Stressful life events in electronic health records: a scoping review

OBJECTIVES

Stressful life events, such as going through divorce, can have an important impact on human health. However, there are challenges in capturing these events in electronic health records (EHR). We conducted a scoping review aimed to answer 2 major questions: how stressful life events are documented in EHR and how they are utilized in research and clinical care.

MATERIALS AND METHODS

Three online databases (EBSCOhost platform, PubMed, and Scopus) were searched to identify papers that included information on stressful life events in EHR; paper titles and abstracts were reviewed for relevance by 2 independent reviewers.

RESULTS

Five hundred fifty-seven unique papers were retrieved, and of these 70 were eligible for data extraction. Most articles (n = 36, 51.4%) were focused on the statistical association between one or several stressful life events and health outcomes, followed by clinical utility (n = 15, 21.4%), extraction of events from free-text notes (n = 12, 17.1%), discussing privacy and other issues of storing life events (n = 5, 7.1%), and new EHR features related to life events (n = 4, 5.7%). The most frequently mentioned stressful life events in the publications were child abuse/neglect, arrest/legal issues, and divorce/relationship breakup. Almost half of the papers (n = 7, 46.7%) that analyzed clinical utility of stressful events were focused on decision support systems for child abuse, while others (n = 7, 46.7%) were discussing interventions related to social determinants of health in general.

DISCUSSION AND CONCLUSIONS

Few citations are available on the prevalence and use of stressful life events in EHR reflecting challenges in screening and storing of stressful life events.

Establishing an infrastructure to optimize the integration of genomics into research: Results from a precision health needs assessment

Researchers across the translational research continuum have emphasized the importance of integrating genomics into their research program. To date capacity and resources for genomics research have been limited; however, a recent population-wide genomic screening initiative launched at the Medical University of South Carolina in partnership with Helix has rapidly advanced the need to develop appropriate infrastructure for genomics research at our institution. We conducted a survey with researchers from across our institution (n = 36) to assess current knowledge about genomics health, barriers, and facilitators to uptake, and next steps to support translational research using genomics. We also completed 30-minute qualitative interviews with providers and researchers from diverse specialties (n = 8). Quantitative data were analyzed using descriptive analyses. A rapid assessment process was used to develop a preliminary understanding of each interviewee's perspective. These interviews were transcribed and coded to extract themes. The codes included types of research, alignment with precision health, opportunities to incorporate precision health, examples of researchers in the field, barriers, and facilitators to uptake, educational activity suggestions, questions to be answered, and other observations. Themes from the surveys and interviews inform implementation strategies that are applicable not only to our institution, but also to other organizations interested in making genomic data available to researchers to support genomics-informed translational research.

Using implementation science to evaluate a population-wide genomic screening program: Findings from the first 20,000 In Our DNA SC participants

We use the implementation science framework RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) to describe outcomes of In Our DNA SC, a population-wide genomic screening (PWGS) program. In Our DNA SC involves participation through clinical appointments, community events, or at home collection. Participants provide a saliva sample that is sequenced by Helix, and those with a pathogenic variant or likely pathogenic variant for CDC Tier 1 conditions are offered free genetic counseling. We assessed key outcomes among the first cohort of individuals recruited. Over 14 months, 20,478 participants enrolled, and 14,053 samples were collected. The majority selected at-home sample collection followed by clinical sample collection and collection at community events. Participants were predominately female, White (self-identified), non-Hispanic, and between the ages of 40-49. Participants enrolled through community events were the most racially diverse and the youngest. Half of those enrolled completed the program. We identified 137 individuals with pathogenic or likely pathogenic variants for CDC Tier 1 conditions. The majority (77.4%) agreed to genetic counseling, and of those that agreed, 80.2% completed counseling. Twelve clinics participated, and we conducted 108 collection events. Participants enrolled at home were most likely to return their sample for sequencing. Through this evaluation, we identified facilitators and barriers to implementation of our state-wide PWGS program. Standardized reporting using implementation science frameworks can help generalize strategies and improve the impact of PWGS.

Barriers and facilitators to the implementation of family cancer history collection tools in oncology clinical practices

INTRODUCTION

This study aimed to identify barriers and facilitators to the implementation of family cancer history (FCH) collection tools in clinical practices and community settings by assessing clinicians' perceptions of implementing a chatbot interface to collect FCH information and provide personalized results to patients and providers.

OBJECTIVES

By identifying design and implementation features that facilitate tool adoption and integration into clinical workflows, this study can inform future FCH tool development and adoption in healthcare settings.

MATERIALS AND METHODS

Quantitative data were collected using survey to evaluate the implementation outcomes of acceptability, adoption, appropriateness, feasibility, and sustainability of the chatbot tool for collecting FCH. Semistructured interviews were conducted to gather qualitative data on respondents' experiences using the tool and recommendations for enhancements.

RESULTS

We completed data collection with 19 providers (n = 9, 47%), clinical staff (n = 5, 26%), administrators (n = 4, 21%), and other staff (n = 1, 5%) affiliated with the NCI Community Oncology Research Program. FCH was systematically collected using a wide range of tools at sites, with information being inserted into the patient's medical record. Participants found the chatbot tool to be highly acceptable, with the tool aligning with existing workflows, and were open to adopting the tool into their practice.

DISCUSSION AND CONCLUSIONS

We further the evidence base about the appropriateness of scripted chatbots to support FCH collection. Although the tool had strong support, the varying clinical workflows across clinic sites necessitate that future FCH tool development accommodates customizable implementation strategies. Implementation support is necessary to overcome technical and logistical barriers to enhance the uptake of FCH tools in clinical practices and community settings.

Application of Spatial Analysis for Electronic Health Records: Characterizing Patient Phenotypes and Emerging Trends

Background

Electronic health records (EHR) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHR in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes.

Objective

This study reviews advanced spatial analyses that employed individual-level health data from EHR within the US to characterize patient phenotypes.

Methods

We systematically evaluated English-language peer-reviewed articles from PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on time, study design, or specific health domains.

Results

Only 49 articles met the eligibility criteria. These articles utilized diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were relatively underexplored. A noteworthy surge (n = 42, 85.7%) in publications was observed post-2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains, such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were rarely utilized.

Conclusions

This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. Additionally, this review proposes guidelines for harnessing the potential of spatial analysis to enhance the context of individual patients for future clinical decision support.

Association of prior treatment with nitrogen-containing bisphosphonates on outcomes of COVID-19 positive patients

COVID-19 infection has resulted in significant morbidity and mortality globally, especially among older adults. Repurposed drugs have demonstrated activity in respiratory illnesses, including nitrogen-containing bisphosphonates. In this retrospective longitudinal study at 4 academic medical centers, we show no benefit of nitrogen-containing bisphosphonates regarding ICU admission, ventilator use, and mortality among older adults with COVID-19 infection. We specifically evaluated the intravenous bisphosphonate zoledronic acid and found no difference compared to oral bisphosphonates.

BACKGROUND

Widely used in osteoporosis treatment, nitrogen-containing bisphosphonates (N-BP) have been associated with reduced mortality and morbidity among older adults. Based on prior studies, we hypothesized that prior treatment with N-BP might reduce intensive care unit (ICU) admission, ventilator use, and death among older adults diagnosed with COVID-19.

METHODS

This retrospective analysis of the PCORnet Common Data Model across 4 academic medical centers through 1 September 2021 identified individuals age >50 years with a diagnosis of COVID-19. The composite outcome included ICU admission, ventilator use, or death within 15, 30, and 180 days of COVID-19 diagnosis. Use of N-BP was defined as a prescription within 3 years prior. ICU admission and ventilator use were determined using administrative codes. Death included both in-hospital and out-of-hospital events. Patients treated with N-BP were matched 1:1 by propensity score to patients without prior N-BP use. Secondary analysis compared outcomes among those prescribed zoledronic acid (ZOL) to those prescribed oral N-BPs.

RESULTS

Of 76,223 COVID-19 patients identified, 1,853 were previously prescribed N-BP, among whom 559 were prescribed ZOL. After propensity score matching, there were no significant differences in the composite outcome at 15 days (HR 1.22, 95% CI: 0.89-1.67), 30 days (HR 1.24, 95% CI: 0.93-1.66), or 180 days (HR 1.17, 95% CI: 0.93-1.48), comparing those prescribed and not prescribed N-BP. Compared to those prescribed oral N-BP, there were no significant differences in outcomes among those prescribed ZOL.

CONCLUSION

Among older COVID-19 patients, prior exposure to N-BP including ZOL was not associated with a reduction in ICU admission, ventilator use, or death.

Stressful life events in electronic health records: a scoping review

Objective

Stressful life events, such as going through divorce, can have an important impact on human health. However, there are challenges in capturing these events in electronic health records (EHR). We conducted a scoping review aimed to answer two major questions: how stressful life events are documented in EHR and how they are utilized in research and clinical care.

Materials and Methods

Three online databases (EBSCOhost platform, PubMed, and Scopus) were searched to identify papers that included information on stressful life events in EHR; paper titles and abstracts were reviewed for relevance by two independent reviewers.

Results

557 unique papers were retrieved, and of these 70 were eligible for data extraction. Most articles (n=36, 51.4%) were focused on the statistical association between one or several stressful life events and health outcomes, followed by clinical utility (n=15, 21.4%), extraction of events from free-text notes (n=12, 17.1%), discussing privacy and other issues of storing life events (n=5, 7.1%), and new EHR features related to life events (n=4, 5.7%). The most frequently mentioned stressful life events in the publications were child abuse/neglect, arrest/legal issues, and divorce/relationship breakup. Almost half of the papers (n=7, 46.7%) that analyzed clinical utility of stressful events were focused on decision support systems for child abuse, while others (n=7, 46.7%) were discussing interventions related to social determinants of health in general.

Discussion and Conclusions

Few citations are available on the prevalence and use of stressful life events in EHR reflecting challenges in screening and storing of stressful life events.

Stressful life events in electronic health records: a scoping review

Objective

Stressful life events, such as going through divorce, can have an important impact on human health. However, there are challenges in capturing these events in electronic health records (EHR). We conducted a scoping review aimed to answer two major questions: how stressful life events are documented in EHR and how they are utilized in research and clinical care.

Materials and Methods

Three online databases (EBSCOhost platform, PubMed, and Scopus) were searched to identify papers that included information on stressful life events in EHR; paper titles and abstracts were reviewed for relevance by two independent reviewers.

Results

557 unique papers were retrieved, and of these 70 were eligible for data extraction. Most articles (n=36, 51.4%) were focused on the statistical association between one or several stressful life events and health outcomes, followed by clinical utility (n=15, 21.4%), extraction of events from free-text notes (n=12, 17.1%), discussing privacy and other issues of storing life events (n=5, 7.1%), and new EHR features related to life events (n=4, 5.7%). The most frequently mentioned stressful life events in the publications were child abuse/neglect, arrest/legal issues, and divorce/relationship breakup. Almost half of the papers (n=7, 46.7%) that analyzed clinical utility of stressful events were focused on decision support systems for child abuse, while others (n=7, 46.7%) were discussing interventions related to social determinants of health in general.

Discussion and Conclusions

Few citations are available on the prevalence and use of stressful life events in EHR reflecting challenges in screening and storing of stressful life events.

Initial development of tools to identify child abuse and neglect in pediatric primary care

BACKGROUND

Child abuse and neglect (CAN) is prevalent, associated with long-term adversities, and often undetected. Primary care settings offer a unique opportunity to identify CAN and facilitate referrals, when warranted. Electronic health records (EHR) contain extensive information to support healthcare decisions, yet time constraints preclude most providers from thorough EHR reviews that could indicate CAN. Strategies that summarize EHR data to identify CAN and convey this to providers has potential to mitigate CAN-related sequelae. This study used expert review/consensus and Natural Language Processing (NLP) to develop and test a lexicon to characterize children who have experienced or are at risk for CAN and compared machine learning methods to the lexicon + NLP approach to determine the algorithm's performance for identifying CAN.

METHODS

Study investigators identified 90 CAN terms and invited an interdisciplinary group of child abuse experts for review and validation. We then used NLP to develop pipelines to finalize the CAN lexicon. Data for pipeline development and refinement were drawn from a randomly selected sample of EHR from patients seen at pediatric primary care clinics within a U.S. academic health center. To explore a machine learning approach for CAN identification, we used Support Vector Machine algorithms.

RESULTS

The investigator-generated list of 90 CAN terms were reviewed and validated by 25 invited experts, resulting in a final pool of 133 terms. NLP utilized a randomly selected sample of 14,393 clinical notes from 153 patients to test the lexicon, and .03% of notes were identified as CAN positive. CAN identification varied by clinical note type, with few differences found by provider type (physicians versus nurses, social workers, etc.). An evaluation of the final NLP pipelines indicated 93.8% positive CAN rate for the training set and 71.4% for the test set, with decreased precision attributed primarily to false positives. For the machine learning approach, SVM pipeline performance was 92% for CAN + and 100% for non-CAN, indicating higher sensitivity than specificity.

CONCLUSIONS

The NLP algorithm's development and refinement suggest that innovative tools can identify youth at risk for CAN. The next key step is to refine the NLP algorithm to eventually funnel this information to care providers to guide clinical decision making.

Anticipating adaptation: tracking the impact of planned and unplanned adaptations during the implementation of a complex population-based genomic screening program

In 2021, the Medical University of South Carolina (MUSC) launched In Our DNA SC. This large-scale initiative will screen 100,000 individuals in South Carolina for three preventable hereditary conditions that impact approximately two million people in the USA but often go undetected. In anticipation of inevitable changes to the delivery of this complex initiative, we developed an approach to track and assess the impact of evaluate adaptations made during the pilot phase of program implementation. We used a modified version of the Framework for Reporting Adaptations and Modification-Enhanced (FRAME) and Adaptations to code adaptations made during the 3-month pilot phase of In Our DNA SC. Adaptations were documented in real-time using a REDCap database. We used segmented linear regression models to independently test three hypotheses about the impact of adaptations on program reach (rate of enrollment in the program, rate of messages viewed) and implementation (rate of samples collected) 7 days pre- and post-adaptation. Effectiveness was assessed using qualitative observations. Ten adaptations occurred during the pilot phase of program implementation. Most adaptations (60%) were designed to increase the number and type of patient contacted (reach). Adaptations were primarily made based on knowledge and experience (40%) or from quality improvement data (30%). Of the three adaptations designed to increase reach, shortening the recruitment message potential patients received significantly increased the average rate of invitations viewed by 7.3% (p = 0.0106). There was no effect of adaptations on implementation (number of DNA samples collected). Qualitative findings support improvement in effectiveness of the intervention after shortening the consent form and short-term positive impact on uptake of the intervention as measured by team member's participation. Our approach to tracking adaptations of In Our DNA SC allowed our team to quantify the utility of modifications, make decisions about pursuing the adaptation, and understand consequences of the change. Streamlining tools for tracking and responding to adaptations can help monitor the incremental impact of interventions to support continued learning and problem solving for complex interventions being delivered in health systems based on real-time data.

Strengths, weaknesses, opportunities, and threats for the nation's public health information systems infrastructure: synthesis of discussions from the 2022 ACMI Symposium

OBJECTIVE

The annual American College of Medical Informatics (ACMI) symposium focused discussion on the national public health information systems (PHIS) infrastructure to support public health goals. The objective of this article is to present the strengths, weaknesses, threats, and opportunities (SWOT) identified by public health and informatics leaders in attendance.

MATERIALS AND METHODS

The Symposium provided a venue for experts in biomedical informatics and public health to brainstorm, identify, and discuss top PHIS challenges. Two conceptual frameworks, SWOT and the Informatics Stack, guided discussion and were used to organize factors and themes identified through a qualitative approach.

RESULTS

A total of 57 unique factors related to the current PHIS were identified, including 9 strengths, 22 weaknesses, 14 opportunities, and 14 threats, which were consolidated into 22 themes according to the Stack. Most themes (68%) clustered at the top of the Stack. Three overarching opportunities were especially prominent: (1) addressing the needs for sustainable funding, (2) leveraging existing infrastructure and processes for information exchange and system development that meets public health goals, and (3) preparing the public health workforce to benefit from available resources.

DISCUSSION

The PHIS is unarguably overdue for a strategically designed, technology-enabled, information infrastructure for delivering day-to-day essential public health services and to respond effectively to public health emergencies.

CONCLUSION

Most of the themes identified concerned context, people, and processes rather than technical elements. We recommend that public health leadership consider the possible actions and leverage informatics expertise as we collectively prepare for the future.

Enhancing the nation's public health information infrastructure: a report from the ACMI symposium

The COVID-19 pandemic exposed multiple weaknesses in the nation's public health system. Therefore, the American College of Medical Informatics selected "Rebuilding the Nation's Public Health Informatics Infrastructure" as the theme for its annual symposium. Experts in biomedical informatics and public health discussed strategies to strengthen the US public health information infrastructure through policy, education, research, and development. This article summarizes policy recommendations for the biomedical informatics community postpandemic. First, the nation must perceive the health data infrastructure to be a matter of national security. The nation must further invest significantly more in its health data infrastructure. Investments should include the education and training of the public health workforce as informaticians in this domain are currently limited. Finally, investments should strengthen and expand health data utilities that increasingly play a critical role in exchanging information across public health and healthcare organizations.

VACtrac: enhancing access immunization registry data for population outreach using the Bulk Fast Healthcare Interoperable Resource (FHIR) protocol

COVID-19 vaccination uptake has been suboptimal, even in high-risk populations. New approaches are needed to bring vaccination data to the groups leading outreach efforts. This article describes work to make state-level vaccination data more accessible by extending the Bulk Fast Healthcare Interoperability Resource (FHIR) standard to better support the repeated retrieval of vaccination data for coordinated outreach efforts. We also describe a corresponding low-foot-print software for population outreach that automates repeated checks of state-level immunization data and prioritizes outreach by social determinants of health. Together this software offers an integrated approach to addressing vaccination gaps. Several extensions to the Bulk FHIR protocol were needed to support bulk query of immunization records. These are described in detail. The results of a pilot study, using the outreach tool to target a population of 1500 patients are also described. The results confirmed the limitations of current patient-by-patient approach for querying state immunizations systems for population data and the feasibility of a Bulk FHIR approach.

A pragmatic implementation research study for In Our DNA SC: a protocol to identify multi-level factors that support the implementation of a population-wide genomic screening initiative in diverse populations

BACKGROUND

In 2021, the Medical University of South Carolina (MUSC) partnered with Helix, a population genetic testing company, to offer population-wide genomic screening for Centers for Disease Control and Preventions' Tier 1 conditions of hereditary breast and ovarian cancer, Lynch syndrome, and familial hypercholesterolemia to 100,000 individuals in South Carolina. We developed an implementation science protocol to study the multi-level factors that influence the successful implementation of the In Our DNA SC initiative.

METHODS

We will use a convergent parallel mixed-methods study design to evaluate the implementation of planned strategies and associated outcomes for In Our DNA SC. Aims focus on monitoring participation to ensure engagement of diverse populations, assessing contextual factors that influence implementation in community and clinical settings, describing the implementation team's facilitators and barriers, and tracking program adaptations. We report details about each data collection tool and analyses planned, including surveys, interview guides, and tracking logs to capture and code work group meetings, adaptations, and technical assistance needs.

DISCUSSION

The goal of In Our DNA SC is to provide population-level screening for actionable genetic conditions and to foster ongoing translational research. The use of implementation science can help better understand how to support the success of In Our DNA SC, identify barriers and facilitators to program implementation, and can ensure the sustainability of population-level genetic testing. The model-based components of our implementation science protocol can support the identification of best practices to streamline the expansion of similar population genomics programs at other institutions.

Not Home Alone: Leveraging Telehealth and Informatics to Create a Lean Model for COVID-19 Patient Home Care

In response to the emerging COVID-19 public health emergency in March 2020, the Medical University of South Carolina rapidly implemented an analytics-enhanced remote patient monitoring (RPM) program with state-wide reach for SARS-CoV-2-positive patients. Patient-reported data and other analytics were used to prioritize the sickest patients for contact by RPM nurses, enabling a small cadre of RPM nurses, with the support of ambulatory providers and urgent care video visits, to oversee 1234 patients, many of whom were older, from underserved populations, or at high risk of serious complications. Care was escalated based on prespecified criteria to primary care provider or emergency department visit, with 89% of moderate- to high-risk patients treated solely at home. The RPM nurses facilitated the continuity of care during escalation or de-escalation of care, provided much-needed emotional support to patients quarantining at home and helped find medical homes for patients with tenuous ties to health care.

A Modified Public Health Automated Case Event Reporting Platform for Enhancing Electronic Laboratory Reports With Clinical Data: Design and Implementation Study

Background

Public health reporting is the cornerstone of public health practices that inform prevention and control strategies. There is a need to leverage advances made in the past to implement an architecture that facilitates the timely and complete public health reporting of relevant case-related information that has previously not easily been available to the public health community. Electronic laboratory reporting (ELR) is a reliable method for reporting cases to public health authorities but contains very limited data. In an earlier pilot study, we designed the Public Health Automated Case Event Reporting (PACER) platform, which leverages existing ELR infrastructure as the trigger for creating an electronic case report. PACER is a FHIR (Fast Health Interoperability Resources)-based system that queries the electronic health record from where the laboratory test was requested to extract expanded additional information about a case.

Objective

This study aims to analyze the pilot implementation of a modified PACER system for electronic case reporting and describe how this FHIR-based, open-source, and interoperable system allows health systems to conduct public health reporting while maintaining the appropriate governance of the clinical data.

Methods

ELR to a simulated public health department was used as the trigger for a FHIR-based query. Predetermined queries were translated into Clinical Quality Language logics. Within the PACER environment, these Clinical Quality Language logical statements were managed and evaluated against the providers’ FHIR servers. These predetermined logics were filtered, and only data relevant to that episode of the condition were extracted and sent to simulated public health agencies as an electronic case report. Design and testing were conducted at the Georgia Tech Research Institute, and the pilot was deployed at the Medical University of South Carolina. We evaluated this architecture by examining the completeness of additional information in the electronic case report, such as patient demographics, medications, symptoms, and diagnoses. This additional information is crucial for understanding disease epidemiology, but existing electronic case reporting and ELR architectures do not report them. Therefore, we used the completeness of these data fields as the metrics for enriching electronic case reports.

Results

During the 8-week study period, we identified 117 positive test results for chlamydia. PACER successfully created an electronic case report for all 117 patients. PACER extracted demographics, medications, symptoms, and diagnoses from 99.1% (116/117), 72.6% (85/117), 70.9% (83/117), and 65% (76/117) of the cases, respectively.

Conclusions

PACER deployed in conjunction with electronic laboratory reports can enhance public health case reporting with additional relevant data. The architecture is modular in design, thereby allowing it to be used for any reportable condition, including evolving outbreaks. PACER allows for the creation of an enhanced and more complete case report that contains relevant case information that helps us to better understand the epidemiology of a disease.

Public health reporting and outbreak response: synergies with evolving clinical standards for interoperability

Public health needs up-to-date information for surveillance and response. As healthcare application programming interfaces become widely available, a novel data gathering mechanism could provide public health with critical information in a timely fashion to respond to a fast-moving epidemic. In this article, we extrapolate from our experiences using a Fast Healthcare Interoperability Resource-based architecture for infectious disease surveillance for sexually transmitted diseases to its application to gather case information for an outbreak. One of the challenges with a fast-moving outbreak is to accurately assess its demand on healthcare resources, since information specific to comorbidities is often not available. These comorbidities are often associated with poor prognosis and higher resource utilization. If the comorbidity data and other clinical information were readily available to public health workers, they could better address community disruption and manage healthcare resources. The use of FHIR resources available through application programming and filtered through tools such as described herein will give public health the flexibility needed to investigate rapidly emerging disease while protecting patient privacy.

Balancing health privacy, health information exchange, and research in the context of the COVID-19 pandemic

The novel coronavirus disease 2019 infection poses serious challenges to the healthcare system that are being addressed through the creation of new unique and advanced systems of care with disjointed care processes (eg, telehealth screening, drive-through specimen collection, remote testing, telehealth management). However, our current regulations on the flows of information for clinical care and research are antiquated and often conflict at the state and federal levels. We discuss proposed changes to privacy regulations such as the Health Insurance Portability and Accountability Act designed to let health information seamlessly and frictionlessly flow among the health entities that need to collaborate on treatment of patients and, also, allow it to flow to researchers trying to understand how to limit its impacts.

Automatically Identifying Financial Stress Information from Clinical Notes for Patients with Prostate Cancer

Background

Financial stress, one of the social determinants, is common among cancer patients because of high out-ofpocket costs for treatment, as well as indirect costs. The National Academy of Medicine (NAM) has advised providers to recognize and discuss cost concerns with patients in order to enhance shared decision-making for treatment and exploration of financial assistant programs. However, financial stress is rarely assessed in clinical practice or research, thus, under-coded and under-documented in clinical practice. Natural language processing (NLP) offers great potential that can automatically extract and process data on financial stress from clinical free text existing in the patient electronic health record (EHR).

Methods

We developed and evaluated an NLP approach to identify financial stress from clinical narratives for patients with prostate cancer. Of 4,195 eligible prostate cancer patients, we randomly sampled 3,138 patients (75%) as a training dataset (150,990 documents) to develop a financial stress lexicon and NLP algorithms iteratively. The remaining 1,057 patients (25%) were used as a test dataset (55,516 documents) to evaluate the NLP algorithm performance. The common terms representing financial stress were "financial concerns," "unable to afford," "insurance issue," "unemployed," and "financial assistance." Negations were used to exclude false mentions of financial stress.

Results

Applying both pre- and post-negation, the NLP algorithm identified 209 patients (6.0%) from the training sample and 66 patients (6.2%) with 161 notes from the test sample as having documented financial stress. Two independent domain experts manually reviewed all 161 notes with NLP identified positives and randomly selected 161 notes with NLP-identified negatives, the NLP algorithm yielded 0.86 for precision, 1 for recall, and 0.9.2 for F-score.

Conclusions

Financial stress information is not commonly documented in the EHR, neither in structured format nor in clinical narratives. However, natural language processing can accurately extract financial stress information from clinical notes when such narrative information is available.

A Comprehensive View of Frequent Emergency Department Users Based on Data from a Regional HIE

OBJECTIVES

A small but significant number of patients make frequent emergency department (ED) visits to multiple EDs within a region. We have a unique health information exchange (HIE) that includes every ED encounter in all hospital systems in our region. Using our HIE we were able to characterize all frequent ED users in our region, regardless of hospital visited or payer class. The objective of our study was to use data from an HIE to characterize patients in a region who are frequent ED users (FEDUs).

METHODS

We constructed a database from a cohort of adult patients (18 years old or older) with information in a regional HIE for a 1-year period beginning in April 2012. Patients were defined as FEDUs (those who made four or more visits during the study period) and non-FEDUs (those who made fewer than four ED visits during the study period). Predictor variables included age, race, sex, payer class, county of residence, and International Classification of Diseases, Ninth Revision codes. Bivariate (χ(2)) and multivariate (logistic regression) analyses were performed to determine associations between predictor variables and the outcome of being a FEDU.

RESULTS

The database contained 127,672 patients, 12,293 (9.6%) of whom were FEDUs. Logistic regression showed the following patient characteristics to be significantly associated with the outcome of being a FEDU: age 35 to 44 years; African American race; Medicaid, Medicare, and dual-pay payer class; and International Classification of Diseases, Ninth Revision codes 630 to 679 (complications of pregnancy, childbirth, and puerperium), 780 to 799 (ill-defined conditions), 280 to 289 (diseases of the blood), 290-319 (mental disorders), 680 to 709 (diseases of the skin and subcutaneous tissue), 710 to 739 (musculoskeletal and connective tissue disease), 460 to 519 (respiratory disease), and 520 to 579 (digestive disease). No significant differences were noted between men and women.

CONCLUSIONS

Data from an HIE can be used to describe all of the patients within a region who are FEDUs, regardless of the hospital system they visited. This information can be used to focus care coordination efforts and link appropriate patients to a medical home. Future studies can be designed to learn the reasons why patients become FEDUs, and interventions can be developed to address deficiencies in health care that result in frequent ED visits.

Leveraging a Statewide Clinical Data Warehouse to Expand Boundaries of the Learning Health System

Learning Health Systems (LHS) require accessible, usable health data and a culture of collaboration—a challenge for any single system, let alone disparate organizations, with macro- and micro-systems. Recently, the National Science Foundation described this important setting as a cyber-social ecosystem. In 2004, in an effort to create a platform for transforming health in South Carolina, Health Sciences South Carolina (HSSC) was established as a research collaboration of the largest health systems, academic medical centers and research intensive universities in South Carolina. With work beginning in 2010, HSSC unveiled an integrated Clinical Data Warehouse (CDW) in 2013 as a crucial anchor to a statewide LHS. This CDW integrates data from independent health systems in near-real time, and harmonizes the data for aggregation and use in research. With records from over 2.7 million unique patients spanning 9 years, this multi-institutional statewide clinical research repository allows integrated individualized patient-level data to be used for multiple population health and biomedical research purposes. In the first 21 months of operation, more than 2,800 de-identified queries occurred through i2b2, with 116 users. HSSC has developed and implemented solutions to complex issues emphasizing anti-competitiveness and participatory governance, and serves as a recognized model to organizations working to improve healthcare quality by extending the traditional borders of learning health systems.

When Should ED Physicians Use an HIE? Predicting Presence of Patient Data in an HIE

OBJECTIVES

Health information exchanges (HIEs) make possible the construction of databases to characterize patients as multisystem users (MSUs), those visiting emergency departments (EDs) of more than one hospital system within a region during a 1-year period. HIE data can inform an algorithm highlighting patients for whom information is more likely to be present in the HIE, leading to a higher yield HIE experience for ED clinicians and incentivizing their adoption of HIE. Our objective was to describe patient characteristics that determine which ED patients are likely to be MSUs and therefore have information in an HIE, thereby improving the efficacy of HIE use and increasing ED clinician perception of HIE benefit.

METHODS

Data were extracted from a regional HIE involving four hospital systems (11 EDs) in the Charleston, South Carolina area. We used univariate and multivariable regression analyses to develop a predictive model for MSU status.

RESULTS

Factors associated with MSUs included younger age groups, dual-payer insurance status, living in counties that are more rural, and one of at least six specific diagnoses: mental disorders; symptoms, signs, and ill-defined conditions; complications of pregnancy, childbirth, and puerperium; diseases of the musculoskeletal system; injury and poisoning; and diseases of the blood and blood-forming organs. For patients with multiple ED visits during 1 year, 43.8% of MSUs had ≥4 visits, compared with 18.0% of non-MSUs (P < 0.0001).

CONCLUSIONS

This predictive model accurately identified patients cared for at multiple hospital systems and can be used to increase the likelihood that time spent logging on to the HIE will be a value-added effort for emergency physicians.

Pharmacist-provided diabetes management and education via a telemonitoring program

OBJECTIVE

To assess clinical outcomes (glycosylated hemoglobin [A1C], blood pressure, and lipids) and other measurements (disease state knowledge, adherence, and self-efficacy) associated with the use of approved telemonitoring devices to expand and improve chronic disease management of patients with diabetes, with or without hypertension.

SETTING

Four community health centers (CHCs) in Utah.

PRACTICE DESCRIPTION

Federally qualified safety net clinics that provide medical care to underserved patients.

PRACTICE INNOVATION

Pharmacist-led diabetes management using telemonitoring was compared with a group of patients receiving usual care (without telemonitoring).

INTERVENTIONS

Daily blood glucose (BG) and blood pressure (BP) values were reviewed and the pharmacist provided phone follow-up to assess and manage out-of-range BG and BP values.

EVALUATION

Changes in A1C, BP, and low-density lipoprotein (LDL) at approximately 6 months were compared between the telemonitoring group and the usual care group. Patient activation, diabetes/hypertension knowledge, and medication adherence were measured in the telemonitoring group.

RESULTS

Of 150 patients, 75 received pharmacist-provided diabetes management and education via telemonitoring, and 75 received usual medical care. Change in A1C was significantly greater in the telemonitoring group compared with the usual care group (2.07% decrease vs. 0.66% decrease; P <0.001). Although BP and LDL levels also declined, differences between the two groups were not statistically significant. Patient activation measure, diabetes/hypertension knowledge, and medication adherence with antihypertensives (but not diabetes medications) improved in the telemonitoring group.

CONCLUSION

Pharmacist-provided diabetes management via telemonitoring resulted in a significant improvement in A1C in federally qualified CHCs in Utah compared with usual medical care. Telemonitoring may be considered a model for providing clinical pharmacy services to patients with diabetes.

Teleconsent: A Novel Approach to Obtain Informed Consent for Research

Lack of recruitment of qualified research participants continues to be a significant bottleneck in clinical trials, often resulting in costly time extensions, underpowered results, and in some cases early termination. Some of the reasons for suboptimal recruitment include laborious consent processes and access to participants at remote locations. While new electronic consents technologies (eConsent) help overcome challenges related to readability and consent management, they do not adequately address challenges related to remote access. To address this, we have developed an innovative solution called “teleconsent”, which embeds the informed consent process into a telemedicine session. Teleconsent allows a researcher to remotely video conference with a prospective research participant, display and interactively guide participants in real-time through a consent form. When finished, the researcher and participant can electronically sign the consent form and print or download the signed document for archiving. This process can eliminate challenges related to travel and management of personnel at remote sites. Teleconsent has been successfully implemented in several clinical trials. Teleconsent can improve research recruitment by reducing the barriers related to informed consent, while preserving human interaction.

Validation of disease states in schizophrenia: comparison of cluster analysis between US and European populations

BACKGROUND

There is controversy as to whether use of statistical clustering methods to identify common disease patterns in schizophrenia identifies patterns generalizable across countries.

OBJECTIVE

The goal of this study was to compare disease states identified in a published study (Mohr/Lenert, 2004) considering US patients to disease states in a European cohort (EuroSC) considering English, French, and German patients.

METHODS

Using methods paralleling those in Mohr/Lenert, we conducted a principal component analysis (PCA) on Positive and Negative Syndrome Scale items in the EuroSC data set (n=1,208), followed by k-means cluster analyses and a search for an optimal k. The optimal model structure was compared to Mohr/Lenert by assigning discrete severity levels to each cluster in each factor based on the cluster center. A harmonized model was created and patients were assigned to health states using both approaches; agreement rates in state assignment were then calculated.

RESULTS

Five factors accounting for 56% of total variance were obtained from PCA. These factors corresponded to positive symptoms (Factor 1), negative symptoms (Factor 2), cognitive impairment (Factor 3), hostility/aggression (Factor 4), and mood disorder (Factor 5) (as in Mohr/Lenert). The optimal number of cluster states was six. The kappa statistic (95% confidence interval) for agreement in state assignment was 0.686 (0.670-0.703).

CONCLUSION

The patterns of schizophrenia effects identified using clustering in two different data sets were reasonably similar. Results suggest the Mohr/Lenert health state model is potentially generalizable to other populations.

Understanding Patients' Preferences for Referrals to Specialists for an Asymptomatic Condition

BACKGROUND

A specialty referral is a common but complex decision that often requires a primary care provider to balance his or her own interests with those of the patient.

OBJECTIVE

To examine the factors that influence a patient's choice of a specialist for consultation for an asymptomatic condition and better understand the tradeoffs that patients are and are not willing to make in this decision.

DESIGN

Stratified cross-sectional convenience sample of subjects selected to parallel US population demographics.

PARTICIPANTS

Members of an Internet survey panel who reported seeing a physician in the past year whose responses met objective quality metrics for attention.

MAIN MEASURES

Respondents completed an adaptive conjoint analysis survey comparing specialists regarding eight attributes. The reliability of assessments and the predictive validity of models were measured using holdout samples. The relative importance (RI) of different attributes was computed using paired t tests. The implications of utility values were studied using market simulation methods.

KEY RESULTS

Five hundred and thirty subjects completed the survey and had responses that met quality criteria. The reliability of responses was high (86% agreement), and models were predictive of patients' preferences (82.6% agreement with holdout choices). The most important attribute for patients was out-of-pocket cost (RI of 19.5%, P < 0.0001 v. other factors). Among the nonfinancial factors, "collaboration and communication" with the primary care provider was the most important attribute (RI of 13.1% P < 0.001). Third in importance was whether the specialist practiced shared decision making (RI of 12.2% P < 0.001 v. other factors except delay in consultation). Cost did not dominate decision making. In market simulations, patients frequently preferred more expensive providers. For example, most patients (76.3%) were willing to pay more ($80) to see a specialist who both collaborated well with their primary care provider and practiced shared decision making. Most patients prefer to wait for a doctor who practices shared decision making: Only one-third (32.3%) of patients preferred a paternalistic doctor who was available in 2 weeks over a doctor who practiced decision making but was available in 4 weeks.

CONCLUSIONS

In the setting of a referral for an asymptomatic but serious condition, out-of-pocket costs are important to patients; however, they also value specialists who collaborate and communicate well with their primary care providers and who practice shared decision making. Patients have wide variability in preferences for specialists, and referral decisions should be individualized.

Effects of electronic health record use on the exam room communication skills of resident physicians: a randomized within-subjects study

Background The effects of electronic health records (EHRs) on doctor–patient communication are unclear.

Objective To evaluate the effects of EHR use compared with paper chart use, on novice physicians’ communication skills.

Design Within-subjects randomized controlled trial using observed structured clinical examination methods to assess the impact of use of an EHR on communication.

Setting A large academic internal medicine training program.

Population First-year internal medicine residents.

Intervention Residents interviewed, diagnosed, and initiated treatment of simulated patients using a paper chart or an EHR on a laptop computer. Video recordings of interviews were rated by three trained observers using the Four Habits scale.

Results Thirty-two residents completed the study and had data available for review (61.5% of those enrolled in the residency program). In most skill areas in the Four Habits model, residents performed at least as well using the EHR and were statistically better in six of 23 skills areas (p<0.05). The overall average communication score was better when using an EHR: mean difference 0.254 (95% CI 0.05 to 0.45), p = 0.012, Cohen's d of 0.47 (a moderate effect). Residents scoring poorly (>3 average score) with paper methods (n = 8) had clinically important improvement when using the EHR.

Limitations This study was conducted in first-year residents in a training environment using simulated patients at a single institution.

Conclusions Use of an EHR on a laptop computer appears to improve the ability of first-year residents to communicate with patients relative to using a paper chart.

The Utah Remote Monitoring Project: improving health care one patient at a time

BACKGROUND

The expanding role of technology to augment diabetes care and management highlights the need for clinicians to learn about these new tools. As these tools continue to evolve and enhance improved outcomes, it is imperative that clinicians consider the role of telemonitoring, or remote monitoring, in patient care. This article describes a successful telemonitoring project in Utah.

SUBJECTS AND METHODS

This was a nonrandomized prospective observational preintervention-postintervention study, using a convenience sample. Patients with uncontrolled diabetes and/or hypertension from four rural and two urban primary care clinics and one urban stroke center participated in a telemonitoring program. The primary clinical outcome measures were changes in hemoglobin A1C (A1C) and blood pressure. Other outcomes included fasting lipids, weight, patient engagement, diabetes knowledge, hypertension knowledge, medication adherence, and patient perceptions of the usefulness of the telemonitoring program.

RESULTS

Mean A1C decreased from 9.73% at baseline to 7.81% at the end of the program (P<0.0001). Systolic blood pressure also declined significantly, from 130.7 mm Hg at baseline to 122.9 mm Hg at the end (P=0.0001). Low-density lipoprotein content decreased significantly, from 103.9 mg/dL at baseline to 93.7 mg/dL at the end (P=0.0263). Other clinical parameters improved nonsignificantly. Knowledge of diabetes and hypertension increased significantly (P<0.001 for both). Patient engagement and medication adherence also improved, but not significantly. Per questionnaires at study end, patients felt the telemonitoring program was useful.

CONCLUSIONS

Telemonitoring improved clinical outcomes and may be a useful tool to help enhance disease management and care of patients with diabetes and/or hypertension.

A model to support shared decision making in electronic health records systems

Shared decision making (SDM) is an approach to medical care based on collaboration between provider and patient, with both sharing in medical decisions. When patients' values and preferences are incorporated in decision making, care is more appropriate, ethically sound, and often lower in cost. However, SDM is difficult to implement in routine practice because of the time required for SDM methods, the lack of integration of SDM approaches into electronic health record (EHR) systems, and absence of explanatory mechanisms for providers on the results of patients' use of decision aids. This article discusses potential solutions, including the concept of a "personalize button" for EHRs. Leveraging a 4-phase clinical model for SDM, this article describes how computer decision support (CDS) technologies integrated into EHRs can help ensure that health care is delivered in a way that is respectful of those preferences. The architecture described herein, called CDS for SDM, is built on recognized standards that are currently integrated into certification requirements for EHRs as part of meaningful use regulations. While additional work is needed on modeling of preferences and on techniques for rapid communication models of preferences to clinicians, unless EHRs are redesigned to support SDM around and during clinical encounters, they are likely to continue to be an unintended barrier to SDM. With appropriate development, EHRs could be a powerful tool to promote SDM by reminding providers of situations for SDM and monitoring ongoing care to ensure treatments are consistent with patients' preferences.

EHR Big Data Deep Phenotyping. Contribution of the IMIA Genomic Medicine Working Group

OBJECTIVES

Given the quickening speed of discovery of variant disease drivers from combined patient genotype and phenotype data, the objective is to provide methodology using big data technology to support the definition of deep phenotypes in medical records.

METHODS

As the vast stores of genomic information increase with next generation sequencing, the importance of deep phenotyping increases. The growth of genomic data and adoption of Electronic Health Records (EHR) in medicine provides a unique opportunity to integrate phenotype and genotype data into medical records. The method by which collections of clinical findings and other health related data are leveraged to form meaningful phenotypes is an active area of research. Longitudinal data stored in EHRs provide a wealth of information that can be used to construct phenotypes of patients. We focus on a practical problem around data integration for deep phenotype identification within EHR data. The use of big data approaches are described that enable scalable markup of EHR events that can be used for semantic and temporal similarity analysis to support the identification of phenotype and genotype relationships.

CONCLUSIONS

Stead and colleagues' 2005 concept of using light standards to increase the productivity of software systems by riding on the wave of hardware/processing power is described as a harbinger for designing future healthcare systems. The big data solution, using flexible markup, provides a route to improved utilization of processing power for organizing patient records in genotype and phenotype research.

Why common carrier and network neutrality principles apply to the Nationwide Health Information Network (NWHIN)

The Office of the National Coordinator will be defining the architecture of the Nationwide Health Information Network (NWHIN) together with the proposed HealtheWay public/private partnership as a development and funding strategy. There are a number of open questions--for example, what is the best way to realize the benefits of health information exchange? How valuable are regional health information organizations in comparison with a more direct approach? What is the role of the carriers in delivering this service? The NWHIN is to exist for the public good, and thus shares many traits of the common law notion of 'common carriage' or 'public calling,' the modern term for which is network neutrality. Recent policy debates in Congress and resulting potential regulation have implications for key stakeholders within healthcare that use or provide services, and for those who exchange information. To date, there has been little policy debate or discussion about the implications of a neutral NWHIN. This paper frames the discussion for future policy debate in healthcare by providing a brief education and summary of the modern version of common carriage, of the key stakeholder positions in healthcare, and of the potential implications of the network neutrality debate within healthcare.

Shifts in the architecture of the Nationwide Health Information Network

In the midst of a US $30 billion USD investment in the Nationwide Health Information Network (NwHIN) and electronic health records systems, a significant change in the architecture of the NwHIN is taking place. Prior to 2010, the focus of information exchange in the NwHIN was the Regional Health Information Organization (RHIO). Since 2010, the Office of the National Coordinator (ONC) has been sponsoring policies that promote an internet-like architecture that encourages point to-point information exchange and private health information exchange networks. The net effect of these activities is to undercut the limited business model for RHIOs, decreasing the likelihood of their success, while making the NwHIN dependent on nascent technologies for community level functions such as record locator services. These changes may impact the health of patients and communities. Independent, scientifically focused debate is needed on the wisdom of ONC's proposed changes in its strategy for the NwHIN.

Public health surveillance and meaningful use regulations: a crisis of opportunity

The Health Information Technology for Economic and Clinical Health Act is intended to enhance reimbursement of health care providers for meaningful use of electronic health records systems. This presents both opportunities and challenges for public health departments. To earn incentive payments, clinical providers must exchange specified types of data with the public health system, such as immunization and syndromic surveillance data and notifiable disease reporting. However, a crisis looms because public health's information technology systems largely lack the capabilities to accept the types of data proposed for exchange. Cloud computing may be a solution for public health information systems. Through shared computing resources, public health departments could reap the benefits of electronic reporting within federal funding constraints.

A Public Health Grid (PHGrid): Architecture and value proposition for 21st century public health

PURPOSE

This manuscript describes the value of and proposal for a high-level architectural framework for a Public Health Grid (PHGrid), which the authors feel has the capability to afford the public health community a robust technology infrastructure for secure and timely data, information, and knowledge exchange, not only within the public health domain, but between public health and the overall health care system.

METHODS

The CDC facilitated multiple Proof-of-Concept (PoC) projects, leveraging an open-source-based software development methodology, to test four hypotheses with regard to this high-level framework. The outcomes of the four PoCs in combination with the use of the Federal Enterprise Architecture Framework (FEAF) and the newly emerging Federal Segment Architecture Methodology (FSAM) was used to develop and refine a high-level architectural framework for a Public Health Grid infrastructure.

RESULTS

The authors were successful in documenting a robust high-level architectural framework for a PHGrid. The documentation generated provided a level of granularity needed to validate the proposal, and included examples of both information standards and services to be implemented. Both the results of the PoCs as well as feedback from selected public health partners were used to develop the granular documentation.

CONCLUSIONS

A robust high-level cohesive architectural framework for a Public Health Grid (PHGrid) has been successfully articulated, with its feasibility demonstrated via multiple PoCs. In order to successfully implement this framework for a Public Health Grid, the authors recommend moving forward with a three-pronged approach focusing on interoperability and standards, streamlining the PHGrid infrastructure, and developing robust and high-impact public health services.

Transforming healthcare through patient empowerment

The United States faces tremendous challenges with its healthcare system. By any standard, it is expensive and performs poorly in most measures of health and thus, is in great need of reform. But how do we reform things without making the situation worse? Some of the more fundamental problems arise from the combination of a fee-for-service payment system for physicians with insurance-based financing care. This combination results in conflicts among the interests of patients, physicians and payers. This paper examines this issue from a decision analytic perspective, starting with a definition of the patient-centered view, and an assessment of the practicality of controlling costs by making healthcare more patient-centric. It then illustrates how fee-for-service models corrupt decision-making and other solutions designed to reign in the abuses of the fee-for-service model and also negatively impacts the quality of decision making for individual patients. Whatever the strategies for health reform, the degree of patient-centeredness of care is a benchmark that allows policy makers to understand how far they have had to deviate from optimal to achieve the desired ends of cost control.

Evaluation of an internet-based disease trajectory decision tool for prostate cancer screening

OBJECTIVE

To evaluate the application of a chronic disease model (CDM) for prostate cancer to visual analog scale (VAS) and time trade-off (TTO) decision tools.

METHODS

A total of 138 men (mean age 58 years) viewed a CDM module for prostate cancer with and without prostate specific antigen (PSA) screening. Participants rated their hypothetical quality of life with potential prostate cancer treatment complications using a CDM-based VAS decision tool. They were then asked to estimate how many years they would be willing to trade to be free of treatment complications using a CDM-based TTO decision tool. The consistency between VAS and TTO scores and the relationship between scores and preferences for PSA screening test and hypothetical treatment choice for prostate cancer were then evaluated.

RESULTS

There was a significant relationship between the VAS and TTO ratings (regression P < 0.001). The TTO tool was sensitive to age. Mean scores with standard deviations for those less than 58 years compared to those 58 years and more were 7.78 (1.75) and 8.41 (1.52), respectively (P = 0.04). Using the VAS tool, men who chose PSA screening had higher quality of life ratings compared to men who did not choose PSA screening: 7.73 (1.78) and 6.59 (2.39), respectively (P = 0.01). Similar results were found with the TTO decision tool: 8.33 (1.45) and 7.04 (2.00), respectively (P = 0.005). Men who would hypothetically prefer treatment for moderately differentiated prostate cancer also had higher TTO scores compared to men who preferred watchful waiting: 8.54 (1.39) and 7.85 (1.73), respectively (P = 0.04).

CONCLUSION

CDM-based for prostate cancer, VAS and TTO ratings were consistent and were concordant with patient preferences for screening; TTO ratings were also concordant with treatment choice. The use of the CDM-based TTO ratings to adjust for quality of life in decision analytic modeling needs to be explored.

Electronic Support for Public Health: validated case finding and reporting for notifiable diseases using electronic medical data

Health care providers are legally obliged to report cases of specified diseases to public health authorities, but existing manual, provider-initiated reporting systems generally result in incomplete, error-prone, and tardy information flow. Automated laboratory-based reports are more likely accurate and timely, but lack clinical information and treatment details. Here, we describe the Electronic Support for Public Health (ESP) application, a robust, automated, secure, portable public health detection and messaging system for cases of notifiable diseases. The ESP application applies disease specific logic to any complete source of electronic medical data in a fully automated process, and supports an optional case management workflow system for case notification control. All relevant clinical, laboratory and demographic details are securely transferred to the local health authority as an HL7 message. The ESP application has operated continuously in production mode since January 2007, applying rigorously validated case identification logic to ambulatory EMR data from more than 600,000 patients. Source code for this highly interoperable application is freely available under an approved open-source license at http://esphealth.org.

Epilepsy surgery for pharmacoresistant temporal lobe epilepsy: a decision analysis

CONTEXT

Patients with pharmacoresistant epilepsy have increased mortality compared with the general population, but patients with pharmacoresistant temporal lobe epilepsy who meet criteria for surgery and who become seizure-free after anterior temporal lobe resection have reduced excess mortality vs those with persistent seizures.

OBJECTIVE

To quantify the potential survival benefit of anterior temporal lobe resection for patients with pharmacoresistant temporal lobe epilepsy vs continued medical management.

DESIGN

Monte Carlo simulation model that incorporates possible surgical complications and seizure status, with 10,000 runs. The model was populated with health-related quality-of-life data obtained directly from patients and data from the medical literature. Insufficient data were available to assess gamma-knife radiosurgery or vagal nerve stimulation.

MAIN OUTCOME MEASURES

Life expectancy and quality-adjusted life expectancy.

RESULTS

Compared with medical management, anterior temporal lobe resection for a 35-year-old patient with an epileptogenic zone identified in the anterior temporal lobe would increase survival by 5.0 years (95% CI, 2.1-9.2) with surgery preferred in 100% of the simulations. Anterior temporal lobe resection would increase quality-adjusted life expectancy by 7.5 quality-adjusted life-years (95%, CI, -0.8 to 17.4) with surgery preferred in 96.5% of the simulations, primarily due to increased years spent without disabling seizures, thereby reducing seizure-related excess mortality and improving quality of life. The results were robust to sensitivity analyses.

CONCLUSION

The decision analysis model suggests that on average anterior temporal lobe resection should provide substantial gains in life expectancy and quality-adjusted life expectancy for surgically eligible patients with pharmacoresistant temporal lobe epilepsy compared with medical management.

Health state valuation in mild to moderate cognitive impairment: feasibility of computer-based, direct patient utility assessment

BACKGROUND

Most patients with dementia will, at some point, need a proxy health care decision maker. It is unknown whether persons with various degrees of cognitive impairment can reliably report their health-related preferences.

METHODS

The authors performed health state valuations (HSVs) of current and hypothetical future health states on 47 pairs of patients with mild to moderate cognitive impairment and their caregivers using computer-based standard gamble, time tradeoff, and rating scale techniques.

RESULTS

Patients' mean (SD) age was 74.6 (9.3) years. About half of the patients were women (48%), as were most caregivers (73%), who were on average younger (mean age= 66.2 years, SD= 12.2). Most participants were white (83%); 17% were African American. The mean (SD) Mini-Mental State Examination (MMSE) score of patients was 24.2 (4.6) of 30. All caregivers and 77% of patients (36/47) completed all 18 components of the HSV exercise. Patients who completed the HSV exercise were slightly younger (mean age [SD]= 74.1 [8.5] v. 75.9 [11.8]; P = 0.569) and had significantly higher MMSE scores (mean score [SD] = 25.0 [4.3] v. 21.4 [4.4]; P = 0.018). Although MMSE scores below 20 did not preclude the completion of all 18 HSV ratings, being classified as having moderate cognitive impairment was associated with a lower likelihood of completing all scenario ratings (44% v. 82%). Patient and caregiver responses showed good consistency across time and across techniques and were logically consistent.

CONCLUSION

Obtaining HSVs for current and hypothetical health states was feasible for most patients with mild cognitive impairment and many with moderate cognitive impairment. HSV assessments were consistent and reasonable.

Comparison of the effectiveness of wireless electronic tracking devices versus traditional paper systems to track victims in a large scale disaster

We conducted an unblinded experimental comparative trial during a disaster drill involving DMATs using the WIISARD system and traditional paper tracking of casualties. We shadowed the paper work flow to collect data on 40 victims tracked by both systems. WIISARD captured patients as well as the paper system. However, WIISARDwas superior at tracking patient destinations and transporting units. WIISARD proved to be an effective victim tracking system.

Field provider position tracking at mass gathering events

WIISARD (Wireless Internet Information System for Medical Response to Disasters) utilizes wireless technology to improve medical care at mass casualty disasters. An important component of WIISARD is geolocation tracking of field personnel at the disaster site. Accurate, real-time information on personnel has the potential to improve resource utilization at the disaster site, as well as increase the safety of first responders caring for victims at a hazardous scene.

Electronic and computer-generated patient questionnaires in standard care

Patient-derived measures of disease activity have been proven to be reliable, valid, sensitive to change, and less susceptible to placebo effects in the assessment of many rheumatic diseases. Traditionally, paper forms have been used to capture this information but with advances in technology and a growing number of computer users, computerized versions have been developed. The computerized patient-derived questionnaires have been shown to be valid and reliable in many studies. Despite a concern for the usability and acceptability among inexperienced computer users and certain subgroups, such as older persons, a majority of patients queried preferred the electronic versions and found them easy to use. In addition, these computerized versions offer several advantages over the paper format, including improved data capture with less ambiguity, less long-term cost, immediate scoring and availability of the results, and--most importantly--the ability for more frequent disease activity, efficacy, and safety assessments.