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With widespread electronic health record (EHR) adoption and improvements in health information interoperability in the United States, troves of data are available for knowledge discovery. Several data sharing programs and tools have been developed to support research activities, including efforts funded by the National Institutes of Health (NIH), EHR vendors, and other public- and private-sector entities. We surveyed 65 leading research institutions (77% response rate) about their use of and value derived from ten programs/tools, including NIH's Accrual to Clinical Trials, Epic Corporation's Cosmos, and the Observational Health Data Sciences and Informatics consortium. Most institutions participated in multiple programs/tools but reported relatively low usage (even when they participated, they frequently indicated that fewer than one individual/month benefitted from the platform to support research activities). Our findings suggest that investments in research data sharing have not yet achieved desired results., (©2023 AMIA - All rights reserved.)

Structured electronic health record (EHR) data are often used for quality measurement and improvement, clinical research, and other secondary uses. These data, however, are known to suffer from quality problems. There may be value in augmenting structured EHR data to improve data quality, thereby improving the reliability and validity of the conclusions drawn from those data. Focusing on five diagnoses related to cardiovascular care, this paper considers the added value of two alternative data sources: manual chart abstraction and patient self-report. We assess the overall agreement between structured EHR problem list data, abstracted EHR data, and patient self- report; and explore possible causes of disagreement between those sources. Our findings suggest that both chart abstraction and patient self-report contain significantly more diagnoses than the problem list, but that the information they capture is different. Methods for collecting and validating self-reported medical data require further consideration and exploration., (©2019 AMIA - All rights reserved.)

Clinical quality measures (CQMs) aim to identify gaps in care and to promote evidence-based guidelines. Official CQM definitions consist of a measure's logic and grouped, standardized codes to define key concepts. In this study, we used the official CQM update process to understand how CQMs' meanings change over time. First, we identified differences between the narrative description, logic, and the vocabulary specifications offour standardized CQMs' definitions in subsequent versions (2015, 2016, and 2017). Next, we implemented the various versions in a quality measure calculation registry to understand how the differences affected calculated prevalence of risk and measure performance. Global performance rates changed up to 5.32%, and an increase of up to 28% new patients was observed for key conditions between versions. Updates to definitions that change a measure's logic and choices to include/exclude codes in value set vocabularies changes measurement of quality and likely introduces variation by implementation.

Clinical quality measures (CQMs) are important tools for the assessment and improvement of health care quality. Federal requirements initially set forth in the American Recovery and Reinvestment Act, and advanced in subsequent stages of the requirements, codified electronic health record (EHR)-based CQM reporting, and have made automated CQM implementation a priority amongst the clinical and informatics communities. Nevertheless, the processes surrounding CQM implementation and validation remain complex, time-consuming, and largely undefined. We collected issue-tracking data during the course of an agile and rigorous collaborative project to build an analytics platform for the Knight Cardiovascular Institute at OHSU, with nine heart failure CQMs defined by the American College of Cardiology (ACC) as an exemplar. Using a mixed methods approach we provide an overview of our CQM implementation and validation process, identify major roadblocks and bottlenecks, and make recommendations for other professionals working in the area of health care quality assessment and improvement.

Objective: Examine how the Electronic Health Record (EHR) and its related systems support or inhibit provider collaboration., Background: Health care systems in the US are simultaneously implementing EHRs and transitioning to more collaborative delivery systems; this study examines the interaction between these two changes., Methods: This qualitative study of five US EHR implementations included 49 interviews and over 60 hours of provider observation. We examined the role of the EHR in building relationships, communicating, coordinating, and collaborative decision-making., Results: The EHR plays four roles in collaboration: a repository, a messenger, an orchestrator, and a monitor. While EHR performance varied, common themes were decreased trust due to poor quality documentation, incomplete communication, potential for increased effectiveness through better coordination, and the emerging role of the EHR in identifying performance gaps., Conclusion: Both organizational and technical innovations are needed if the EHR is to truly support collaborative behaviors.

Care coordination is at the forefront of current health reform efforts, yet most electronic health records (EHRs) lack the functionality needed to facilitate and document care coordination activities. The Integrated Care Coordination Information System (ICCIS) was iteratively developed with user input to meet these needs. Following 16 months of system use, ICCIS users and developers were interviewed about their experiences. These interviews, along with quantitative information about system use, were analyzed using a combination of Linstone's Multiple Perspective approach and the ABC framework to determine lessons learned about novel system creation. Overall, clinicians saw value in specialized health information technology (HIT) tools for care coordination as long as development focuses on providing user-requested functionality that integrates closely with existing HIT systems and workflows. Close integration between novel HIT and EHRs may increase use by relieving the cited fatigues of duplicative data entry, multiple system logins, and potential data inconsistencies.

Quality measurement has been slow to make a major impact in health care. Initial measures were too simple to affect outcomes of importance. Incentive programs such as Meaningful Use encourage better measures, but in process may become more complex. We evaluated the measures selected for Meaningful Use in two ways: we counted unique concept identifiers, taxonomies, and aggregated concepts as measures of complexity; and we surveyed informatics professionals to assess difficulty. There were 20,316 unique concept identifiers, 35 taxonomies, and 317 aggregated concepts across the 45 measures. Half the respondents reported measures at least moderately difficult. The number of identifiers was associated with fewer implementations (r=-.37); rating-of-difficulty was associated with more taxonomies (r=.24). The impact on accuracy may be substantial when moving to measures intended to be more relevant to clinical outcomes but requiring the use of more taxonomies, unused structured concept identifiers, or concepts only in free text fields.

We present the User Need Analysis process for the Integrated Care Coordination Information System (ICCIS) research project at Oregon Health & Science University (OHSU). The primary goal of the process was to select a mix of methodologies to perform user need analysis which would lead to generation of system requirements. The ICCIS Team developers would use these prioritized requirements to build the next generation of the ICCIS software that would help meet the overall goals of the care model. First, semi-structured User Discussion Guides and structured questioners were used to collect raw needs from Physicians, Nurses, IT and Administrative personal. Later Conjoint Analysis (CA) and Dimensional Analysis (DA) were used to generate emerging concepts and priorities from the discussions with the end-user community.

We present a model for architectural strategies and issues regarding health information exchange (HIE). This model is a continuum of options, between separated systems and a monolithic approach. We discuss characteristics, examples and potential impacts of each approach, based on generalized observations of practical implementations. The continuum model allows for approaches that mature from an initial implementation to a long-term strategy.

Unmet information needs of physicians and patients are common, but those of nurse care managers - defined as collaborative care planners for with chronic conditions - are less well understood. We taped and transcribed daily activities and conducted semi-structured interviews of 7 care managers, and analyzed questions elicited through this work through a variety of frameworks.

Advanced clinical information systems have been proposed to improve patient care in terms of safety, effectiveness, and efficiency. In order to be effective, such systems require detailed patient-specific clinical information in a form easily reviewed by clinicians. We have developed a patient summary worksheet for use in outpatient clinics, which presents a structured overview of patient health information. The worksheet provides patient demographic information, specific problems and conditions, the patient's current medication profile, laboratory test results pertinent to patient problems, and disease-specific or preventive care actionable advisories. Usage has grown from a few hundred to over 25,000 unique patients per month during a two-year period. Diabetic patients for whom the worksheet is accessed are significantly more likely to be in compliance with accepted testing regimens for glycosolated hemoglobin (OR 1.47, 95% CI 1.28, 1.61).

Prior to the implementation of Electronic Medical Record (EMR) web access for patients at a large integrated delivery systems, we surveyed physicians? attitudes. Our web based questionnaire revealed largely positive attitudes about access. The exceptions included abnormal reports, progress notes, and e-care. A factor analysis identified the group of physicians who didn?t view patients as partners felt most negative about the process.