Impact of the HITECH financial incentives on EHR adoption in small, physician-owned practices

Outcomes of Vital Sign Monitoring of an Acute Surgical Cohort With Wearable Sensors and Digital Alerting Systems: A Pragmatically Designed Cohort Study and Propensity-Matched Analysis

Background: The implementation and efficacy of wearable sensors and alerting systems in acute secondary care have been poorly described.

Objectives: to pragmatically test one such system and its influence on clinical outcomes in an acute surgical cohort.

Methods: In this pragmatically designed, pre-post implementation trial, participants admitted to the acute surgical unit at our institution were recruited. In the pre-implementation phase (September 2017 to May 2019), the SensiumVitals™ monitoring system, which continuously measures temperature, heart, and respiratory rates, was used for monitoring alongside usual care (intermittent monitoring in accordance with the National Early Warning Score 2 [NEWS 2] protocol) without alerts being generated. In the post-implementation phase (May 2019 to March 2020), alerts were generated when pre-established thresholds for vital parameters were breached, requiring acknowledgement from healthcare staff on provided mobile devices. Hospital length of stay, intensive care use, and 28-days mortality were measured. Balanced cohorts were created with 1:1 ‘optimal’ propensity score logistic regression models.

Results: The 1:1 matching method matched the post-implementation group (n = 141) with the same number of subjects from the pre-implementation group (n = 141). The median age of the entire cohort was 52 (range: 18–95) years and the median duration of wearing the sensor was 1.3 (interquartile range: 0.7–2.0) days. The median alert acknowledgement time was 111 (range: 1–2,146) minutes. There were no significant differences in critical care admission (planned or unplanned), hospital length of stay, or mortality.

Conclusion: This study offered insight into the implementation of digital health technologies within our institution. Further work is required for optimisation of digital workflows, particularly given their more favourable acceptability in the post pandemic era. Clinical trials registration information: ClinicalTrials.gov Identifier: NCT04638738.

OUP accepted manuscript

OBJECTIVES

To assess whether previously observed differences in interoperable exchange by physician practice size persisted in 2019 and identify the role of 3 factors shaping interoperable exchange among physicians in practices of varying sizes: Federal incentive programs designed to encourage health IT use, value-based care, and selection of electronic health record (EHR) developer.

MATERIALS

Cross-sectional analysis of a 2019 survey of physicians. We used multivariable Poisson models to estimate the relative risk of interoperable exchange based on the size of the practice accounting for other characteristics and the mediating role of 3 factors.

RESULTS

Seventeen percent of solo practice physicians integrated outside data relative to 51% of large practice physicians. This difference remained substantial in initial multivariable models including physician characteristics. When included in models, Federal incentive programs partially mediated the relationship between practice size and interoperable exchange status. In final models including EHR developer, developer was strongly associated with both exchange and integration while practice size was no longer an independent predictor. These trends persisted when comparing practices with 4 or fewer physicians to those with 5 or more.

DISCUSSION

Public and private initiatives that increase the benefits of interoperable exchange may encourage small practices to pursue it. Technical and policy changes that reduce the costs and complexity of supporting exchange could make it easier for small developers to advance their capabilities to support small practices.

CONCLUSION

Addressing the gap between small and large practices will take a 2-pronged approach that targets both small EHR developers and small practices.

Barriers and best practices related to documentation of electronic care plans: A survey of community-based pharmacies in 4 states

BACKGROUND

The provision of enhanced services within community-based pharmacy is increasing. However, an opportunity remains to improve efficient documentation of services, and barriers to implementation exist. Electronic care (eCare) planning is the act of using health information technology to submit a pharmacist eCare plan for a patient encounter, similar to a Subjective, Objective, Assessment, Plan note.

OBJECTIVE

The primary objective was to identify barriers and best practices related to documentation of eCare plans within community-based pharmacies participating in 4 Community Pharmacy Enhanced Services Networks (CPESN).

METHODS

One of two 24-question electronic surveys was distributed to pharmacies in CPESN Florida, Georgia, Mississippi, and Ohio. Pharmacies submitting fewer than 10 eCare plans in the previous quarter received a survey to assess barriers to implementation; pharmacies submitting 10 or more eCare plans received a survey to assess best practices for implementation. Surveys remained open for 14 days, with a reminder sent on days 7 and 12. Data were analyzed using descriptive statistics. An independent-samples t test assessed for between-group differences in the overall knowledge.

RESULTS

A total of 63 responses were received (Barriers = 19; Best Practices = 44). Best Practices pharmacies earned a higher overall knowledge score than Barriers pharmacies (9.26 vs. 7.26 out of 13 points, P = 0.001). Frequently reported barriers were staffing resources (n = 11, 57.9%), perceived time commitment (n = 8, 42.1%), and lack of payment (n = 8, 42.1%). Most Best Practices pharmacies agreed or strongly agreed that they involve pharmacists (n = 36, 81.8%) and student pharmacists (n = 33, 75.5%) in eCare planning processes. Common foci of eCare plans by Best Practice pharmacies were medication synchronization (n = 35, 79.5%), drug therapy problems (n = 29, 65.9%), adherence assessment (n = 28, 63.6%).

CONCLUSIONS

A difference in knowledge and perceptions exists between pharmacies who regularly eCare plan and those who do not. Observed trends in knowledge, perceptions, barriers, and best practices should be used to create a training to increase eCare planning quality and consistency.

Impact of Electronic Health Records Interoperability on Telehealth Service Outcomes

This paper aims to develop a telehealth success model and discusses three critical components: (1) health information quality, (2) electronic health record system quality, and (3) telehealth service quality to ensure effective telehealth service delivery, reduce professional burnout, and enhance access to care. The paper applied a policy analysis method and discussed telehealth applications in rural health, mental health, and veterans health services. The results pointed out the fact that, although telehealth paired with semantic/organizational interoperability facilitates value-based and team-based care, challenges remain to enhance user (both patients and clinicians) experience and satisfaction. The conclusion indicates that approaches at systemic and physician levels are needed to reduce disparities in health technology adoption and improve access to telehealth care.

Gaps in standards for integrating artificial intelligence technologies into ophthalmic practice

PURPOSE OF REVIEW

The purpose of this review is to provide an overview of healthcare standards and their relevance to multiple ophthalmic workflows, with a specific emphasis on describing gaps in standards development needed for improved integration of artificial intelligence technologies into ophthalmic practice.

RECENT FINDINGS

Healthcare standards are an essential component of data exchange and critical for clinical practice, research, and public health surveillance activities. Standards enable interoperability between clinical information systems, healthcare information exchange between institutions, and clinical decision support in a complex health information technology ecosystem. There are several gaps in standards in ophthalmology, including relatively low adoption of imaging standards, lack of use cases for integrating apps providing artificial intelligence -based decision support, lack of common data models to harmonize big data repositories, and no standards regarding interfaces and algorithmic outputs.

SUMMARY

These gaps in standards represent opportunities for future work to develop improved data flow between various elements of the digital health ecosystem. This will enable more widespread adoption and integration of artificial intelligence-based tools into clinical practice. Engagement and support from the ophthalmology community for standards development will be important for advancing this work.

Automated NLP Extraction of Clinical Rationale for Treatment Discontinuation in Breast Cancer

PURPOSE

Key oncology end points are not routinely encoded into electronic medical records (EMRs). We assessed whether natural language processing (NLP) can abstract treatment discontinuation rationale from unstructured EMR notes to estimate toxicity incidence and progression-free survival (PFS).

METHODS

We constructed a retrospective cohort of 6,115 patients with early-stage and 701 patients with metastatic breast cancer initiating care at Memorial Sloan Kettering Cancer Center from 2008 to 2019. Each cohort was divided into training (70%), validation (15%), and test (15%) subsets. Human abstractors identified the clinical rationale associated with treatment discontinuation events. Concatenated EMR notes were used to train high-dimensional logistic regression and convolutional neural network models. Kaplan-Meier analyses were used to compare toxicity incidence and PFS estimated by our NLP models to estimates generated by manual labeling and time-to-treatment discontinuation (TTD).

RESULTS

Our best high-dimensional logistic regression models identified toxicity events in early-stage patients with an area under the curve of the receiver-operator characteristic of 0.857 ± 0.014 (standard deviation) and progression events in metastatic patients with an area under the curve of 0.752 ± 0.027 (standard deviation). NLP-extracted toxicity incidence and PFS curves were not significantly different from manually extracted curves (P = .95 and P = .67, respectively). By contrast, TTD overestimated toxicity in early-stage patients (P < .001) and underestimated PFS in metastatic patients (P < .001). Additionally, we tested an extrapolation approach in which 20% of the metastatic cohort were labeled manually, and NLP algorithms were used to abstract the remaining 80%. This extrapolated outcomes approach resolved PFS differences between receptor subtypes (P < .001 for hormone receptor+/human epidermal growth factor receptor 2- v human epidermal growth factor receptor 2+ v triple-negative) that could not be resolved with TTD.

CONCLUSION

NLP models are capable of abstracting treatment discontinuation rationale with minimal manual labeling.

Investigation of patients' access to EHR data via smart apps in Chinese Hospitals

Background

Given that China has encouraged EHR usage in hospitals for more than a decade, patients’ access to their own EHR data is still not as widely utilized as expected.

Methods

We cultivated a survey with four categories and field interviews of measures to identify whether hospitals have already released EHR data to patients, inpatients or outpatients, the top EHR release contents and the most popular release software.

Results

Of the 1344 responding hospitals from 30 provinces nationwide, 41.37% of hospitals have already released their EHR data to patients, of which 97.12% are through smart apps. More than 91% of hospitals use WeChat, and 32.37% of hospitals developed their own standalone apps or use vendors’ apps. A total of 54.63% were released to both outpatients and inpatients, and the top release contents were all objective. A rough estimation is made that releasing EHR data to patients via smart apps may save the hospital 15.9 million RMB per year and patients 9.4 million RMB altogether.

Conclusions

EHR data release is believed to bring both patient and hospital cost savings and efficiency gains but is still considered spontaneous and requires legal support and government regulation.

Clinical decision support systems in orthodontics: A narrative review of data science approaches

Advancements in technology and data collection generated immense amounts of information from various sources such as health records, clinical examination, imaging, medical devices, as well as experimental and biological data. Proper management and analysis of these data via high-end computing solutions, artificial intelligence and machine learning approaches can assist in extracting meaningful information that enhances population health and well-being. Furthermore, the extracted knowledge can provide new avenues for modern healthcare delivery via clinical decision support systems. This manuscript presents a narrative review of data science approaches for clinical decision support systems in orthodontics. We describe the fundamental components of data science approaches including (a) Data collection, storage and management; (b) Data processing; (c) In-depth data analysis; and (d) Data communication. Then, we introduce a web-based data management platform, the Data Storage for Computation and Integration, for temporomandibular joint and dental clinical decision support systems.

Does coaching matter? Examining the impact of specific practice facilitation strategies on implementation of quality improvement interventions in the Healthy Hearts in the Heartland study

BACKGROUND

Practice facilitation is a multicomponent implementation strategy used to improve the capacity for practices to address care quality and implementation gaps. We sought to assess whether practice facilitators use of coaching strategies aimed at improving self-sufficiency were associated with improved implementation of quality improvement (QI) interventions in the Healthy Hearts in the Heartland Study.

METHODS

We mapped 27 practice facilitation activities to a framework that classifies practice facilitation strategies by the degree to which the practice develops its own process expertise (Doing Tasks, Project Management, Consulting, Teaching, and Coaching) and then used regression tree analysis to group practices by facilitation strategies experienced. Kruskal-Wallis tests were used to assess whether practice groups identified by regression tree analysis were associated with successful implementation of QI interventions and practice and study context variables.

RESULTS

There was no association between number of strategies performed by practice facilitators and number of QI interventions implemented. Regression tree analysis identified 4 distinct practice groups based on the number of Project Management and Coaching strategies performed. The median number of interventions increased across the groups. Practices receiving > 4 project management and > 6 coaching activities implemented a median of 17 of 35 interventions. Groups did not differ significantly by practice size, association with a healthcare network, or practice type. Statistically significant differences in practice location, number and duration of facilitator visits, and early study termination emerged among the groups, compared to the overall practice population.

CONCLUSIONS

Practices that engage in more coaching-based strategies with practice facilitators are more likely to implement more QI interventions, and practice receptivity to these strategies was not dependent on basic practice demographics.

Adoption of Electronic Health Records (EHRs) in China During the Past 10 Years: Consecutive Survey Data Analysis and Comparison of Sino-American Challenges and Experiences

Background

The adoption rate of electronic health records (EHRs) in hospitals has become a main index to measure digitalization in medicine in each country.

Objective

This study summarizes and shares the experiences with EHR adoption in China and in the United States.

Methods

Using the 2007-2018 annual hospital survey data from the Chinese Health Information Management Association (CHIMA) and the 2008-2017 United States American Hospital Association Information Technology Supplement survey data, we compared the trends in EHR adoption rates in China and the United States. We then used the Bass model to fit these data and to analyze the modes of diffusion of EHRs in these 2 countries. Finally, using the 2007, 2010, and 2014 CHIMA and Healthcare Information and Management Systems Services survey data, we analyzed the major challenges faced by hospitals in China and the United States in developing health information technology.

Results

From 2007 to 2018, the average adoption rates of the sampled hospitals in China increased from 18.6% to 85.3%, compared to the increase from 9.4% to 96% in US hospitals from 2008 to 2017. The annual average adoption rates in Chinese and US hospitals were 6.1% and 9.6%, respectively. However, the annual average number of hospitals adopting EHRs was 1500 in China and 534 in the US, indicating that the former might require more effort. Both countries faced similar major challenges for hospital digitalization.

Conclusions

The adoption rates of hospital EHRs in China and the United States have both increased significantly in the past 10 years. The number of hospitals that adopted EHRs in China exceeded 16,000, which was 3.3 times that of the 4814 nonfederal US hospitals. This faster adoption outcome may have been a benefit of top-level design and government-led policies, particularly the inclusion of EHR adoption as an important indicator for performance evaluation and the appointment of public hospitals.

Clinical Implementation of Predictive Models Embedded within Electronic Health Record Systems: A Systematic Review

Predictive analytics using electronic health record (EHR) data have rapidly advanced over the last decade. While model performance metrics have improved considerably, best practices for implementing predictive models into clinical settings for point-of-care risk stratification are still evolving. Here, we conducted a systematic review of articles describing predictive models integrated into EHR systems and implemented in clinical practice. We conducted an exhaustive database search and extracted data encompassing multiple facets of implementation. We assessed study quality and level of evidence. We obtained an initial 3393 articles for screening, from which a final set of 44 articles was included for data extraction and analysis. The most common clinical domains of implemented predictive models were related to thrombotic disorders/anticoagulation (25%) and sepsis (16%). The majority of studies were conducted in inpatient academic settings. Implementation challenges included alert fatigue, lack of training, and increased work burden on the care team. Of 32 studies that reported effects on clinical outcomes, 22 (69%) demonstrated improvement after model implementation. Overall, EHR-based predictive models offer promising results for improving clinical outcomes, although several gaps in the literature remain, and most study designs were observational. Future studies using randomized controlled trials may help improve the generalizability of findings.

Quantifying the competitiveness of the electronic health record market and its implications for interoperability

OBJECTIVE

The objective of this study was to quantify both the competitiveness of the EHR vendor market in the United States of America (US) and the degree of fragmentation of individual Medicare beneficiaries' medical records across the differing EHR vendors found in the US healthcare system.

METHODS AND MATERIALS

We determined the Part A and Part B Medicare-expenditure weighted market shares of EHR vendors and estimated the rate of attestation of meaningful use (MU) for EHRs among Medicare Part A & B providers from 2011 to 2016. Based on these data we calculated the annual Herfindahl-Hirschman Index to quantify the competitiveness of the EHR market as well as the number of vendors individual Medicare beneficiaries' medical records were stored in for the period 2014-2016.

RESULTS

We find that as of 2016 the EHR vendor environment was competitive but trending towards becoming highly concentrated soon. We also found that patient medical records were highly fragmented as only 4.5 % of expenditure-weighted individual Medicare beneficiaries had their MU medical records associated with a single vendor, while 19.8 % of expenditure-weighted beneficiaries had their MU medical records stored in 8 or more vendors.

DISCUSSION

These results indicate that there are tradeoffs between EHR market competition, and the challenges associated with achieving interoperability across numerous competing vendors.

CONCLUSION

Uncertainty of interoperability among different EHR vendors may make transmission of medical records among different providers challenging, mitigating the benefit of vendor competition. This highlights the critical importance of current interoperability efforts moving forward.

Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records

Current models for correlating electronic medical records with -omics data largely ignore clinical text, which is an important source of phenotype information for patients with cancer. This data convergence has the potential to reveal new insights about cancer initiation, progression, metastasis, and response to treatment. Insights from this real-world data will catalyze clinical care, research, and regulatory activities. Natural language processing (NLP) methods are needed to extract these rich cancer phenotypes from clinical text. Here, we review the advances of NLP and information extraction methods relevant to oncology based on publications from PubMed as well as NLP and machine learning conference proceedings in the last 3 years. Given the interdisciplinary nature of the fields of oncology and information extraction, this analysis serves as a critical trail marker on the path to higher fidelity oncology phenotypes from real-world data.

Wearable Health Technology and Electronic Health Record Integration: Scoping Review and Future Directions

BACKGROUND

Due to the adoption of electronic health records (EHRs) and legislation on meaningful use in recent decades, health systems are increasingly interdependent on EHR capabilities, offerings, and innovations to better capture patient data. A novel capability offered by health systems encompasses the integration between EHRs and wearable health technology. Although wearables have the potential to transform patient care, issues such as concerns with patient privacy, system interoperability, and patient data overload pose a challenge to the adoption of wearables by providers.

OBJECTIVE

This study aimed to review the landscape of wearable health technology and data integration to provider EHRs, specifically Epic, because of its prevalence among health systems. The objectives of the study were to (1) identify the current innovations and new directions in the field across start-ups, health systems, and insurance companies and (2) understand the associated challenges to inform future wearable health technology projects at other health organizations.

METHODS

We used a scoping process to survey existing efforts through Epic's Web-based hub and discussion forum, UserWeb, and on the general Web, PubMed, and Google Scholar. We contacted Epic, because of their position as the largest commercial EHR system, for information on published client work in the integration of patient-collected data. Results from our searches had to meet criteria such as publication date and matching relevant search terms.

RESULTS

Numerous health institutions have started to integrate device data into patient portals. We identified the following 10 start-up organizations that have developed, or are in the process of developing, technology to enhance wearable health technology and enable EHR integration for health systems: Overlap, Royal Philips, Vivify Health, Validic, Doximity Dialer, Xealth, Redox, Conversa, Human API, and Glooko. We reported sample start-up partnerships with a total of 16 health systems in addressing challenges of the meaningful use of device data and streamlining provider workflows. We also found 4 insurance companies that encourage the growth and uptake of wearables through health tracking and incentive programs: Oscar Health, United Healthcare, Humana, and John Hancock.

CONCLUSIONS

The future design and development of digital technology in this space will rely on continued analysis of best practices, pain points, and potential solutions to mitigate existing challenges. Although this study does not provide a full comprehensive catalog of all wearable health technology initiatives, it is representative of trends and implications for the integration of patient data into the EHR. Our work serves as an initial foundation to provide resources on implementation and workflows around wearable health technology for organizations across the health care industry.

Research-grade data in the real world: challenges and opportunities in data quality from a pragmatic trial in community-based practices

Randomized controlled trials face cost, logistic, and generalizability limitations, including difficulty engaging racial/ethnic minorities. Real-world data (RWD) from pragmatic trials, including electronic health record (EHR) data, may produce intervention evaluation findings generalizable to diverse populations. This case study of Project IMPACT describes unique barriers and facilitators of optimizing RWD to improve health outcomes and advance health equity in small immigrant-serving community-based practices. Project IMPACT tested the effect of an EHR-based health information technology intervention on hypertension control among small urban practices serving South Asian patients. Challenges in acquiring accurate RWD included EHR field availability and registry capabilities, cross-sector communication, and financial, personnel, and space resources. Although using RWD from community-based practices can inform health equity initiatives, it requires multidisciplinary collaborations, clinic support, procedures for data input (including social determinants), and standardized field logic/rules across EHR platforms.

Data-Driven Diffusion Of Innovations: Successes And Challenges In 3 Large-Scale Innovative Delivery Models

Failed diffusion of innovations may be linked to an inability to use and apply data, information, and knowledge to change perceptions of current practice and motivate change. Using qualitative and quantitative data from three large-scale health care delivery innovations-accountable care organizations, advanced primary care practice, and EvidenceNOW-we assessed where data-driven innovation is occurring and where challenges lie. We found that implementation of some technological components of innovation (for example, electronic health records) has occurred among health care organizations, but core functions needed to use data to drive innovation are lacking. Deficits include the inability to extract and aggregate data from the records; gaps in sharing data; and challenges in adopting advanced data functions, particularly those related to timely reporting of performance data. The unexpectedly high costs and burden incurred during implementation of the innovations have limited organizations' ability to address these and other deficits. Solutions that could help speed progress in data-driven innovation include facilitating peer-to-peer technical assistance, providing tailored feedback reports to providers from data aggregators, and using practice facilitators skilled in using data technology for quality improvement to help practices transform. Policy efforts that promote these solutions may enable more rapid uptake of and successful participation in innovative delivery system reforms.

Design of healthy hearts in the heartland (H3): A practice-randomized, comparative effectiveness study

BACKGROUND

The Healthy Hearts in the Heartland (H3) study is part of a nationwide effort, EvidenceNOW, seeking to better understand the ability of small primary care practices to improve "ABCS" clinical quality measures: appropriate Aspirin therapy, Blood pressure control, Cholesterol management, and Smoking cessation. H3 aimed to assess feasibility of implementing Point-of-Care (POC) or POC plus Population Management (POC + PM) quality improvement (QI) strategies to improve ABCS at practices in Illinois, Indiana, and Wisconsin. We describe the design and randomization of the H3 study.

METHODS

We conducted a two-arm (1:1, POC:POC + PM), practice-randomized, comparative effectiveness study in 226 primary care practices across four "waves" of randomization with a 12-month intervention period, followed by a six-month sustainability period. Randomization controlled imbalance in nine baseline variables through a modified constrained algorithm. Among others, we used initial, unverified estimates of baseline ABCS values.

RESULTS

We randomized 112 and 114 practices to POC and POC + PM arms, respectively. Randomization ensured baseline comparability for all nine key variables, including the ABCS measures indicating proportion of patients at the practice level meeting each quality measure. Median(Inner Quartile Range) values were A: 0.78(0.66-0.86) in POC arm vs. 0.77(0.63-0.86) in POC + PM arm, B: 0.64(0.53-0.73) vs. 0.64(0.53-0.75), C: 0.78(0.63-0.86) vs. 0.75(0.64-0.81), S: 0.80(0.65-0.81) vs. 0.79(0.61-0.91).

DISCUSSION

Surrogate estimates for the true ABCS at baseline coupled with the unique randomization logic achieved adequate baseline balance on these outcomes. Similar practice- or cluster-randomized trials may consider adaptations of this design. Final analyses on 12- and 18-month ABCS outcomes for the H3 study are forthcoming.

TRIAL REGISTRATION

This trial is registered on ClinicalTrials.gov (Initial post: 11/05/2015; identifier: NCT02598284; https://clinicaltrials.gov/ct2/show/NCT02598284?term=NCT02598284&rank=1).

2016 Year-in-Review of Clinical and Consumer Informatics: Analysis and Visualization of Keywords and Topics

Objectives

The objective of this study was to review and visualize the medical informatics field over the previous 12 months according to the frequencies of keywords and topics in papers published in the top four journals in the field and in Healthcare Informatics Research (HIR), an official journal of the Korean Society of Medical Informatics.

Methods

A six-person team conducted an extensive review of the literature on clinical and consumer informatics. The literature was searched using keywords employed in the American Medical Informatics Association year-in-review process and organized into 14 topics used in that process. Data were analyzed using word clouds, social network analysis, and association rules.

Results

The literature search yielded 370 references and 1,123 unique keywords. ‘Electronic Health Record’ (EHR) (78.6%) was the most frequently appearing keyword in the articles published in the five studied journals, followed by ‘telemedicine’ (2.1%). EHR (37.6%) was also the most frequently studied topic area, followed by clinical informatics (12.0%). However, ‘telemedicine’ (17.0%) was the most frequently appearing keyword in articles published in HIR, followed by ‘telecommunications’ (4.5%). Telemedicine (47.1%) was the most frequently studied topic area, followed by EHR (14.7%).

Conclusions

The study findings reflect the Korean government's efforts to introduce telemedicine into the Korean healthcare system and reactions to this from the stakeholders associated with telemedicine.