Developing an Institute for Health Care Delivery Science: successes, challenges, and solutions in the first five years.

Medical knowledge is increasing at an exponential rate. At the same time, unexplained variations in practice and patient outcomes and unacceptable rates of medical errors and inefficiencies in health care delivery have emerged. Our Institute for Health Care Delivery Science (I-HDS) began in 2014 as a novel platform to conduct multidisciplinary healthcare delivery research. We followed ten strategies to develop a successful institute with excellence in methodology and strong understanding of the value of team science. Our work was organized around five hubs: 1) Quality/Process Improvement and Systematic Review, 2) Comparative Effectiveness Research, Pragmatic Clinical Trials, and Predictive Analytics, 3) Health Economics and Decision Modeling, 4) Qualitative, Survey, and Mixed Methods, and 5) Training and Mentoring. In the first 5 years of the I-HDS, we have identified opportunities for change in clinical practice through research using our health system's electronic health record (EHR) data, and designed programs to educate clinicians in the value of research to improve patient care and recognize efficiencies in processes. Testing the value of several model interventions has guided prioritization of evidence-based quality improvements. Some of the changes in practice have already been embedded in the EHR workflow successfully. Development and sustainability of the I-HDS has been fostered by a mix of internal and external funding, including philanthropic foundations. Challenges remain due to the highly competitive funding environment and changes needed to adapt the EHR to healthcare delivery research. Further stakeholder engagement and culture change working with hospital leadership and I-HDS core and affiliate members continues.

Understanding enterprise data warehouses to support clinical and translational research: impact, sustainability, demand management, and accessibility.

OBJECTIVES: Healthcare organizations, including Clinical and Translational Science Awards (CTSA) hubs funded by the National Institutes of Health, seek to enable secondary use of electronic health record (EHR) data through an enterprise data warehouse for research (EDW4R), but optimal approaches are unknown. In this qualitative study, our goal was to understand EDW4R impact, sustainability, demand management, and accessibility. MATERIALS AND METHODS: We engaged a convenience sample of informatics leaders from CTSA hubs (n = 21) for semi-structured interviews and completed a directed content analysis of interview transcripts. RESULTS: EDW4R have created institutional capacity for single- and multi-center studies, democratized access to EHR data for investigators from multiple disciplines, and enabled the learning health system. Bibliometrics have been challenging due to investigator non-compliance, but one hub's requirement to link all study protocols with funding records enabled quantifying an EDW4R's multi-million dollar impact. Sustainability of EDW4R has relied on multiple funding sources with a general shift away from the CTSA grant toward institutional and industry support. To address EDW4R demand, institutions have expanded staff, used different governance approaches, and provided investigator self-service tools. EDW4R accessibility can benefit from improved tools incorporating user-centered design, increased data literacy among scientists, expansion of informaticians in the workforce, and growth of team science. DISCUSSION: As investigator demand for EDW4R has increased, approaches to tracking impact, ensuring sustainability, and improving accessibility of EDW4R resources have varied. CONCLUSION: This study adds to understanding of how informatics leaders seek to support investigators using EDW4R across the CTSA consortium and potentially elsewhere.

Erratum: Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration - CORRIGENDUM.

[This corrects the article DOI: 10.1017/cts.2024.2.].

Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration.

Introduction: The focus on social determinants of health (SDOH) and their impact on health outcomes is evident in U.S. federal actions by Centers for Medicare & Medicaid Services and Office of National Coordinator for Health Information Technology. The disproportionate impact of COVID-19 on minorities and communities of color heightened awareness of health inequities and the need for more robust SDOH data collection. Four Clinical and Translational Science Award (CTSA) hubs comprising the Texas Regional CTSA Consortium (TRCC) undertook an inventory to understand what contextual-level SDOH datasets are offered centrally and which individual-level SDOH are collected in structured fields in each electronic health record (EHR) system potentially for all patients. Methods: Hub teams identified American Community Survey (ACS) datasets available via their enterprise data warehouses for research. Each hub's EHR analyst team identified structured fields available in their EHR for SDOH using a collection instrument based on a 2021 PCORnet survey and conducted an SDOH field completion rate analysis. Results: One hub offered ACS datasets centrally. All hubs collected eleven SDOH elements in structured EHR fields. Two collected Homeless and Veteran statuses. Completeness at four hubs was 80%-98%: Ethnicity, Race; < 10%: Education, Financial Strain, Food Insecurity, Housing Security/Stability, Interpersonal Violence, Social Isolation, Stress, Transportation. Conclusion: Completeness levels for SDOH data in EHR at TRCC hubs varied and were low for most measures. Multiple system-level discussions may be necessary to increase standardized SDOH EHR-based data collection and harmonization to drive effective value-based care, health disparities research, translational interventions, and evidence-based policy.

The need to strengthen the evaluation of the impact of Artificial Intelligence-based decision support systems on healthcare provision.

Despite the renewed interest in Artificial Intelligence-based clinical decision support systems (AI-CDS), there is still a lack of empirical evidence supporting their effectiveness. This underscores the need for rigorous and continuous evaluation and monitoring of processes and outcomes associated with the introduction of health information technology. We illustrate how the emergence of AI-CDS has helped to bring to the fore the critical importance of evaluation principles and action regarding all health information technology applications, as these hitherto have received limited attention. Key aspects include assessment of design, implementation and adoption contexts; ensuring systems support and optimise human performance (which in turn requires understanding clinical and system logics); and ensuring that design of systems prioritises ethics, equity, effectiveness, and outcomes. Going forward, information technology strategy, implementation and assessment need to actively incorporate these dimensions. International policy makers, regulators and strategic decision makers in implementing organisations therefore need to be cognisant of these aspects and incorporate them in decision-making and in prioritising investment. In particular, the emphasis needs to be on stronger and more evidence-based evaluation surrounding system limitations and risks as well as optimisation of outcomes, whilst ensuring learning and contextual review. Otherwise, there is a risk that applications will be sub-optimally embodied in health systems with unintended consequences and without yielding intended benefits.

Understanding enterprise data warehouses to support clinical and translational research: enterprise information technology relationships, data governance, workforce, and cloud computing.

OBJECTIVE: Among National Institutes of Health Clinical and Translational Science Award (CTSA) hubs, effective approaches for enterprise data warehouses for research (EDW4R) development, maintenance, and sustainability remain unclear. The goal of this qualitative study was to understand CTSA EDW4R operations within the broader contexts of academic medical centers and technology. MATERIALS AND METHODS: We performed a directed content analysis of transcripts generated from semistructured interviews with informatics leaders from 20 CTSA hubs. RESULTS: Respondents referred to services provided by health system, university, and medical school information technology (IT) organizations as "enterprise information technology (IT)." Seventy-five percent of respondents stated that the team providing EDW4R service at their hub was separate from enterprise IT; strong relationships between EDW4R teams and enterprise IT were critical for success. Managing challenges of EDW4R staffing was made easier by executive leadership support. Data governance appeared to be a work in progress, as most hubs reported complex and incomplete processes, especially for commercial data sharing. Although nearly all hubs (n = 16) described use of cloud computing for specific projects, only 2 hubs reported using a cloud-based EDW4R. Respondents described EDW4R cloud migration facilitators, barriers, and opportunities. DISCUSSION: Descriptions of approaches to how EDW4R teams at CTSA hubs work with enterprise IT organizations, manage workforces, make decisions about data, and approach cloud computing provide insights for institutions seeking to leverage patient data for research. CONCLUSION: Identification of EDW4R best practices is challenging, and this study helps identify a breadth of viable options for CTSA hubs to consider when implementing EDW4R services.

Creating Surveillance Data Infrastructure Using Laboratory Analytics: Leveraging Visiun and Epic Systems to Support COVID-19 Pandemic Response.

BACKGROUND: Pandemics are unpredictable and can rapidly spread. Proper planning and preparation for managing the impact of outbreaks is only achievable through continuous and systematic collection and analysis of health-related data. We describe our experience on how to comply with required reporting and develop a robust platform for surveillance data during an outbreak. MATERIALS AND METHODS: At Mount Sinai Health System, New York City, we applied Visiun, a laboratory analytics dashboard, to support main response activities. Epic System Inc.'s SlicerDicer application was used to develop clinical and research reports. We followed World Health Organization (WHO); federal and state guidelines; departmental policies; and expert consultation to create the framework. RESULTS: The developed dashboard integrated data from scattered sources are used to seamlessly distribute reports to key stakeholders. The main report categories included federal, state, laboratory, clinical, and research. The first two groups were created to meet government and state reporting requirements. The laboratory group was the most comprehensive category and included operational reports such as performance metrics, technician performance assessment, and analyzer metrics. The close monitoring of testing volumes and lab operational efficiency was essential to manage increasing demands and provide timely and accurate results. The clinical data reports were valuable for proper managing of medical surge requirements, such as healthcare workforce and medical supplies. The reports included in the research category were highly variable and depended on healthcare setting, research priorities, and available funding. We share a few examples of queries that were included in the designed framework for research projects. CONCLUSION: We reviewed here the key components of a conceptual surveillance framework required for a robust response to COVID-19 pandemics. We demonstrated leveraging a lab analytics dashboard, Visiun, combined with Epic reporting tools to function as a surveillance system. The framework could be used as a generic template for possible future outbreak events.

Evolution of the literature identifying physicians' roles in leadership, clinical development, and practice of the subspecialty of emergency medical services.

PURPOSE: The 2007 Institute of Medicine report entitled Emergency Medical Services at the Crossroads identified a need for the establishment of physician subspecialty certification in emergency medical services (EMS). The purpose of this study was to identify and explore the evolution of publications that define the role of the physician in EMS systems in the United States. METHODS: Three comprehensive searches were undertaken to identify articles that define the physician's role in the leadership, clinical development, and practice of EMS. Independent reviewers then evaluated these articles to further determine whether the articles identified the physician's role in EMS. Then, identified articles were classified by the type of publication in order to evaluate the transition from a non-peer reviewed to peer-reviewed literature base and an analysis was performed on the differences in the growth between these two groups. In addition, for the peer-reviewed articles, an analysis was performed to identify the proportion of articles that were quantitative versus qualitative in nature. RESULTS: The comprehensive review identified 1,504 articles. Ninety articles were excluded due to lack of relevance to the US. The remaining 1,414 articles were reviewed, and 194 papers that address the physician's role within EMS systems were identified; 72 additional articles were identified by hand search of references for a total of 266 articles. The percentage of peer-reviewed articles has increased steadily over the past three decades. In addition, the percentage of quantitative articles increased from the first decade to the second and third decades. CONCLUSIONS: This comprehensive review demonstrates that over the past 30 years an evidence base addressing the role of the physician in EMS has developed. This evidence base has steadily evolved to include a greater proportion of peer-reviewed, quantitative literature.

Investigating the Impact of Intensive Care Unit Interruptions on Patient Safety Events and Electronic Health Records Use: An Observational Study.

BACKGROUND: Constant interruptions and continual data flow result in information overload for clinicians and become barriers to identification and extraction of relevant patient data and its correct interpretation. The aim of the study was to describe the types, frequencies, and impact of intensive care unit (ICU) interruptions on patient safety event occurrences and electronic health records (EHR) use. METHODS: We conducted a live observational study for 6 weeks, observing critical care physicians' and other providers' communication while recording interruptions, patient safety events, and EHR use. RESULTS: Across 55 hours, the researchers observed 7515 ICU tasks, 15.7% of which were interrupted. We found that technological interruptions directly influences the occurrence of patient safety events: an increase in technological interruptions directly contributes to patient safety event occurrence (P = 0.004). Technological interruptions had a direct effect on human interruptions, as the frequency of technological interruptions increase, human interruptions also increase (P = 0.02). CONCLUSIONS: A prospective, observational study was conducted to understand the relationship between interruptions and patient safety events and EHR use, in a time-sensitive, activity-based study in a large academic medical center with a certified EHR system. We found that technological interruptions were statistically correlated to the occurrence of patient safety events, and human interruptions significantly affected the level of EHR use. This study recommends that ICUs adopt a safety culture that promotes minimizing unnecessary interruptions, such as side conversations during rounds, for improved quality of care.

Beyond Getting Rid of Stupid Stuff in the Electronic Health Record (Beyond-GROSS): Protocol for a User-Centered, Mixed-Method Intervention to Improve the Electronic Health Record System.

BACKGROUND: Up to 60% of health care providers experience one or more symptoms of burnout. Perceived clinician burden resulting in burnout arises from factors such as electronic health record (EHR) usability or lack thereof, perceived loss of autonomy, and documentation burden leading to less clinical time with patients. Burnout can have detrimental effects on health care quality and contributes to increased medical errors, decreased patient satisfaction, substance use, workforce attrition, and suicide. OBJECTIVE: This project aims to improve the user-centered design of the EHR by obtaining direct input from clinicians about deficiencies. Fixing identified deficiencies via user-centered design has the potential to improve usability, thereby increasing satisfaction by reducing EHR-induced burnout. METHODS: Quantitative and qualitative data will be obtained from clinician EHR users. The input will be received through a form built in a REDCap database via a link embedded in the home page of the EHR. The REDCap data will be analyzed in 2 main dimensions, based on nature of the input, what section of the EHR is affected, and what is required to fix the issue(s). Identified issues will be escalated to relevant stakeholders responsible for rectifying the problems identified. Data analysis, project evaluation, and lessons learned from the evaluation will be incorporated in a Plan-Do-Study-Act (PDSA) manner every 4-6 weeks. RESULTS: The pilot phase of the study began in October 2020 in the Gastroenterology Division at Mount Sinai Hospital, New York City, NY, which includes 39 physicians and 15 nurses. The pilot is expected to run over a 4-6-month period. The results of the REDCap data analysis will be reported within 1 month of completing the pilot phase. We will analyze the nature of requests received and the impact of rectified issues on the clinician EHR user. We expect that the results will reveal which sections of the EHR have the highest deficiencies while also highlighting issues about workflow difficulties. Perceived impact of the project on provider engagement, patient safety, and workflow efficiency will also be captured by evaluation survey and other qualitative methods where possible. CONCLUSIONS: The project aims to improve user-centered design of the EHR by soliciting direct input from clinician EHR users. The ultimate goal is to improve efficiency, reduce EHR inefficiencies with the possibility of improving staff engagement, and lessen EHR-induced clinician burnout. Our project implementation includes using informatics expertise to achieve the desired state of a learning health system as recommended by the National Academy of Medicine as we facilitate feedback loops and rapid cycles of improvement. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/25148.

Whole Slide Imaging for Teleconsultation: The Mount Sinai Hospital, Labcorp Dianon, and Philips Collaborative Experience.

BACKGROUND: With the emergence of whole slide imaging (WSI) and widespread access to high-speed Internet, pathology labs are now poised to implement digital pathology as a way to access diagnostic pathology expertise. This paper describes a collaborative partnership between a high-volume reference diagnostic laboratory (Labcorp) and an academic pathology department (Mount Sinai Hospital) in the transition from a traditional glass slide service to a digital platform. Using the standard framework of implementation science, we evaluate the consistency and quality of the Philips IntelliSite Pathology Solution (PIPS) in delivering save and efficient diagnostic services. MATERIALS AND METHODS: Digital and glass slide diagnoses of all consult cases were documented over a 12-month period. The Proctor guideline was used to quantitatively and qualitatively measure (e.g., focus group studies, field notes, and administrative data) implementation success. Lean techniques (e.g., value stream mapping) were applied to measure changes in efficiency with the transition to a digital platform. RESULTS: Our study supports the acceptability, high adoption, appropriateness, feasibility, fidelity, and sustainability of the digital pathology platform. The digital portal also improved the quality of patient care by increasing efficiency, effectiveness, safety, and timeliness. The intraobserver concordance rate was 100%. The digital transition resulted in a reduction in turnaround time from 86 h to an average 35 min and a 20-fold increase in efficiency of the consultation process. CONCLUSION: As the pathology community contemplates digital pathology as a transformational tool in providing broad access to diagnostic expertise across time and space, our study provides an implementation strategy along with evidence that the digital platform is safe, effective, and efficient.

Managing Pandemic Responses with Health Informatics - Challenges for Assessing Digital Health Technologies.

OBJECTIVES: To highlight the role of technology assessment in the management of the COVID-19 pandemic. METHOD: An overview of existing research and evaluation approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Informatics and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems. RESULTS: Evaluation of digital health technologies for COVID-19 should be based on their technical maturity as well as the scale of implementation. For mature technologies like telehealth whose efficacy has been previously demonstrated, pragmatic, rapid evaluation using the complex systems paradigm which accounts for multiple sociotechnical factors, might be more suitable to examine their effectiveness and emerging safety concerns in new settings. New technologies, particularly those intended for use on a large scale such as digital contract tracing, will require assessment of their usability as well as performance prior to deployment, after which evaluation should shift to using a complex systems paradigm to examine the value of information provided. The success of a digital health technology is dependent on the value of information it provides relative to the sociotechnical context of the setting where it is implemented. CONCLUSION: Commitment to evaluation using the evidence-based medicine and complex systems paradigms will be critical to ensuring safe and effective use of digital health technologies for COVID-19 and future pandemics. There is an inherent tension between evaluation and the imperative to urgently deploy solutions that needs to be negotiated.

A Multi-Phase Quality Improvement Initiative for the Treatment of Active Delirium in Older Persons.

BACKGROUND/OBJECTIVES: The Hospital Elder Life Program emerged 20 years ago as the reference model for delirium prevention in hospitalized older patients. However, implementation has been achieved at only 200 hospitals worldwide over the last 20 years. Among the barriers to implementation for some institutions is an unwillingness of hospital administration to assume the costs associated with implementing programs that service all hospitalized older patients at risk for delirium. Facing such a situation, we implemented a unique and self-evolving model of care of older hospitalized patients who had already developed delirium. DESIGN: Hypothesis testing was carried out using a pretest-posttest design on program administrative data. SETTING: Mount Sinai Hospital, New York, NY, a tertiary-care teaching facility. PARTICIPANTS A total of 9,214 consecutively admitted older patients to non-intensive care (ICU) inpatient units over a 5.5-year period, regardless of the suspected presence of delirium or risk status for developing delirium. INTERVENTION: A delirium intervention program targeting patients in whom delirium has already developed, with a modified delirium team supported by extensive workflow automation with custom tools in our electronic medical records system. MEASUREMENTS: Length of stay (LOS) for delirious and non-delirious patients on units where this program was piloted. Benzodiazepine, opiate, and antipsychotic use on the same units. RESULTS: There was a significant drop in LOS by 1.98 days (95% confidence interval = .24-3.71), a decrease in the average morphine dose equivalents administered from .38 mg to .21 mg per patient hospital day, diazepam dose equivalents from .22 mg to .15 mg per patient hospital day, and quetiapine administered from .17 mg to .14 mg per patient hospital day for delirious patients on the program pilot units. CONCLUSION: Elements of our unique active delirium treatment program may provide some direction to other program developers working on improving the care of older hospitalized delirious patients. However, the supporting evidence presented is limited, and a more rigorous prospective study is needed.

Comparison of Methods for Alcohol and Drug Screening in Primary Care Clinics.

Importance: Guidelines recommend that adult patients receive screening for alcohol and drug use during primary care visits, but the adoption of screening in routine practice remains low. Clinics frequently struggle to choose a screening approach that is best suited to their resources, workflows, and patient populations. Objective: To evaluate how to best implement electronic health record (EHR)-integrated screening for substance use by comparing commonly used screening methods and examining their association with implementation outcomes. Design, Setting, and Participants: This article presents the outcomes of phases 3 and 4 of a 4-phase quality improvement, implementation feasibility study in which researchers worked with stakeholders at 6 primary care clinics in 2 large urban academic health care systems to define and implement their optimal screening approach. Site A was located in New York City and comprised 2 clinics, and site B was located in Boston, Massachusetts, and comprised 4 clinics. Clinics initiated screening between January 2017 and October 2018, and 93 114 patients were eligible for screening for alcohol and drug use. Data used in the analysis were collected between January 2017 and October 2019, and analysis was performed from July 13, 2018, to March 23, 2021. Interventions: Clinics integrated validated screening questions and a brief counseling script into the EHR, with implementation supported by the use of clinical champions (ie, clinicians who advocate for change, motivate others, and use their expertise to facilitate the adoption of an intervention) and the training of clinic staff. Clinics varied in their screening approaches, including the type of visit targeted for screening (any visit vs annual examinations only), the mode of administration (staff-administered vs self-administered by the patient), and the extent to which they used practice facilitation and EHR usability testing. Main Outcomes and Measures: Data from the EHRs were extracted quarterly for 12 months to measure implementation outcomes. The primary outcome was screening rate for alcohol and drug use. Secondary outcomes were the prevalence of unhealthy alcohol and drug use detected via screening, and clinician adoption of a brief counseling script. Results: Patients of the 6 clinics had a mean (SD) age ranging from 48.9 (17.3) years at clinic B2 to 59.1 (16.7) years at clinic B3, were predominantly female (52.4% at clinic A1 to 64.6% at clinic A2), and were English speaking. Racial diversity varied by location. Of the 93,114 patients with primary care visits, 71.8% received screening for alcohol use, and 70.5% received screening for drug use. Screening at any visit (implemented at site A) in comparison with screening at annual examinations only (implemented at site B) was associated with higher screening rates for alcohol use (90.3%-94.7% vs 24.2%-72.0%, respectively) and drug use (89.6%-93.9% vs 24.6%-69.8%). The 5 clinics that used a self-administered screening approach had a higher detection rate for moderate- to high-risk alcohol use (14.7%-36.6%) compared with the 1 clinic that used a staff-administered screening approach (1.6%). The detection of moderate- to high-risk drug use was low across all clinics (0.5%-1.0%). Clinics with more robust practice facilitation and EHR usability testing had somewhat greater adoption of the counseling script for patients with moderate-high risk alcohol or drug use (1.4%-12.5% vs 0.1%-1.1%). Conclusions and Relevance: In this quality improvement study, EHR-integrated screening was feasible to implement in all clinics and unhealthy alcohol use was detected more frequently when self-administered screening was used at any primary care visit. The detection of drug use was low at all clinics, as was clinician adoption of counseling. These findings can be used to inform the decision-making of health care systems that are seeking to implement screening for substance use. Trial Registration: ClinicalTrials.gov Identifier: NCT02963948.

Understanding enterprise data warehouses to support clinical and translational research.

OBJECTIVE: Among National Institutes of Health Clinical and Translational Science Award (CTSA) hubs, adoption of electronic data warehouses for research (EDW4R) containing data from electronic health record systems is nearly ubiquitous. Although benefits of EDW4R include more effective, efficient support of scientists, little is known about how CTSA hubs have implemented EDW4R services. The goal of this qualitative study was to understand the ways in which CTSA hubs have operationalized EDW4R to support clinical and translational researchers. MATERIALS AND METHODS: After conducting semistructured interviews with informatics leaders from 20 CTSA hubs, we performed a directed content analysis of interview notes informed by naturalistic inquiry. RESULTS: We identified 12 themes: organization and data; oversight and governance; data access request process; data access modalities; data access for users with different skill sets; engagement, communication, and literacy; service management coordinated with enterprise information technology; service management coordinated within a CTSA hub; service management coordinated between informatics and biostatistics; funding approaches; performance metrics; and future trends and current technology challenges. DISCUSSION: This study is a step in developing an improved understanding and creating a common vocabulary about EDW4R operations across institutions. Findings indicate an opportunity for establishing best practices for EDW4R operations in academic medicine. Such guidance could reduce the costs associated with developing an EDW4R by establishing a clear roadmap and maturity path for institutions to follow. CONCLUSIONS: CTSA hubs described varying approaches to EDW4R operations that may assist other institutions in better serving investigators with electronic patient data.

Transitions from One Electronic Health Record to Another: Challenges, Pitfalls, and Recommendations.

OBJECTIVE: We address the challenges of transitioning from one electronic health record (EHR) to another-a near ubiquitous phenomenon in health care. We offer mitigating strategies to reduce unintended consequences, maximize patient safety, and enhance health care delivery. METHODS: We searched PubMed and other sources to identify articles describing EHR-to-EHR transitions. We combined these references with the authors' extensive experience to construct a conceptual schema and to offer recommendations to facilitate transitions. RESULTS: Our PubMed query retrieved 1,351 citations: 43 were relevant for full paper review and 18 met the inclusion criterion of focus on EHR-to-EHR transitions. An additional PubMed search yielded 1,014 citations, for which we reviewed 74 full papers and included 5. We supplemented with additional citations for a total of 70 cited. We distinguished 10 domains in the literature that overlap yet present unique and salient opportunities for successful transitions and for problem mitigation. DISCUSSION: There is scant literature concerning EHR-to-EHR transitions. Identified challenges include financial burdens, personnel resources, patient safety threats from limited access to legacy records, data integrity during migration, cybersecurity, and semantic interoperability. Transition teams must overcome inadequate human infrastructure, technical challenges, security gaps, unrealistic providers' expectations, workflow changes, and insufficient training and support-all factors affecting potential clinician burnout. CONCLUSION: EHR transitions are remarkably expensive, laborious, personnel devouring, and time consuming. The paucity of references in comparison to the topic's salience reinforces the necessity for this type of review and analysis. Prudent planning may streamline EHR transitions and reduce expenses. Mitigating strategies, such as preservation of legacy data, managing expectations, and hiring short-term specialty consultants can overcome some of the greatest hurdles. A new medical subject headings (MeSH) term for EHR transitions would facilitate further research on this topic.

Augmented intelligence with natural language processing applied to electronic health records for identifying patients with non-alcoholic fatty liver disease at risk for disease progression.

OBJECTIVE: Electronic health record (EHR) systems contain structured data (such as diagnostic codes) and unstructured data (clinical documentation). Clinical insights can be derived from analyzing both. The use of natural language processing (NLP) algorithms to effectively analyze unstructured data has been well demonstrated. Here we examine the utility of NLP for the identification of patients with non-alcoholic fatty liver disease, assess patterns of disease progression, and identify gaps in care related to breakdown in communication among providers. MATERIALS AND METHODS: All clinical notes available on the 38,575 patients enrolled in the Mount Sinai BioMe cohort were loaded into the NLP system. We compared analysis of structured and unstructured EHR data using NLP, free-text search, and diagnostic codes with validation against expert adjudication. We then used the NLP findings to measure physician impression of progression from early-stage NAFLD to NASH or cirrhosis. Similarly, we used the same NLP findings to identify mentions of NAFLD in radiology reports that did not persist into clinical notes. RESULTS: Out of 38,575 patients, we identified 2,281 patients with NAFLD. From the remainder, 10,653 patients with similar data density were selected as a control group. NLP outperformed ICD and text search in both sensitivity (NLP: 0.93, ICD: 0.28, text search: 0.81) and F2 score (NLP: 0.92, ICD: 0.34, text search: 0.81). Of 2281 NAFLD patients, 673 (29.5%) were believed to have progressed to NASH or cirrhosis. Among 176 where NAFLD was noted prior to NASH, the average progression time was 410 days. 619 (27.1%) NAFLD patients had it documented only in radiology notes and not acknowledged in other forms of clinical documentation. Of these, 170 (28.4%) were later identified as having likely developed NASH or cirrhosis after a median 1057.3 days. DISCUSSION: NLP-based approaches were more accurate at identifying NAFLD within the EHR than ICD/text search-based approaches. Suspected NAFLD on imaging is often not acknowledged in subsequent clinical documentation. Many such patients are later found to have more advanced liver disease. Analysis of information flows demonstrated loss of key information that could have been used to help prevent the progression of early NAFLD (NAFL) to NASH or cirrhosis. CONCLUSION: For identification of NAFLD, NLP performed better than alternative selection modalities. It then facilitated analysis of knowledge flow between physician and enabled the identification of breakdowns where key information was lost that could have slowed or prevented later disease progression.

How to Teach Health IT Evaluation: Recommendations for Health IT Evaluation Courses.

Systematic health IT evaluation studies are needed to ensure system quality and safety and to provide the basis for evidence-based health informatics. Well-trained health informatics specialists are required to guarantee that health IT evaluation studies are conducted in accordance with robust standards. Also, policy makers and managers need to appreciate how good evidence is obtained by scientific process and used as an essential justification for policy decisions. In a consensus-based approach with over 80 experts in health IT evaluation, recommendations for the structure, scope and content of health IT evaluation courses on the master or postgraduate level have been developed, supported by a structured analysis of available courses and of available literature. The recommendations comprise 15 mandatory topics and 15 optional topics for a health IT evaluation course.

Evidence-based Health Informatics Frameworks for Applied Use.

Health Informatics frameworks have been created surrounding the implementation, optimization, adoption, use and evaluation of health information technology including electronic health record systems and medical devices. In this contribution, established health informatics frameworks are presented. Important considerations for each framework are its purpose, component parts, rigor of development, the level of testing and validation its undergone, and its limitations. In order to understand how to use a framework effectively, it's often necessary to seek additional explanation via literature, documentation, and discussions with the developers.