Impact of the HITECH Act on physicians' adoption of electronic health records

Researching big IT in the UK National Health Service: A systematic review of theory-based studies

OBJECTIVE

To identify and discuss theory-based studies of large-scale health information technology programs in the UK National Health Service.

MATERIALS AND METHODS

Using the PRISMA systematic review framework, we searched Scopus, PubMed and CINAHL databases from inception to March 2022 for theory-based studies of large-scale health IT implementations. We undertook detailed full-text analyses of papers meeting our inclusion criteria.

RESULTS

Forty-six studies were included after assessment for eligibility, of which twenty-five applied theories from the information systems arena (socio-technical approaches, normalization process theory, user acceptance theories, diffusion of innovation), twelve from sociology (structuration theory, actor-network theory, institutional theory), while nine adopted other theories. Most investigated England's National Program for IT (2002-2011), exploring various technologies among which electronic records predominated. Research themes were categorized into user factors, program factors, process outcomes, clinical impact, technology, and organizational factors. Most research was qualitative, often using a case study strategy with a longitudinal or cross-sectional approach. Data were typically collected through interviews, observation, and document analysis; sampling was generally purposive; and most studies used thematic or related analyses. Theories were generally applied in a superficial or fragmentary manner; and articles frequently lacked detail on how theoretical constructs and relationships aided organization, analysis, and interpretation of data.

CONCLUSION

Theory-based studies of large NHS IT programs are relatively uncommon. As large healthcare programs evolve over a long timeframe in complex and dynamic environments, wider adoption of theory-based methods could strengthen the explanatory and predictive utility of research findings across multiple evaluation studies. Our review has confirmed earlier suggestions for theory selection, and we suggest there is scope for more explicit use of such theoretical constructs to strengthen the conceptual foundations of health informatics research. Additionally, the challenges of large national health informatics programs afford wide-ranging opportunities to test, refine, and adapt sociological and information systems theories.

Comparing "Meaningful Use" of Health Information Technology in Pennsylvania: Electronic Prescription Rates of Metropolitan and Rural Counties

The Health Information Technology for Economic and Clinical Health Act incentivized the adoption of electronic health records (EHRs). Health systems looked to leverage technology to assist in serving populations in health professional shortage areas. Qualitative research points to EHR usability as a source of health inequities in rural settings, making the challenges of EHR usage a subject of interest. Pennsylvania offers a model for investigating rural health infrastructure with it having the third largest rural population in the United States. This study analyzed the adoption of Electronic Prescribing in the 67 Pennsylvania (PA) counties. Physician adoption and usage data for PA and the United States were compared using a t-test to establish a basis for comparison. PA counties were categorized using the United States Department of Agriculture (USDA)'s Rural-Urban Commuting Areas (RUCAs) system. Surescript use percentages were plotted against the RUCA scores of each PA county to create a polynomial regression model. PA office-based physicians, on average, utilize e-prescription tools at the same rate as the national average with 59% of practices utilizing Surescripts as of 2013. There was no significant correlation between Surescript usage and the rural/urban classification of counties in Pennsylvania (R-squared value of 0.06). Pennsylvania was able to adopt health information technology (HIT) infrastructure at the same rate as the national average. Rural and metropolitan definitions do not correlate to meaningful use of HIT, thus usability of HIT cannot be tied to health outcomes. Future studies looking at specific forms of HIT and their ability to decrease the burden of administrative work for clinicians.

Public Health Informatics and the Perioperative Physician: Looking to the Future

The role of informatics in public health has increased over the past few decades, and the coronavirus disease 2019 (COVID-19) pandemic has underscored the critical importance of aggregated, multicenter, high-quality, near-real-time data to inform decision-making by physicians, hospital systems, and governments. Given the impact of the pandemic on perioperative and critical care services (eg, elective procedure delays; information sharing related to interventions in critically ill patients; regional bed-management under crisis conditions), anesthesiologists must recognize and advocate for improved informatic frameworks in their local environments. Most anesthesiologists receive little formal training in public health informatics (PHI) during clinical residency or through continuing medical education. The COVID-19 pandemic demonstrated that this knowledge gap represents a missed opportunity for our specialty to participate in informatics-related, public health-oriented clinical care and policy decision-making. This article briefly outlines the background of PHI, its relevance to perioperative care, and conceives intersections with PHI that could evolve over the next quarter century.

Contextual Variation of Clinical Notes induced by EHR Migration

The structure and semantics of clinical notes vary considerably across different Electronic Health Record (EHR) systems, sites, and institutions. Such heterogeneity hampers the portability of natural language processing (NLP) models in extracting information from the text for clinical research or practice. In this study, we evaluate the contextual variation of clinical notes by measuring the semantic and syntactic similarity of the notes of two sets of physicians comprising four medical specialties across EHR migrations at two Mayo Clinic sites. We find significant semantic and syntactic variation imposed by the context of the EHR system and between medical specialties whereas only minor variation is caused by variation of spatial context across sites. Our findings suggest that clinical language models need to account for process differences at the specialty sublanguage level to be generalizable.

A Conference (Missingness in Action) to Address Missingness in Data and AI in Health Care: Qualitative Thematic Analysis

BACKGROUND

Missingness in health care data poses significant challenges in the development and implementation of artificial intelligence (AI) and machine learning solutions. Identifying and addressing these challenges is critical to ensuring the continued growth and accuracy of these models as well as their equitable and effective use in health care settings.

OBJECTIVE

This study aims to explore the challenges, opportunities, and potential solutions related to missingness in health care data for AI applications through the conduct of a digital conference and thematic analysis of conference proceedings.

METHODS

A digital conference was held in September 2022, attracting 861 registered participants, with 164 (19%) attending the live event. The conference featured presentations and panel discussions by experts in AI, machine learning, and health care. Transcripts of the event were analyzed using the stepwise framework of Braun and Clark to identify key themes related to missingness in health care data.

RESULTS

Three principal themes-data quality and bias, human input in model development, and trust and privacy-emerged from the analysis. Topics included the accuracy of predictive models, lack of inclusion of underrepresented communities, partnership with physicians and other populations, challenges with sensitive health care data, and fostering trust with patients and the health care community.

CONCLUSIONS

Addressing the challenges of data quality, human input, and trust is vital when devising and using machine learning algorithms in health care. Recommendations include expanding data collection efforts to reduce gaps and biases, involving medical professionals in the development and implementation of AI models, and developing clear ethical guidelines to safeguard patient privacy. Further research and ongoing discussions are needed to ensure these conclusions remain relevant as health care and AI continue to evolve.

Experimental evidence for structured information-sharing networks reducing medical errors

Errors in clinical decision-making are disturbingly common. Recent studies have found that 10 to 15% of all clinical decisions regarding diagnoses and treatment are inaccurate. Here, we experimentally study the ability of structured information-sharing networks among clinicians to improve clinicians' diagnostic accuracy and treatment decisions. We use a pool of 2,941 practicing clinicians recruited from around the United States to conduct 84 independent group-level trials, ranging across seven different clinical vignettes for topics known to exhibit high rates of diagnostic or treatment error (e.g., acute cardiac events, geriatric care, low back pain, and diabetes-related cardiovascular illness prevention). We compare collective performance in structured information-sharing networks to collective performance in independent control groups, and find that networks significantly reduce clinical errors, and improve treatment recommendations, as compared to control groups of independent clinicians engaged in isolated reflection. Our results show that these improvements are not a result of simple regression to the group mean. Instead, we find that within structured information-sharing networks, the worst clinicians improved significantly while the best clinicians did not decrease in quality. These findings offer implications for the use of social network technologies to reduce errors among clinicians.

Examining the adoption and implementation of behavioral electronic health records by healthcare professionals based on the clinical adoption framework

Background

While various quantitative studies based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and Technology Acceptance Models (TAM) exist in the general medical sectors, just a few have been conducted in the behavioral sector; they have all been qualitative interview-based studies.

Objective

The purpose of this study is to assess the adoption dimensions of a behavioral electronic health record (EHR) system for behavioral clinical professionals using a modified clinical adoption (CA) research model that incorporates a variety of micro, meso, and macro level factors.

Methods

A questionnaire survey with quantitative analysis approach was used via purposive sampling method. We modified the existing CA framework to be suitable for evaluating the adoption of an EHR system by behavioral clinical professionals. We designed and verified questionnaires that fit into the dimensions of the CA framework. The survey was performed in five US behavioral hospitals, and the adoption factors were analyzed using a structural equation analysis.

Results

We derived a total of seven dimensions, omitting those determined to be unsuitable for behavioral clinical specialists to respond to. We polled 409 behavioral clinical experts from five hospitals. As a result, the ease of use and organizational support had a substantial impact on the use of the behavioral EHR system. Although the findings were not statistically significant, information and service quality did appear to have an effect on the system's ease of use. The primary reported benefit of behavioral EHR system adoption was the capacity to swiftly locate information, work efficiently, and access patient information via a mobile app, which resulted in more time for better care. The primary downside, on the other hand, was an unhealthy reliance on the EHR system.

Conclusions

We demonstrated in this study that the CA framework can be a useful tool for evaluating organizational and social elements in addition to the EHR system's system features. Not only the EHR system's simplicity of use, but also organizational support, should be considered for the effective implementation of the behavioral EHR system.

Trial Registration: The study was approved by the Institutional Review Board of Seoul National University Bundang Hospital (IRB No.: B-1904-534-301).

Medical scribes improve documentation consistency and efficiency in an otolaryngology clinic

OBJECTIVE

Scribes in medical practice enable more efficient documentation requirements but insufficient analyses have occurred to fully evaluate their efficacy in otolaryngology. We analyzed pre/post metrics of scribe implementation that may aid practitioners in determining feasibility for use in their practices.

METHODS

1808 patient charts were analyzed in The Epic Electronic Medical Record system (EMR) (903 pre and 905 post scribe implementation). We measured: clinic volumes, time saved in documentation, chart billing level, and lag days of chart closure.

RESULTS

Patient volumes increased by 3.02% with an 11-17% decrease in time spent in clinic/day and lag days for billing. The distribution of visits for new patients was 17.75% level 2, 51.45% level 3, 29.71% level 4 before the scribe and was 6.83% level 2, 89.21% level 3, 3.96% level 4 after the scribe. For established patients it was 3.97% level 2, 84.92% level 3, 8.93% level 4 before and 0.34% level 2, 91.76% level 3, 7.73% level 4 after. The change in level of documentation for established and new patients pre and post scribe implementation was not statistically significant (p = 0.821, 0.063, respectively). Charts were closed within 0 to 7 days with the implementation of a scribe instead of 7-21 days when awaiting dictations for transcription.

CONCLUSIONS

The implementation of a scribe in an academic otolaryngology clinic facilitated more rapid completion of documentation while decreasing provider hours/day in clinic. We feel the analysis can be generalized to otolaryngology practitioners in general and the data structures we implemented are usable for others.

The Case for the Anesthesiologist-Informaticist

Health care has been transformed by computerization, and the use of electronic health record systems has become widespread. Anesthesia information management systems are commonly used in the operating room to maintain records of anesthetic care delivery. The perioperative environment and the practice of anesthesia generate a large volume of data that may be reused to support clinical decision-making, research, and process improvement. Anesthesiologists trained in clinical informatics, referred to as informaticists or informaticians, may help implement and optimize anesthesia information management systems. They may also participate in clinical research, management of information systems, and quality improvement in the operating room or throughout a health care system. Here, we describe the specialty of clinical informatics, how anesthesiologists may obtain training in clinical informatics, and the considerations particular to the subspecialty of anesthesia informatics. Management of perioperative information systems, implementation of computerized clinical decision support systems in the perioperative environment, the role of virtual visits and remote monitoring, perioperative informatics research, perioperative process improvement, leadership, and change management are described from the perspective of the anesthesiologist-informaticist.

Not another box to check! Using the UTAUT to explore nurses' psychological adaptation to electronic health record usability

BACKGROUND

As the integration of electronic health records (EHRs) continues to expand throughout healthcare organizations worldwide, nurses are faced with the challenge to acclimate to a dynamic technology-based environment of patient care. As technology advances, EHR usability may influence nurses' clinical performance and require nurses to adapt to a wide range of situations associated with electronic documentation. While perceived benefits of EHRs include improvements in healthcare delivery and patient outcomes, there are also unintended consequences associated with EHR usability.

AIMS

The aim of this article is to explore dimensions of EHR usability that may influence nurses' psychological adaptation to the use of EHRs in daily practice.

MATERIALS AND METHODS

The unified theory of acceptance and use of technology (UTAUT) consists of four constructs theorized to have a direct influence on end-user behavior and acceptance of technology: performance expectancy, effort expectancy, social influence, and facilitating conditions. The UTAUT provides the framework to explore EHR usability as found in literature and describe the positive and negative psychological responses of nurses related to the use of EHRs in daily practice.

RESULTS

Integration of EHRs into daily nursing practice can result in positive experiences or have a negative impact on nurses ability to perform their role as patient caregivers.

DISCUSSION

While integration of EHRs into healthcare systems can facilitate improvements in patient outcomes, the delivery of patient care and nurses' daily practices can simultaneously be complicated by EHR usability issues.

CONCLUSION

Although positive and negative experiences of nurses in relationship to EHR usability can be found in literature, there is limited evidence on nurses' psychological adaptation to use of EHRs. Further research on EHR usability is needed based on nursing perspectives and feedback to determine the psychological impact EHRs exert on nurses, discover effective methods for resolving issues, and promote positive trends associated with EHR usability.

Health Professionals' Perspectives on Electronic Medical Record Infusion and Individual Performance: Model Development and Questionnaire Survey Study

Background

Electronic medical records (EMRs) are integrated information sources generated by health care professionals (HCPs) from various health care information systems. EMRs play crucial roles in improving the quality of care and medical decision-making and in facilitating cross-hospital health information exchange. Although many hospitals have invested considerable resources and efforts to develop EMRs for several years, the factors affecting the long-term success of EMRs, particularly in the EMR infusion stage, remain unclear.

Objective

The aim of this study was to investigate the effects of technology, user, and task characteristics on EMR infusion to determine the factors that largely affect EMR infusion. In addition, we examined the effect of EMR infusion on individual HCP performance.

Methods

A questionnaire survey was used to collect data from HCPs with >6 months experience in using EMRs in a Taiwanese teaching hospital. A total of 316 questionnaires were distributed and 211 complete copies were returned, yielding a valid response rate of 66.8%. The collected data were further analyzed using WarpPLS 5.0.

Results

EMR infusion (R²=0.771) was mainly affected by user habits (β=.411), portability (β=.217), personal innovativeness (β=.198), technostress (β=.169), and time criticality (β=.168), and individual performance (R²=0.541) was affected by EMR infusion (β=.735). This finding indicated that user (habit, personal innovativeness, and technostress), technology (portability), and task (mobility and time criticality) characteristics have major effects on EMR infusion. Furthermore, the results indicated that EMR infusion positively affects individual performance.

Conclusions

The factors identified in this study can extend information systems infusion theory and provide useful insights for the further improvement of EMR development in hospitals and by the government, specifically in its infusion stage. In addition, the developed instrument can be used as an assessment tool to identify the key factors for EMR infusion, and to evaluate the extent of EMR infusion and the individual performance of hospitals that have implemented EMR systems. Moreover, the results can help governments to understand the urgent needs of hospitals in implementing EMR systems, provide sufficient resources and support to improve the incentives of EMR development, and develop adequate EMR policies for the meaningful use of electronic health records among hospitals and clinics.

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.

Factors Affecting the Adoption of Electronic Data Reporting and Outcomes Among Selected Central Cancer Registries of the National Program of Cancer Registries

PURPOSE

The CDC's National Program of Cancer Registries has expanded the use of electronic reporting to collect more timely information on newly diagnosed cancers. The adoption, implementation, and use of electronic reporting vary significantly among central cancer registries. We identify factors affecting the adoption of electronic reporting among these registries.

METHODS

Directors and data managers of nine National Program of Cancer Registries took part in separate 1-hour telephone interviews in early 2019. Directors were asked about their registry's key data quality goals; staffing, resources, and tools used to aid processes; their definition and self-perception of electronic reporting adoption; key helpers and challenges; and cost and sustainability implications for adoption of electronic reporting. Data managers were asked about specific data collection processes, software applications, electronic reporting adoption and self-perception, information technology infrastructure, and helpers and challenges to data collection and processing, data quality, and sustainability of approach.

RESULTS

Larger registries identified organizational capacity and technical expertise as key aides. Other help for implementing electronic reporting processes came from partnerships, funding availability, management support, legislation, and access to an interstate data exchange. Common challenges among lower adopters included lack of capacity at both registry and data source levels, insufficient staffing, and a lack of information technology or technical support. Other challenges consisted of automation and interoperability of software, volume of cases received, state political environment, and quality of data received.

CONCLUSION

Feedback from the formative evaluation yielded several useful solutions that can guide implementation of electronic reporting and help refine the technical assistance provided to registries. Our findings may help guide future process and economic evaluations of electronic reporting and identify best practices to strengthen registry operations.

Frustration With Technology and its Relation to Emotional Exhaustion Among Health Care Workers: Cross-sectional Observational Study

BACKGROUND

New technology adoption is common in health care, but it may elicit frustration if end users are not sufficiently considered in their design or trained in their use. These frustrations may contribute to burnout.

OBJECTIVE

This study aimed to evaluate and quantify health care workers' frustration with technology and its relationship with emotional exhaustion, after controlling for measures of work-life integration that may indicate excessive job demands.

METHODS

This was a cross-sectional, observational study of health care workers across 31 Michigan hospitals. We used the Safety, Communication, Operational Reliability, and Engagement (SCORE) survey to measure work-life integration and emotional exhaustion among the survey respondents. We used mixed-effects hierarchical linear regression to evaluate the relationship among frustration with technology, other components of work-life integration, and emotional exhaustion, with adjustment for unit and health care worker characteristics.

RESULTS

Of 15,505 respondents, 5065 (32.7%) reported that they experienced frustration with technology on at least 3-5 days per week. Frustration with technology was associated with higher scores for the composite Emotional Exhaustion scale (r=0.35, P<.001) and each individual item on the Emotional Exhaustion scale (r=0.29-0.36, P<.001 for all). Each 10-point increase in the frustration with technology score was associated with a 1.2-point increase (95% CI 1.1-1.4) in emotional exhaustion (both measured on 100-point scales), after adjustment for other work-life integration items and unit and health care worker characteristics.

CONCLUSIONS

This study found that frustration with technology and several other markers of work-life integration are independently associated with emotional exhaustion among health care workers. Frustration with technology is common but not ubiquitous among health care workers, and it is one of several work-life integration factors associated with emotional exhaustion. Minimizing frustration with health care technology may be an effective approach in reducing burnout among health care workers.

Digital Transformation in Ophthalmic Clinical Care During the COVID-19 Pandemic

ABSTRACT

COVID-19 has placed unprecedented pressure on health systems globally, whereas simultaneously stimulating unprecedented levels of transformation. Here, we review digital adoption that has taken place during the pandemic to drive improvements in ophthalmic clinical care, with a specific focus on out-of-hospital triage and services, clinical assessment, patient management, and use of electronic health records. We show that although there have been some successes, shortcomings in technology infrastructure prepandemic became only more apparent and consequential as COVID-19 progressed. Through our review, we emphasize the need for clinicians to better grasp and harness key technology trends such as telecommunications and artificial intelligence, so that they can effectively and safely shape clinical practice using these tools going forward.

Deriving Weight from Big Data: A Comparison of Body Weight Measurement Cleaning Algorithms (Preprint)

Background

Patient body weight is a frequently used measure in biomedical studies, yet there are no standard methods for processing and cleaning weight data. Conflicting documentation on constructing body weight measurements presents challenges for research and program evaluation.

Objective

In this study, we aim to describe and compare methods for extracting and cleaning weight data from electronic health record databases to develop guidelines for standardized approaches that promote reproducibility.

Methods

We conducted a systematic review of studies published from 2008 to 2018 that used Veterans Health Administration electronic health record weight data and documented the algorithms for constructing patient weight. We applied these algorithms to a cohort of veterans with at least one primary care visit in 2016. The resulting weight measures were compared at the patient and site levels.

Results

We identified 496 studies and included 62 (12.5%) that used weight as an outcome. Approximately 48% (27/62) included a replicable algorithm. Algorithms varied from cutoffs of implausible weights to complex models using measures within patients over time. We found differences in the number of weight values after applying the algorithms (71,961/1,175,995, 6.12% to 1,175,177/1,175,995, 99.93% of raw data) but little difference in average weights across methods (93.3, SD 21.0 kg to 94.8, SD 21.8 kg). The percentage of patients with at least 5% weight loss over 1 year ranged from 9.37% (4933/52,642) to 13.99% (3355/23,987).

Conclusions

Contrasting algorithms provide similar results and, in some cases, the results are not different from using raw, unprocessed data despite algorithm complexity. Studies using point estimates of weight may benefit from a simple cleaning rule based on cutoffs of implausible values; however, research questions involving weight trajectories and other, more complex scenarios may benefit from a more nuanced algorithm that considers all available weight data.

Systematic review of approaches to use of neighborhood-level risk factors with clinical data to predict clinical risk and recommend interventions

BACKGROUND

Despite a large body of literature investigating how the environment influences health outcomes, most published work to date includes only a limited subset of the rich clinical and environmental data that is available and does not address how these data might best be used to predict clinical risk or expected impact of clinical interventions.

OBJECTIVE

Identify existing approaches to inclusion of a broad set of neighborhood-level risk factors with clinical data to predict clinical risk and recommend interventions.

METHODS

A systematic review of scientific literature published and indexed in PubMed, Web of Science, Association of Computing Machinery (ACM) and SCOPUS from 2010 through October 2020 was performed. To be included, articles had to include search terms related to Electronic Health Record (EHR) data Neighborhood-Level Risk Factors (NLRFs), and Machine Learning (ML) Methods. Citations of relevant articles were also reviewed for additional articles for inclusion. Articles were reviewed and coded by two independent reviewers to capture key information including data sources, linkage of EHR to NRLFs, methods, and results. Articles were assessed for quality using a modified Quality Assessment Tool for Systematic Reviews of Observational Studies (QATSO).

RESULTS

A total of 334 articles were identified for abstract review. 36 articles were identified for full review with 19 articles included in the final analysis. All but two of the articles included socio-demographic data derived from the U.S. Census and we found great variability in sources of NLRFs beyond the Census. The majority or the articles (14 of 19) included broader clinical (e.g. medications, labs and co-morbidities) and demographic information about the individual from the EHR in addition to the clinical outcome variable. Half of the articles (10) had a stated goal to predict the outcome(s) of interest. While results of the studies reinforced the correlative association of NLRFs to clinical outcomes, only one article found that adding NLRFs into a model with other data added predictive power with the remainder concluding either that NLRFs were of mixed value depending on the model and outcome or that NLRFs added no predictive power over other data in the model. Only one article scored high on the quality assessment with 13 scoring moderate and 4 scoring low.

CONCLUSIONS

In spite of growing interest in combining NLRFs with EHR data for clinical prediction, we found limited evidence that NLRFs improve predictive power in clinical risk models. We found these data and methods are being used in four ways. First, early approaches to include broad NLRFs to predict clinical risk primarily focused on dimension reduction for feature selection or as a data preparation step to input into regression analysis. Second, more recent work incorporates NLRFs into more advanced predictive models, such as Neural Networks, Random Forest, and Penalized Lasso to predict clinical outcomes or predict value of interventions. Third, studies that test how inclusion of NLRFs predict clinical risk have shown mixed results regarding the value of these data over EHR or claims data alone and this review surfaced evidence of potential quality challenges and biases inherent to this approach. Finally, NLRFs were used with unsupervised learning to identify underlying patterns in patient populations to recommend targeted interventions. Further access to computable, high quality data is needed along with careful study design, including sub-group analysis, to better determine how these data and methods can be used to support decision making in a clinical setting.

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.

Anesthesia in the modern world of apps and technology: Implications and impact on wellness

Recent decades have seen an unprecedented leap in digital innovation, with far-reaching implications in healthcare. Anesthesiologists have historically championed the adoption of new technologies. However, the rapid evolution of these technologies has outpaced attempts at studying their potential impact on healthcare providers' well-being. This document introduces several categories of workplace technologies commonly encountered by the anesthesiologist. We examine examples of novel technology and the impact of these digital interventions on the anesthesiologist's well-being. We also review popular personalized technology aimed at improving wellness and the impact on well-being examined. Finally, technology acceptance models are introduced to improve technology adoption, which, when appropriately applied, may minimize the negative impacts of technology on anesthesiologists' well-being. Incorporating quantitative, serial assessments of well-being as part of technology implementation are proposed as a future direction for examining the wellness impact of technology on anesthesiologists.

Formative independent evaluation of a digital change programme in the English National Health Service: study protocol for a longitudinal qualitative study

INTRODUCTION

Many countries are launching large-scale, digitally enabled change programmes as part of efforts to improve the quality, safety and efficiency of care. We have been commissioned to conduct an independent evaluation of a major national change programme, the Global Digital Exemplar (GDE) Programme, which aims to develop exemplary digital health solutions and encourage their wider adoption by creating a learning ecosystem across English National Health Service (NHS) provider organisations.

METHODS AND ANALYSIS

This theoretically informed, qualitative, longitudinal formative evaluation comprises five inter-related work packages. We will conduct a combination of 12 in-depth and 24 broader qualitative case studies in GDE sites exploring digital transformation, local learning and mechanisms of spread of knowledge within the Programme and across the wider NHS. Data will be collected through a combination of semistructured interviews with managers, implementation staff (clinical and non-clinical), vendors and policymakers, plus non-participant observations of meetings, site visits, workshops and documentary analysis of strategic local and national plans. Data will be analysed through inductive and deductive methods, beginning with in-depth case study sites and testing the findings against data from the wider sample and national stakeholders.

ETHICS AND DISSEMINATION

This work is commissioned as part of a national change programme and is therefore a service evaluation. We have ethical approval from the University of Edinburgh. Results will be disseminated at six monthly intervals to national policymakers, and made available via our publicly accessible website. We will also identify lessons for the management and evaluation of large-scale evolving digital health change programmes that are of international relevance.

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.

ClinicNet: machine learning for personalized clinical order set recommendations

OBJECTIVE

This study assesses whether neural networks trained on electronic health record (EHR) data can anticipate what individual clinical orders and existing institutional order set templates clinicians will use more accurately than existing decision support tools.

MATERIALS AND METHODS

We process 57 624 patients worth of clinical event EHR data from 2008 to 2014. We train a feed-forward neural network (ClinicNet) and logistic regression applied to the traditional problem structure of predicting individual clinical items as well as our proposed workflow of predicting existing institutional order set template usage.

RESULTS

ClinicNet predicts individual clinical orders (precision = 0.32, recall = 0.47) better than existing institutional order sets (precision = 0.15, recall = 0.46). The ClinicNet model predicts clinician usage of existing institutional order sets (avg. precision = 0.31) with higher average precision than a baseline of order set usage frequencies (avg. precision = 0.20) or a logistic regression model (avg. precision = 0.12).

DISCUSSION

Machine learning methods can predict clinical decision-making patterns with greater accuracy and less manual effort than existing static order set templates. This can streamline existing clinical workflows, but may not fit if historical clinical ordering practices are incorrect. For this reason, manually authored content such as order set templates remain valuable for the purposeful design of care pathways. ClinicNet's capability of predicting such personalized order set templates illustrates the potential of combining both top-down and bottom-up approaches to delivering clinical decision support content.

CONCLUSION

ClinicNet illustrates the capability for machine learning methods applied to the EHR to anticipate both individual clinical orders and existing order set templates, which has the potential to improve upon current standards of practice in clinical order entry.

Evaluating the Applications of Health Information Technologies in China During the Past 11 Years: Consecutive Survey Data Analysis

Background

To achieve universal access to medical resources, China introduced its second health care reform in 2010, with health information technologies (HIT) as an important technical support point.

Objective

This study is the first attempt to explore the unique contributions and characteristics of HIT development in Chinese hospitals from the three major aspects of hospital HIT—human resources, funding, and materials—in an all-around, multi-angled, and time-longitudinal manner, so as to serve as a reference for decision makers in China and the rest of the world when formulating HIT development strategies.

Methods

A longitudinal research method is used to analyze the results of the CHIMA Annual Survey of Hospital Information System in China carried out by a Chinese national industrial association, CHIMA, from 2007 to 2018. The development characteristics of human resources, funding, and materials of HIT in China for the past 12 years are summarized. The Bass model is used to fit and predict the popularization trend of EMR in Chinese hospitals from 2007 to 2020.

Results

From 2007 to 2018, the CHIMA Annual Survey interviewed 10,954 hospital CIOs across 32 administrative regions in Mainland China. Compared with 2007, as of 2018, in terms of human resources, the average full time equivalent (FTE) count in each hospital’s IT center is still lower than the average level of US counterparts in 2014 (9.66 FTEs vs. 34 FTEs). The proportion of CIOs with a master’s degree or above was 25.61%, showing an increase of 18.51%, among which those with computer-related backgrounds accounted for 64.75%, however, those with a medical informatics background only accounted for 3.67%. In terms of funding, the sampled hospitals’ annual HIT investment increased from ¥957,700 (US $136,874) to ¥6.376 million (US $911,261), and the average investment per bed increased from ¥4,600 (US $658) to ¥8,100 (US $1158). In terms of information system construction, as of 2018, the average EMR implementation rate of the sampled hospitals exceeded the average level of their US counterparts in 2015 and their German counterparts in 2017 (85.26% vs. 83.8% vs. 68.4%, respectively). The results of the Bass prediction model show that Chinese hospitals will likely reach an adoption rate of 91.4% by 2020 (R²=0.95).

Conclusions

In more than 10 years, based on this top-down approach, China’s medical care industry has accepted government instructions and implemented the unified model planned by administrative intervention. With only about one-fifth of the required funding, and about one-fourth of the required human resources per hospital as compared to the US HITECH project, China’s EMR coverage in 2018 exceeded the average level of its US counterparts in 2015 and German counterparts in 2017. This experience deserves further study and analysis by other countries.

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.

Understanding Patient Information Needs About Their Clinical Laboratory Results: A Study of Social Q&A Site

Clinical data, such as laboratory test results, is increasingly being made available to patients through patient portals. However, patients often have difficulties understanding and acting upon the clinical data presented in portals. As such, many turn to online resources to fill their knowledge gaps and obtain actionable advice. In this work, we present a content analysis of the questions posted in a major social Q&A site to characterize lay people's general information needs concerning laboratory test results and to inform the design of patient portals for supporting patients' understanding of clinical data. We identified 15 information needs related to laboratory test results, and clustered them under four themes: understanding the results of lab test, interpreting doctor's diagnosis, learning about lab tests, and consulting the next steps. We draw on our findings to discuss design opportunities for supporting the understanding of laboratory results.

The experience and challenges of healthcare-reform-driven medical consortia and Regional Health Information Technologies in China: A longitudinal study

OBJECTIVE

To achieve universal access to medical resources-a partial goal of the second ambitious health reform since 2010-the Chinese government aimed to build a regional medical consortium and enhance the efficiency of health information exchange (HIE). We analyzed the experience of constructing a medical consortium in Chinese hospitals, which was based on regional health information technology (RHIT) promoted by HIE.

METHOD

In this longitudinal study, we analyzed the results of the annual surveys that were conducted by the China Hospital Information Management Association from 2006 to 2015. The survey results mainly concerned whether hospitals should join the regional medical consortium, the methods used for sharing inter-hospital medical data, and the out-of-hospital information interaction system. The Bass diffusion model was adopted to fit and predict the proportion of Chinese hospitals joining the consortium from 2006 to 2025.

RESULT

As of 2015, the survey results of 7272 hospitals were obtained. The proportion of hospitals in partnership systems increased from 3.0% in 2007 to 57.2% in 2015. There has been a rapid development in the electronic sharing of medical data between hospitals. The proportion of hospitals that relied solely on paper documents for data interaction decreased from 43.3% in 2011 to 8.0% in 2015. There was a strong positive linear correlation between hospitals joining the consortium and the accessibility of electronic medical data exchange within hospitals (r = 0.925). The proportions of hospitals that supported dual referral systems and appointments, data browsing between hospitals and regional information systems, and remote consultation services increased to 65.0%, 61.6%, and 81.9% in 2015, as compared to 18.8%, 16.8%, and 10.9% in 2011, respectively. The Bass prediction model showed that the goal of recruiting 90% of the hospitals to the consortium by 2020 will likely be achieved (adjusted R² = 0.93).

CONCLUSION

The Chinese government has applied a top-down, high-level design model to promote the rapid development of a medical consortium, in which the RHIT technologies are crucial technical enabler.

Diffusion dynamics of electronic health records: A longitudinal observational study comparing data from hospitals in Germany and the United States

BACKGROUND

While aiming for the same goal of building a national eHealth Infrastructure, Germany and the United States pursued different strategic approaches - particularly regarding the role of promoting the adoption and usage of hospital Electronic Health Records (EHR).

OBJECTIVE

To measure and model the diffusion dynamics of EHRs in German hospital care and to contrast the results with the developments in the US.

MATERIALS AND METHODS

All acute care hospitals that were members of the German statutory health system were surveyed during the period 2007-2017 for EHR adoption. Bass models were computed based on the German data and the corresponding data of the American Hospital Association (AHA) from non-federal hospitals in order to model and explain the diffusion of innovation.

RESULTS

While the diffusion dynamics observed in the US resembled the typical s-shaped curve with high imitation effects (q = 0.583) but with a relatively low innovation effect (p = 0.025), EHR diffusion in Germany stagnated with adoption rates of approx. 50% (imitation effect q = -0.544) despite a higher innovation effect (p = 0.303).

DISCUSSION

These findings correlate with different governmental strategies in the US and Germany of financially supporting EHR adoption. Imitation only seems to work if there are financial incentives, e.g. those of the HITECH Act in the US. They are lacking in Germany, where the government left health IT adoption strategies solely to the free market and the consensus among all of the stakeholders.

CONCLUSION

Bass diffusion models proved to be useful for distinguishing the diffusion dynamics in German and US non-federal hospitals. When applying the Bass model, the imitation parameter needs a broader interpretation beyond the network effects, including driving forces such as incentives and regulations, as was demonstrated by this study.

The Use of an Electronic Health Record Patient Portal to Access Diagnostic Test Results by Emergency Patients at an Academic Medical Center: Retrospective Study

BACKGROUND

Electronic health record (EHR) patient portals provide a means by which patients can access their health information, including diagnostic test results. Little is known about portal usage by emergency department (ED) patients.

OBJECTIVE

The study aimed to assess patient portal utilization by ED patients at an academic medical center using account activation rates along with the rates of access of diagnostic test results (laboratory results and radiology reports), analyzing the impact of age, gender, and self-reported patient race.

METHODS

This institutional review board-approved retrospective study was performed at a 60,000-visits-per-year university-based ED. We utilized EHR data reporting tools to examine EHR portal activation and utilization for all patients who had at least one ED encounter with one or more diagnostic tests performed between October 1, 2016, and October 1, 2017. The total dataset for laboratory testing included 208,635 laboratory tests on 25,361 unique patients, of which 9482 (37.39%) had active portal accounts. The total dataset for radiologic imaging included 23,504 radiology studies on 14,455 unique patients, of which 5439 (37.63%) had an active portal account.

RESULTS

Overall, 8.90% (18,573/208,635) of laboratory tests and 8.97% (2019/22,504) of radiology reports ordered in the ED were viewed in the patient portal. The highest rates of viewing of laboratory and radiology results were seen for those who were female, were aged 0 to 11 years (parent or guardian viewing by proxy) and 18 to 60 years, and self-reported their race as Caucasian or Asian. The lowest rates were for those who were teenagers, aged older than 81 years, African American/black, and Hispanic/Latino. Infectious disease, urinalysis, and pregnancy testing constituted the highest number of laboratory tests viewed. Magnetic resonance imaging reports were viewed at higher rates than computed tomography or x-ray studies (P<.001). Approximately half of all the diagnostic test results accessed by patients were reviewed within 72 hours of availability in the patient portal (laboratory results: 9904/18,573, 53.32% and radiology reports: 971/2019, 48.1%). On the other extreme, 19.9% (3701/18,573) of laboratory results and 31.6% (639/2019) of radiology reports were viewed more than 2 weeks after availability in the portal.

CONCLUSIONS

The data highlight the relatively low use of a patient portal by ED patients and existing disparities between patient groups. There can be wide lag time (months) between result/report availability and access by patients. Opportunities for improvement exist for both activation and more robust utilization of patient portals by ED patients.

Transformation of the Doctor-Patient Relationship: Big Data, Accountable Care, and Predictive Health Analytics

The medical profession is steeped in traditions that guide its practice. These traditions were developed to preserve the well-being of patients. Transformations in science, technology, and society, while maintaining a self-governance structure that drives the goal of care provision, have remained hallmarks of the profession. The purpose of this paper is to examine ethical challenges in health care as it relates to Big Data, Accountable Care Organizations, and Health Care Predictive Analytics using the principles of biomedical ethics laid out by Beauchamp and Childress (autonomy, beneficence, non-maleficence, and justice). Among these are the use of Electronic Health Records within stipulations of the Health Insurance Portability and Accountability Act. Clinicians are well-positioned to impact health policy development to address ethical issues associated with the use of Big Data, Accountable Care, and Health Care Predictive Analytics as we work to transform the doctor-patient relationship towards improving population health outcomes and creating a healthier society.

Understanding the use of digital technology to promote human papillomavirus vaccination - A RE-AIM framework approach

The human papillomavirus virus (HPV) vaccine is effective at preventing various cancers, but coverage falls short of targets that are needed for community protection. Here, we use the RE-AIM implementation framework (Reach, Effectiveness, Adoption, Implementation, Maintenance) to understand how text, email, and electronic health record (EHR) reminders and social media campaigns can be used as part of policy and practice interventions to increase HPV vaccination. These technology-based interventions could be used together and mainstreamed into clinical and system-based practice to have the greatest impact. Of the interventions explored, text-based, email-based, and EHR reminders have the most evidence behind them to support their effectiveness. While there are several studies of promotion of the HPV vaccine on social media, more studies are needed to demonstrate their effects and better methods are needed to be able to attribute results to these interventions.

Facility and Regional Factors Associated With the New Adoption of Electronic Medical Records in Japan: Nationwide Longitudinal Observational Study

Background

The rate of adoption of electronic medical record (EMR) systems has increased internationally, and new EMR adoption is currently a major topic in Japan. However, no study has performed a detailed analysis of longitudinal data to evaluate the changes in the EMR adoption status over time.

Objective

This study aimed to evaluate the changes in the EMR adoption status over time in hospitals and clinics in Japan and to examine the facility and regional factors associated with these changes.

Methods

Secondary longitudinal data were created by matching data in fiscal year (FY) 2011 and FY 2014 using reference numbers. EMR adoption status was defined as “EMR adoption,” “specified adoption schedule,” or “no adoption schedule.” Data were obtained for hospitals (n=4410) and clinics (n=67,329) that had no adoption schedule in FY 2011 and for hospitals (n=1068) and clinics (n=3132) with a specified adoption schedule in FY 2011. The EMR adoption statuses of medical institutions in FY 2014 were also examined. A multinomial logistic model was used to investigate the associations between EMR adoption status in FY 2014 and facility and regional factors in FY 2011. Considering the regional variations of these models, multilevel analyses with second levels were conducted. These models were constructed separately for hospitals and clinics, resulting in four multinomial logistic models. The odds ratio (OR) and 95% Bayesian credible interval (CI) were estimated for each variable.

Results

A total of 6.9% of hospitals and 14.82% of clinics with no EMR adoption schedules in FY 2011 had adopted EMR by FY 2014, while 10.49% of hospitals and 33.65% of clinics with specified adoption schedules in FY 2011 had cancelled the scheduled adoption by FY 2014. For hospitals with no adoption schedules in FY 2011, EMR adoption/scheduled adoption was associated with practice size characteristics, such as number of outpatients (from quantile 4 to quantile 1: OR 1.67, 95% CI 1.005-2.84 and OR 2.40, 95% CI 1.80-3.21, respectively), and number of doctors (from quantile 4 to quantile 1: OR 4.20, 95% CI 2.39-7.31 and OR 2.02, 95% CI 1.52-2.64, respectively). For clinics with specified EMR adoption schedules in FY 2011, the factors negatively associated with EMR adoption/cancellation of scheduled EMR adoption were the presence of beds (quantile 4 to quantile 1: OR 0.57, 95% CI 0.45-0.72 and OR 0.74, 95% CI 0.58-0.96, respectively) and having a private establisher (quantile 4 to quantile 1: OR 0.27, 95% CI 0.13-0.55 and OR 0.43, 95% CI 0.19-0.91, respectively). No regional factors were significantly associated with the EMR adoption status of hospitals with no EMR adoption schedules; population density was positively associated with EMR adoption in clinics with no EMR adoption schedule (quantile 4 to quantile 1: OR 1.49, 95% CI 1.32-1.69).

Conclusions

Different approaches are needed to promote new adoption of EMR systems in hospitals as compared to clinics. It is important to induce decision making in small- and medium-sized hospitals, and regional postdecision technical support is important to avoid cancellation of scheduled EMR adoption in clinics.

Neural Networks for Clinical Order Decision Support

Consistent and high quality medical decisions are difficult as the amount of literature, data, and treatment options grow. We developed a model to provide automated physician order decision support suggestions for inpatient care through a feed-forward neural network. Given a patient's current status based on information data-mined and extracted from the Electronic Health Record (EHR), our model predicts clinical orders a physician enters for a patient within 24 hours. As a reference benchmark of real-world standard-of-care clinical decision support, existing manually-curated order sets implemented in the hospital demonstrate precision: 0.21, recall: 0.48, AUROC: 0.75 relative to what clinicians actually order within 24 hours. Our feed-forward model provides an automated, scalable, and robust system that achieves precision: 0.41, recall: 0.61, AUROC: 0.80.

Improving Oral-Systemic Healthcare through the Interoperability of Electronic Medical and Dental Records: An Exploratory Study

OBJECTIVES

Electronic health records (EHRs) are rarely shared among medical and dental providers. The purpose of this study was to assess current information sharing and the value of improved electronic information sharing among physicians and dentists in Germany and the United States.

MATERIALS AND METHODS

A survey was validated and distributed electronically to physicians and dentists at four academic medical centers. Respondents were asked anonymously about EHR use and the medical and dental information most valuable to their practice.

RESULTS

There were 118 responses, a response rate of 23.2%. The majority (63.9%) of respondents were dentists and the remainder were physicians. Most respondents (66.3%) rated the importance of sharing information an 8 or above on a 1-to-10 Likert scale. Dentists rated the importance of sharing clinical information significantly higher than physicians (p = 0.0033). Most (68.5%) providers could recall an instance when access to medical or dental information would have improved patient care. Dentists were significantly more likely to report this than physicians (p = 0.008).

CONCLUSION

Physicians would value a standardized measure of "oral health" in their EHR. Dentists were less likely to find specific medical diagnostic test results of value. Both dentists and physicians agreed that oral-systemic health was important; interoperable EHRs could facilitate information transfer between providers and enhance research on oral-systemic health connections. Both dentists and physicians believed that an interoperable EHR would be useful to practice, but desired information was different between these groups. Refinement of the information needed for shared practice is required.

The Digitization of Patient Care: A Review of the Effects of Electronic Health Records on Health Care Quality and Utilization

Electronic health records (EHRs) adoption has become nearly universal during the past decade. Academic research into the effects of EHRs has examined factors influencing adoption, clinical care benefits, financial and cost implications, and more. We provide an interdisciplinary overview and synthesis of this literature, drawing on work in public and population health, informatics, medicine, management information systems, and economics. We then chart paths forward for policy, practice, and research.

Web-based patient portal access in an orthopedic adult reconstruction patient population

Background

We surveyed patients in an adult reconstruction practice as to their use of the Web-based portal provided by our electronic health record, seeking to reveal patterns of use and helpfulness.

Methods

A total of 150 completed surveys were received. The survey queried demographics, the number of clinic visits, Internet access, portal activation, portal use frequency, and portal information questions and how patients answered them. Helpfulness was rated from 1 (not helpful) to 5 (very helpful). Statistical analysis included bivariate analysis and logistic regression, with odds ratio (OR) and 95% confidence interval (CI) reported.

Results

The mean age was 67.6 years. Most were females (n = 97, 65.1%). Most (68.7%) patients used the portal. Younger age (OR, 0.94; CI, 0.90-0.99) and access to Internet (OR, 31.8; CI, 8.5-119.4) predicted portal use (P < .005), whereas gender and number of clinic visits did not (P > .373). Of all, 47.5% of patients were unclear about online chart information. Older age indicated being unclear of portal information (68.5 vs 66, P = .0002). Of those who clarified doubts regarding information (n = 67), 23 used the Internet (34.3%), 32 (47.7%) called the physician, and 12 (17.9%) asked a friend and/or family member. Most (90.3%) patients felt the portal was helpful in gathering health information.

Conclusions

Age and Internet access affected portal usage; ability to understand chart information decreased with age. Most patients used the Internet or a family member to clarify doubts regarding portal information. The use of portal data resulted in 32 extra communications to the physician.

Use of Health Information Technology in Patients Care Management: a Mixed Methods Study in Iran

Introduction

New technologies, including health information technologies, play an important role in effectiveness of management and nursing care services.

Aim

This study was aimed to determining the use of health information technology in patient care management in a case study in Iran.

Methods

This Mixed method study was conducted in 2018 in Kowsar Hospital of Semnan, Iran. Data gathered by an observational checklist and one questionnaire included two main parts, one demographic and another assessment of information technology use in care management of inpatients. The researcher prepared the questionnaire and its validity was verified. The data were organized and analyzed in the form of a descriptive analytic report. In the process of data collection, 10 participants including nurses, head nurses, physicians, radiology experts and Information Technology (IT) managers were interviewed and data analyzed using Directed Content Analysis.

Results

Nurses were satisfied with the computerized system and believed it can facilitate the affair. From the nurse's viewpoint, the most common use of the Health Information Technology (HIT) were access (observation) of patients admit and discharge information (100%), providing medicine and equipment, transfer of patients (92.3%). The least of them were retrieve of evidence in the care process (0 %) and judgment and analysis of radiological diagnostic procedures (0%). The potential of electronic record is not still applicable.

Conclusion

Use of modern information and communication technology in hospitals facilitates access and transfer of information, and also accelerates patient's admission and discharge process, relation between hospital units, providing medical equipment supporting affairs' process and diagnostic procedures. However, modifying organizational policies, improve the infrastructure and enhancing nurses' motivation in documenting nursing reports can be effective in increasing the impact of information technology in care management processes especially in electronic record and nurse's clinical judgment and evidence-based care.

Using Electronic Health Records To Generate Phenotypes For Research

Electronic health records contain patient-level data collected during and for clinical care. Data within the electronic health record include diagnostic billing codes, procedure codes, vital signs, laboratory test results, clinical imaging, and physician notes. With repeated clinic visits, these data are longitudinal, providing important information on disease development, progression, and response to treatment or intervention strategies. The near universal adoption of electronic health records nationally has the potential to provide population-scale real-world clinical data accessible for biomedical research, including genetic association studies. For this research potential to be realized, high-quality research-grade variables must be extracted from these clinical data warehouses. We describe here common and emerging electronic phenotyping approaches applied to electronic health records, as well as current limitations of both the approaches and the biases associated with these clinically collected data that impact their use in research. © 2018 by John Wiley & Sons, Inc.

Overlaying multiple sources of data to identify bottlenecks in clinical workflow

The pursuit of increased efficiency and quality of clinical care based on the analysis of workflow has seen the introduction of several modern technologies into medical environments. Electronic health records (EHRs) remain central to analysis of workflow, owing to their wide-ranging impact on clinical processes. The two most common interventions to facilitate EHR-related workflow analysis are automated location tracking using sensor-based technologies and EHR usage data logs. However, to maximize the potential of these technologies, and especially to facilitate workflow redesign, it is necessary to overlay these quantitative findings on the contextual data from qualitative methods such as ethnography. Such a complementary approach promises to yield more precise measures of clinical workflow that provide insights into how redesign could address inefficiencies. In this paper, we categorize clinical workflow in the Emergency Department (ED) into three types (perceived, real and ideal) to create a structured approach to workflow redesign using the available data. We use diverse data sources: sensor-based location tracking through Radio-Frequency Identification (RFID), summary EHR usage data logs, and data from physician interviews augmented by direct observations (through clinician shadowing). Our goal is to discover inefficiencies and bottlenecks that can be addressed to achieve a more ideal workflow state relative to its real and perceived state. We thereby seek to demonstrate a novel data-driven approach toward iterative workflow redesign that generalizes for use in a variety of settings. We also propose types of targeted support or adjustments to offset some of the inefficiencies we noted.

Primary care physicians' attitudes to the adoption of electronic medical records: a systematic review and evidence synthesis using the clinical adoption framework

BACKGROUND

Recent decades have seen rapid growth in the implementation of Electronic Medical Records (EMRs) in healthcare settings in both developed regions as well as low and middle income countries. Yet despite substantial investment, the implementation of EMRs in some primary care systems has lagged behind other settings, with piecemeal adoption of EMR functionality by primary care physicians (PCPs) themselves. We aimed to review and synthesise international literature on the attitudes of PCPs to EMR adoption using the Clinical Adoption (CA) Framework.

METHODS

MEDLINE, PsycINFO, and EMBASE were searched from 1st January 1996 to 1st August 2017 for studies investigating PCP attitudes towards EMR adoption. Papers were screened by two independent reviewers, and eligible studies selected for further assessment. Findings were categorised against the CA Framework and the quality of studies assessed against one of three appropriate tools.

RESULTS

Out of 2263 potential articles, 33 were included, based in North and South America, Europe, Middle East and Hong Kong. Concerns about the accessibility, reliability and EMR utility exerted an adverse influence on PCPs' attitudes to adoption. However many were positive about their potential to improve clinical productivity, patient safety and care quality. Younger, computer-literate PCPs, based in large/multi-group practices, were more likely to be positively inclined to EMR use than older physicians, less-skilled in technology use, based in solo practices. Adequate training, policies and procedures favourably impacted on PCPs' views on EMR implementation. Financial factors were common system level influencers shaping EMR adoption, from start-up costs to the resources required by ongoing use.

CONCLUSIONS

By using the CA Framework to synthesise the evidence, we identified a linked series of factors influencing PCPs attitudes to EMR adoption. Findings underline the need to involve end-users in future implementation programmes from the outset, to avoid the development of an EMR which is neither feasible nor acceptable for use in practice.

TRIAL REGISTRATION

PROSPERO CRD42016038790 .

Qualified Clinical Data Registries: How Wound Care Practitioners Can Make the Most Out of the Merit-Based Incentive Payment System

Significance: Wound care practitioners have no professional society to promote participation in a Qualified Clinical Data Registry (QCDR), which is essential to thrive under the Merit-Based Incentive Payment System (MIPS), and until recently have lacked relevant quality measures to report. Practitioners can now participate in the nonprofit U.S. Wound Registry (USWR) QCDR for MIPS credit, which can receive data from any certified electronic health record (EHR) and, in so doing, generate data useful for comparative effectiveness research. Recent Advances: For 2018, the Centers for Medicare and Medicaid Services (CMS) has approved 12 wound care and hyperbaric medicine-relevant quality measures and several clinical practice Improvement Activities, which can be reported for MIPS credit through the USWR. Several QCDR measures have met the CMS 3-year reporting criteria to establish national benchmark rates, likely enabling practitioners to achieve higher quality scores than possible with standard MIPS measures. The structured registry data generated have been harnessed to evaluate adherence to evidence-based clinical practice guidelines, understand real-world patient healing rates, and demonstrate the comparative effectiveness of wound therapies. Critical Issues: Wound care practitioners can participate in a QCDR for MIPS credit, which enables them to optimize their MIPS score, particularly if they transmit data directly from their EHR. Utilizing structured data for comparative effectiveness research may help ensure patient access to advanced therapeutics. Future Directions: By 2019, to overcome technological barriers to participation, USWR quality measures will be available as "apps" for EHRs that support the interface required to achieve the next stage of EHR certification as part of the open Application Programming Initiative.

The Impact of Law on Syndromic Disease Surveillance Implementation

CONTEXT

Legal environments influence how health information technologies are implemented in public health practice settings. Syndromic disease surveillance (SyS) is a relatively new approach to surveillance that depends heavily on health information technologies to achieve rapid awareness of disease trends. Evidence suggests that legal concerns have impeded the optimization of SyS.

OBJECTIVES

To (1) understand the legal environments in which SyS is implemented, (2) determine the perceived legal basis for SyS, and (3) identify perceived legal barriers and facilitators to SyS implementation.

DESIGN

Multisite case study in which 35 key informant interviews and 5 focus groups were conducted with 75 SyS stakeholders. Interviews and focus groups were audio recorded, transcribed, and analyzed by 3 coders using thematic content analysis. Legal documents were reviewed.

SETTING

Seven jurisdictions (5 states, 1 county, and 1 city) that were purposively selected on the basis of SyS capacity and legal environment.

PARTICIPANTS

Health department directors, SyS system administrators, legal counsel, and hospital personnel.

RESULTS

Federal (eg, HIPAA) and state (eg, notifiable disease reporting) laws that authorize traditional public health surveillance were perceived as providing a legal basis for SyS. Financial incentives for hospitals to satisfy Meaningful Use regulations have eased concerns about the legality of SyS and increased the number of hospitals reporting SyS data. Legal issues were perceived as barriers to BioSense 2.0 (the federal SyS program) participation but were surmountable.

CONCLUSION

Major legal reforms are not needed to promote more widespread use of SyS. The current legal environment is perceived by health department and hospital officials as providing a firm basis for SyS practice. This is a shift from how law was perceived when SyS adoption began and has policy implications because it indicates that major legal reforms are not needed to promote more widespread use of the technology. Beyond SyS, our study suggests that federal monetary incentives can ameliorate legal concerns regarding novel health information technologies.

SNOMED CT Concept Hierarchies for Sharing Definitions of Clinical Conditions Using Electronic Health Record Data

BACKGROUND

Defining clinical conditions from electronic health record (EHR) data underpins population health activities, clinical decision support, and analytics. In an EHR, defining a condition commonly employs a diagnosis value set or "grouper." For constructing value sets, Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) offers high clinical fidelity, a hierarchical ontology, and wide implementation in EHRs as the standard interoperability vocabulary for problems.

OBJECTIVE

This article demonstrates a practical approach to defining conditions with combinations of SNOMED CT concept hierarchies, and evaluates sharing of definitions for clinical and analytic uses.

METHODS

We constructed diagnosis value sets for EHR patient registries using SNOMED CT concept hierarchies combined with Boolean logic, and shared them for clinical decision support, reporting, and analytic purposes.

RESULTS

A total of 125 condition-defining "standard" SNOMED CT diagnosis value sets were created within our EHR. The median number of SNOMED CT concept hierarchies needed was only 2 (25th-75th percentiles: 1-5). Each value set, when compiled as an EHR diagnosis grouper, was associated with a median of 22 International Classification of Diseases (ICD)-9 and ICD-10 codes (25th-75th percentiles: 8-85) and yielded a median of 155 clinical terms available for selection by clinicians in the EHR (25th-75th percentiles: 63-976). Sharing of standard groupers for population health, clinical decision support, and analytic uses was high, including 57 patient registries (with 362 uses of standard groupers), 132 clinical decision support records, 190 rules, 124 EHR reports, 125 diagnosis dimension slicers for self-service analytics, and 111 clinical quality measure calculations. Identical SNOMED CT definitions were created in an EHR-agnostic tool enabling application across disparate organizations and EHRs.

CONCLUSION

SNOMED CT-based diagnosis value sets are simple to develop, concise, understandable to clinicians, useful in the EHR and for analytics, and shareable. Developing curated SNOMED CT hierarchy-based condition definitions for public use could accelerate cross-organizational population health efforts, "smarter" EHR feature configuration, and clinical-translational research employing EHR-derived data.

Using electronic health record data for environmental and place based population health research: a systematic review

PURPOSE

We conducted a systematic review of literature published on January 2000-May 2017 that spatially linked electronic health record (EHR) data with environmental information for population health research.

METHODS

We abstracted information on the environmental and health outcome variables and the methods and data sources used.

RESULTS

The automated search yielded 669 articles; 128 articles are included in the full review. The number of articles increased by publication year; the majority (80%) were from the United States, and the mean sample size was approximately 160,000. Most articles used cross-sectional (44%) or longitudinal (40%) designs. Common outcomes were health care utilization (32%), cardiometabolic conditions/obesity (23%), and asthma/respiratory conditions (10%). Common environmental variables were sociodemographic measures (42%), proximity to medical facilities (15%), and built environment and land use (13%). The most common spatial identifiers were administrative units (59%), such as census tracts. Residential addresses were also commonly used to assign point locations, or to calculate distances or buffer areas.

CONCLUSIONS

Future research should include more detailed descriptions of methods used to geocode addresses, focus on a broader array of health outcomes, and describe linkage methods. Studies should also explore using longitudinal residential address histories to evaluate associations between time-varying environmental variables and health outcomes.