Pages and how users interact with these different conditions.

Methods

We used Google Insights to identify the 20 most searched for health conditions on Google and then searched each of the resulting terms on Facebook. We compiled a list of the first 50 Facebook “Pages” results for each health condition. After filtering results to identify pages relevant to our research, we categorized pages into one of seven categories based on the page’s primary purpose. We then measured user engagement by evaluating the number of “Likes” for different conditions and types of pages.

Results

The search returned 50 pages for 18 of the health conditions, but only 48 pages were found for “anemia” and 5 pages were found for “flu symptoms”, yielding a total of 953 pages. A large number of pages (29.4%, 280/953) were irrelevant to the health condition searched. Of the 673 relevant pages, 151 were not in English or originated outside the United States, leaving 522 pages to be coded for content. The most common type of page was marketing/promotion (32.2%, 168/522) followed by information/awareness (20.7%, 108/522), Wikipedia-type pages (15.5%, 81/522), patient support (9.4%, 49/522), and general support (3.6%, 19/522). Health conditions varied greatly by the primary page type. All health conditions had some marketing/promotion pages and this made up 76% (29/38) of pages on acquired immunodeficiency syndrome (AIDS). The largest percentage of general support pages were cancer (19%, 6/32) and stomach (16%, 4/25). For patient support, stroke (67%, 4/6), lupus (33%, 10/30), breast cancer (19%, 6/31), arthritis (16%, 6/36), and diabetes (16%, 6/37) ranked the highest. Six health conditions were not represented by any type of support pages (ie, human papillomavirus, diarrhea, flu symptoms, pneumonia, spine, human immunodeficiency virus). Marketing/promotion pages accounted for 46.73% (10,371,169/22,191,633) of all Likes, followed by support pages (40.66%, 9,023,234/22,191,633). Cancer and breast cancer accounted for 86.90% (19,284,066/22,191,633) of all page Likes.

Conclusions

This research represents the first attempts to comprehensively describe publicly available health content and user engagement with health conditions on Facebook pages. Public health interventions using Facebook will need to be designed to ensure relevant information is easy to find and with an understanding that stigma associated with some health conditions may limit the users’ engagement with Facebook pages. This line of research merits further investigation as Facebook and other SNS continue to evolve over the coming years.

Academic Medical Centers as digital health catalysts

Emerging digital technologies offer enormous potential to improve quality, reduce cost, and increase patient-centeredness in healthcare. Academic Medical Centers (AMCs) play a key role in advancing medical care through cutting-edge medical research, yet traditional models for invention, validation and commercialization at AMCs have been designed around biomedical initiatives, and are less well suited for new digital health technologies. Recently, two large bi-coastal Academic Medical Centers, the University of California, San Francisco (UCSF) through the Center for Digital Health Innovation (CDHI) and Partners Healthcare through the Center for Connected Health (CCH) have launched centers focused on digital health innovation. These centers show great promise but are also subject to significant financial, organizational, and visionary challenges. We explore these AMC initiatives, which share the following characteristics: a focus on academic research methodology; integration of digital technology in educational programming; evolving models to support "clinician innovators"; strategic academic-industry collaboration and emergence of novel revenue models.

The future of Connected Health in preventive medicine

Over the last decade, Connected Health (CH) has shown great value in the management of chronic disease (CD), but has limited application in preventing these diseases that remain a huge burden to the society. Technological advances have made determination of genetic predisposition to disease possible and have gained wide use in oncology to develop more effective and individualized treatment strategies-Personalized Medicine. There is growing interest in the application of these genetic tests in predicting risk for complex genetic diseases; even, direct-to-consumer tests are increasingly becoming available and affordable. CH has shown great potential in collecting phenotypic data, which can be overlaid on genomic data to deliver a more precise and personalized preventive care that better engages patients. The goal of a CH program that uses genetic data would be to monitor individuals' risk factors and predict the onset of CD. This prediction would be coupled with coaching to delay or prevent the onset of disease. However, the challenge remains that many CDs are due to complex interaction between genes and modifiable environmental risk factors that are still under-studied.

The impact of using mobile-enabled devices on patient engagement in remote monitoring programs

BACKGROUND

Different types of data transmission technologies are used in remote monitoring (RM) programs. This study reports on a retrospective analysis of how participants engage, based on the type of data transfer technology used in a blood pressure (BP) RM program, and its potential impact on RM program design and outcomes.

METHODS

Thirty patients, aged 23-84 years (62 ± 14 years), who had completed at least 2 months in the program and were not participating in any other clinical trial were identified from the Remote Monitoring Data Repository. Half of these patients used wireless-based data transfer devices [wireless-based device (WBD)] while the other half used telephone modem-based data transfer devices [modem-based device (MBD)]. Participants were matched by practice and age. Engagement indices, which include frequency of BP measurements, frequency of data uploads, time to first BP measurement, and time to first data upload, were compared in both groups using the Wilcoxon-Mann-Whitney two-sample rank-sum test. Help desk call data were analyzed by Chi square test.

RESULTS

The frequency of BP measurements and data uploads was significantly higher in the WBD group versus the MBD group [median = 0.66 versus 0.2 measurements/day (p = .01) and 0.46 versus 0.01 uploads/day (p < .001), respectively]. Time to first upload was significantly lower in the WBD group (median = 4 versus 7 days; p = .02), but time to first BP measurement did not differ between the two groups (median = 2 versus 1 day; p = .98).

CONCLUSION

Wireless transmission ensures instantaneous transmission of readings, providing clinicians timely data to intervene on. Our findings suggest that mobile-enabled wireless technologies can positively impact patient engagement, outcomes, and operational workflow in RM programs.

Diabetes connect: an evaluation of patient adoption and engagement in a web-based remote glucose monitoring program

BACKGROUND

We determine whether Diabetes Connect (DC), a Web-based diabetes self-management program, can help patients effectively manage their diabetes and improve clinical outcomes.

METHODS

Diabetes Connect is a 12-month program that allows patients with type 2 diabetes mellitus to upload their blood glucose readings to a database, monitor trends, and share their data with their providers. To examine the impact of the program, we analyzed patient utilization and engagement data, clinical outcomes, as well as qualitative feedback from current and potential users through focus groups.

RESULTS

We analyzed 75 out of 166 patients. Mean age was 61 years (range 27-87). Patients engaged in DC had an average hemoglobin A1c (HbA1c) change of 1.5%, while nonengaged patients had a HbA1c change of 0.4% (p = .05). Patients with the best outcomes (HbAlc decline of at least 0.8%) typically took less than 10 days to upload, while patients with the worst outcomes (a rise in HbAlc) took an average of 65 days to upload. Patients with more engaged providers had a better HbA1c change (1.39% versus 0.87%) for practices with an average of 74 versus 30 logins/providers.

CONCLUSIONS

Patient engagement in the program has a positive impact on the outcomes of this collaborative Web-based diabetes self-management tool. Patients who engage early and remain active have better clinical outcomes than unengaged patients. Provider engagement, too, was found critical in engaging patients in DC.

Feasibility of a clearing house for improved cooperation between telemedicine networks delivering humanitarian services: acceptability to network coordinators

Background

Telemedicine networks, which deliver humanitarian services, sometimes need to share expertise to find particular experts in other networks. It has been suggested that a mechanism for sharing expertise between networks (a ‘clearing house’) might be useful.

Objective

To propose a mechanism for implementing the clearing house concept for sharing expertise, and to confirm its feasibility in terms of acceptability to the relevant networks.

Design

We conducted a needs analysis among eight telemedicine networks delivering humanitarian services. A small proportion of consultations (5–10%) suggested that networks may experience difficulties in finding the right specialists from within their own resources. With the assistance of key stakeholders, many of whom were network coordinators, various methods of implementing a clearing house were considered. One simple solution is to establish a central database holding information about consultants who have agreed to provide help to other networks; this database could be made available to network coordinators who need a specialist when none was available in their own network.

Results

The proposed solution was examined in a desktop simulation exercise, which confirmed its feasibility and probable value.

Conclusion

This analysis informs full-scale implementation of a clearing house, and an associated examination of its costs and benefits.

Evaluating a web-based self-management program for employees with hypertension and prehypertension: a randomized clinical trial

BACKGROUND

Web-based self-management programs offer a novel approach for self-insured employers seeking to improve and maintain employee health.

METHODS

We conducted a 6-month prospective, cluster-randomized controlled trial designed to evaluate whether worksite access to an automated, web-based, self-management program resulted in better blood pressure control. The trial was conducted at 6 EMC Corporation worksites in Massachusetts, each of which had at least 600 employees; 404 EMC employees with pre-hypertension or hypertension participated. Participants at 3 worksites received a home blood pressure cuff that uploaded readings to a Web site where they could view trends and read automated rules-based messages. Participants at 3 worksites received access to an onsite blood pressure cuff. Primary outcome measure was change in systolic blood pressure. Secondary outcome measures were change in diastolic blood pressure, proportion of participants achieving significant changes in systolic and diastolic blood pressure, and subject satisfaction.

RESULTS

Although the mean change in systolic blood pressure was not significantly different between intervention and control groups (-1.69 vs. -0.86 mm HG, respectively, P = .49) the change in diastolic blood pressure between groups was significant. (-1.08 vs. = 1.47 mm HG, respectively, P < .001). Significantly more intervention participants experienced a >10-mm Hg decrease in systolic blood pressure or >5-mm Hg decrease in diastolic blood pressure compared to controls (22% vs 17%, P = .02 and 29% vs 16%, P = .03, respectively). Intervention participants were twice as likely to report starting a new medication (P = .02) and more likely to report improved communication with their doctor (P = .02).

CONCLUSIONS

Participation in an automated online self-management program resulted in improved blood pressure among employees with prehypertension or hypertension.

Comparative performance of seven long-running telemedicine networks delivering humanitarian services

Seven long-running telemedicine networks were surveyed. The networks provided humanitarian services (clinical and educational) in developing countries, and had been in operation for periods of 5–15 years. The number of experts serving each network ranged from 15 to 513. The smallest network had a total of 10 requesters and the largest one had more than 500 requesters. The networks operated in nearly 60 countries. The seven networks managed a total of 1857 cases in 2011, i.e. an average of 265 cases per year per network. There was a significant growth in total activity, amounting to 100.3 cases per year during the 15 year study period. In 2011, network activity was 50–700 teleconsultations per network. There were clear differences in the patterns of activity, with some networks managing an increasing caseload, and others managing a slowly reducing caseload. The seven networks had published a total of 44 papers listed in Medline which summarized the evidence resulting from the delivery of services by telemedicine. There was a dearth of information about clinical and cost-effectiveness. Nevertheless, the services were widely appreciated by referring doctors, considered to be clinically useful, and there were indications that clinical outcomes for telemedicine patients were often improved. Despite a lack of formal evidence, the present study suggests that telemedicine can provide clinically useful services in developing countries.