Establishing a multidisciplinary initiative for interoperable electronic health record innovations at an academic medical center.

OBJECTIVE: To establish an enterprise initiative for improving health and health care through interoperable electronic health record (EHR) innovations. MATERIALS AND METHODS: We developed a unifying mission and vision, established multidisciplinary governance, and formulated a strategic plan. Key elements of our strategy include establishing a world-class team; creating shared infrastructure to support individual innovations; developing and implementing innovations with high anticipated impact and a clear path to adoption; incorporating best practices such as the use of Fast Healthcare Interoperability Resources (FHIR) and related interoperability standards; and maximizing synergies across research and operations and with partner organizations. RESULTS: University of Utah Health launched the ReImagine EHR initiative in 2016. Supportive infrastructure developed by the initiative include various FHIR-related tooling and a systematic evaluation framework. More than 10 EHR-integrated digital innovations have been implemented to support preventive care, shared decision-making, chronic disease management, and acute clinical care. Initial evaluations of these innovations have demonstrated positive impact on user satisfaction, provider efficiency, and compliance with evidence-based guidelines. Return on investment has included improvements in care; over $35 million in external grant funding; commercial opportunities; and increased ability to adapt to a changing healthcare landscape. DISCUSSION: Key lessons learned include the value of investing in digital innovation initiatives leveraging FHIR; the importance of supportive infrastructure for accelerating innovation; and the critical role of user-centered design, implementation science, and evaluation. CONCLUSION: EHR-integrated digital innovation initiatives can be key assets for enhancing the EHR user experience, improving patient care, and reducing provider burnout.

Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus.

OBJECTIVES: Artificial intelligence (AI), including predictive analytics, has great potential to improve the care of common chronic conditions with high morbidity and mortality. However, there are still many challenges to achieving this vision. The goal of this project was to develop and apply methods for enhancing chronic disease care using AI. METHODS: Using a dataset of 27,904 patients with diabetes, an analytical method was developed and validated for generating a treatment pathway graph which consists of models that predict the likelihood of alternate treatment strategies achieving care goals. An AI-driven clinical decision support system (CDSS) integrated with the electronic health record (EHR) was developed by encapsulating the prediction models in an OpenCDS Web service module and delivering the model outputs through a SMART on FHIR (Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources) web-based dashboard. This CDSS enables clinicians and patients to review relevant patient parameters, select treatment goals, and review alternate treatment strategies based on prediction results. RESULTS: The proposed analytical method outperformed previous machine-learning algorithms on prediction accuracy. The CDSS was successfully integrated with the Epic EHR at the University of Utah. CONCLUSION: A predictive analytics-based CDSS was developed and successfully integrated with the EHR through standards-based interoperability frameworks. The approach used could potentially be applied to many other chronic conditions to bring AI-driven CDSS to the point of care.

Using Computational Approaches to Improve Risk-Stratified Patient Management: Rationale and Methods.

BACKGROUND: Chronic diseases affect 52% of Americans and consume 86% of health care costs. A small portion of patients consume most health care resources and costs. More intensive patient management strategies, such as case management, are usually more effective at improving health outcomes, but are also more expensive. To use limited resources efficiently, risk stratification is commonly used in managing patients with chronic diseases, such as asthma, chronic obstructive pulmonary disease, diabetes, and heart disease. Patients are stratified based on predicted risk with patients at higher risk given more intensive care. The current risk-stratified patient management approach has 3 limitations resulting in many patients not receiving the most appropriate care, unnecessarily increased costs, and suboptimal health outcomes. First, using predictive models for health outcomes and costs is currently the best method for forecasting individual patient's risk. Yet, accuracy of predictive models remains poor causing many patients to be misstratified. If an existing model were used to identify candidate patients for case management, enrollment would miss more than half of those who would benefit most, but include others unlikely to benefit, wasting limited resources. Existing models have been developed under the assumption that patient characteristics primarily influence outcomes and costs, leaving physician characteristics out of the models. In reality, both characteristics have an impact. Second, existing models usually give neither an explanation why a particular patient is predicted to be at high risk nor suggestions on interventions tailored to the patient's specific case. As a result, many high-risk patients miss some suitable interventions. Third, thresholds for risk strata are suboptimal and determined heuristically with no quality guarantee. OBJECTIVE: The purpose of this study is to improve risk-stratified patient management so that more patients will receive the most appropriate care. METHODS: This study will (1) combine patient, physician profile, and environmental variable features to improve prediction accuracy of individual patient health outcomes and costs; (2) develop the first algorithm to explain prediction results and suggest tailored interventions; (3) develop the first algorithm to compute optimal thresholds for risk strata; and (4) conduct simulations to estimate outcomes of risk-stratified patient management for various configurations. The proposed techniques will be demonstrated on a test case of asthma patients. RESULTS: We are currently in the process of extracting clinical and administrative data from an integrated health care system's enterprise data warehouse. We plan to complete this study in approximately 5 years. CONCLUSIONS: Methods developed in this study will help transform risk-stratified patient management for better clinical outcomes, higher patient satisfaction and quality of life, reduced health care use, and lower costs.

Improving Continuity of Care via the Discharge Summary.

Discharge summaries (DCS) frequently fail to improve the continuity of care. A chart review of 188 DCS was performed to identify specific components that could be improved through health information technology. Medication reconciliations were analyzed for completeness and for medical reasoning. Documentation of pending results and follow-up details were analyzed. Patient preferences, patient goals, and the handover tone were noted. Patients were discharged on an average of 9.8 medications, only 3% of medication reconciliations were complete and medical reasoning was frequently absent. There were 358 pending results in 188 hospital discharges though only 14% were mentioned in the DCS. Documentation of clear, timely follow-up was present for less than 50% of patients. Patient preferences, patient goals, and lessons learned were rarely included. A handover tone was in only 17% of the DCS. Evaluating the DCS as a clinical handover is novel but information for safe handovers is frequently missing.

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

BACKGROUND: The effects of electronic health records (EHRs) on doctor-patient communication are unclear. OBJECTIVE: To evaluate the effects of EHR use compared with paper chart use, on novice physicians' communication skills. DESIGN: Within-subjects randomized controlled trial using observed structured clinical examination methods to assess the impact of use of an EHR on communication. SETTING: A large academic internal medicine training program. POPULATION: First-year internal medicine residents. INTERVENTION: Residents interviewed, diagnosed, and initiated treatment of simulated patients using a paper chart or an EHR on a laptop computer. Video recordings of interviews were rated by three trained observers using the Four Habits scale. RESULTS: Thirty-two residents completed the study and had data available for review (61.5% of those enrolled in the residency program). In most skill areas in the Four Habits model, residents performed at least as well using the EHR and were statistically better in six of 23 skills areas (p<0.05). The overall average communication score was better when using an EHR: mean difference 0.254 (95% CI 0.05 to 0.45), p = 0.012, Cohen's d of 0.47 (a moderate effect). Residents scoring poorly (>3 average score) with paper methods (n = 8) had clinically important improvement when using the EHR. LIMITATIONS: This study was conducted in first-year residents in a training environment using simulated patients at a single institution. CONCLUSIONS: Use of an EHR on a laptop computer appears to improve the ability of first-year residents to communicate with patients relative to using a paper chart.

Rethinking the discharge summary: a focus on handoff communication.

The discharge summary is one of the most critical documents in medical care settings, but it is prone to systematic lapses that compromise the continuity of care. Discontinuity is fostered not only by incomplete inclusion of data (such as pending labs or medication reconciliations) but also by failure to document clinical reasoning and unfinished diagnostic workups. To correct these problems, the authors propose the Situation-Background-Assessment-Recommendations (SBAR) format for discharge summaries. SBAR is already used for handoffs the way Subjective-Objective-Assessment-Plan is for progress notes. The SBAR format supports the concise presentation of relevant information along with guidance for action. It shifts the paradigm and purpose of the discharge summary away from being a "Captain's Log" (a historical record of the events, actions taken, and their consequences during hospitalization) and towards being a handoff document (a tool for communication between health professionals aimed at ensuring continuity of care). To test SBAR as a template for discharge summaries, the authors have initiated a study to document the impact of the SBAR model on the quality of trainees' thinking in discharge summaries.

Two-photon confocal microscopy: a nondestructive method for studying wound healing.

Two-photon confocal microscopy is a new technology useful in nondestructive analysis of tissue. The pattern generated from laser-excited autofluorescence and second harmonic signals can be analyzed to construct a three-dimensional, microanatomical, structural image. The healing of full-thickness guinea pig skin wounds was studied over a period of 28 days using two-photon confocal microscopy. Three-dimensional data were rendered from two-dimensional images and compared with conventional, en face, histologic sections. Two-photon confocal microscopy images show resolution of muscle, fascia fibers, collagen fibers, inflammatory cells, blood vessels, and hair. Although these images do not currently have the resolution of standard histology, the ability to noninvasively acquire three-dimensional images of skin promises to be an important tool in wound-healing studies.