Endorsement, Prior Action, and Language: Modeling Trusted Advice in Computerized Clinical Alerts

The safe prescribing of medications via computerized physician order entry routinely relies on clinical alerts. Alert compliance, however, remains surprisingly low, with up to 95% often ignored. Prior approaches, such as improving presentational factors in alert design, had limited success, mainly due to physicians' lack of trust in computerized advice. While designing trustworthy alert is key, actionable design principles to embody elements of trust in alerts remain little explored. To mitigate this gap, we introduce a model to guide the design of trust-based clinical alerts-based on what physicians value when trusting advice from peers in clinical activities. We discuss three key dimensions to craft trusted alerts: using colleagues' endorsement, foregrounding physicians' prior actions, and adopting a suitable language. We exemplify our approach with emerging alert designs from our ongoing research with physicians and contribute to the current debate on how to design effective alerts to improve patient safety

The Indiana Learning Health System Initiative: Early experience developing a collaborative, regional learning health system

Introduction Learning health systems (LHSs) are usually created and maintained by single institutions or healthcare systems. The Indiana Learning Health System Initiative (ILHSI) is a new multi-institutional, collaborative regional LHS initiative led by the Regenstrief Institute (RI) and developed in partnership with five additional organizations: two Indiana-based health systems, two schools at Indiana University, and our state-wide health information exchange. We report our experiences and lessons learned during the initial 2-year phase of developing and implementing the ILHSI. Methods The initial goals of the ILHSI were to instantiate the concept, establish partnerships, and perform LHS pilot projects to inform expansion. We established shared governance and technical capabilities, conducted a literature review-based and regional environmental scan, and convened key stakeholders to iteratively identify focus areas, and select and implement six initial joint projects. Results The ILHSI successfully collaborated with its partner organizations to establish a foundational governance structure, set goals and strategies, and prioritize projects and training activities. We developed and deployed strategies to effectively use health system and regional HIE infrastructure and minimize information silos, a frequent challenge for multi-organizational LHSs. Successful projects were diverse and included deploying a Fast Healthcare Interoperability Standards (FHIR)-based tool across emergency departments state-wide, analyzing free-text elements of cross-hospital surveys, and developing models to provide clinical decision support based on clinical and social determinants of health. We also experienced organizational challenges, including changes in key leadership personnel and varying levels of engagement with health system partners, which impacted initial ILHSI efforts and structures. Reflecting on these early experiences, we identified lessons learned and next steps. Conclusions Multi-organizational LHSs can be challenging to develop but present the opportunity to leverage learning across multiple organizations and systems to benefit the general population. Attention to governance decisions, shared goal setting and monitoring, and careful selection of projects are important for early success

Local Foundations and Medical Research Support in Indianapolis after 1945

Indiana University-Purdue University Indianapolis (IUPUI)Philanthropy plays an important and often publicly visible role in modern medicine. Names like Carnegie, Rockefeller, and Gates are associated with medicine both personally and through the foundations they created. This phenomenon also played out on a local level, where communities are dotted with hospitals, university laboratories, and medical schools bearing the names of families who contributed to build, literally and figuratively, the institutions of medical research. Little is known about these local philanthropists, including why they decided to support research and how they organized and carried out the work of grantmaking. Consequently, there is no deep understanding of the value of their contributions. I seek to remedy that omission through this study of the history and work of three small foundations dedicated to medical and scientific research and located in a single, midsized American city. Ultimately this work considers a question fundamental to medical research philanthropy: Can smaller foundations make a meaningful contribution to modern medical research given the scale, complexity, and cost of the work as well as the dominance of federal government funding? This work concludes that the primary value of the foundations under study was not their financial support for research per se, but their flexible and sustained contributions to the local research infrastructure, including philanthropic investments that helped launch research projects and the careers of individual scientists; provided capital for needed physical space; and supported recruiting efforts to bring innovative and productive faculty members to staff new research and patient care departments. The foundations in this study, both individually and collectively, served as valuable strategic allies to the research institutions in their community. As a result, the foundations contributed directly and meaningfully toward the expansion and improvement of the research institutions. The resulting growth in the size and reputation of these programs and facilities generated economic gain that benefitted the broader community. This finding supports a call for the development of a more nuanced and complete understanding of the potential impact that smaller funders can have in a large and complicated system

Understanding Advice Sharing among Physicians: Towards Trust-Based Clinical Alerts

Safe prescribing of medications relies on drug safety alerts, but up to 96% of such warnings are ignored by physicians. Prior research has proposed improvements to the design of alerts, but with limited increase in adherence. We propose a different perspective: before re-designing alerts, we focus on improving the trust between physicians and computerized advice by examining why physicians trust their medical colleagues. To understand trusted advice among physicians, we conducted three contextual inquiries in a hospital setting (22 participants), and corroborated our findings with a survey (37 participants). Drivers that guide physicians in trusting peer advice include: timeliness of the advice, collaborative language, empathy, level of specialization and medical hierarchy. Based on these findings, we introduce seven design directions for trust-based alerts: endorsement, transparency, team sensing, collaborative, empathic, conflict mitigating and agency laden. Our work contributes to novel alert design strategies to improve the effectiveness of drug safety advice

Predictive Modeling of Hypoglycemia for Clinical Decision Support in Evaluating Outpatients with Diabetes Mellitus

Objective: Hypoglycemia occurs in 20–60% of patients with diabetes mellitus. Identifying at-risk patients can facilitate interventions to lower risk. We sought to develop a hypoglycemia prediction model. Methods: In this retrospective cohort study, urban adults prescribed a diabetes drug between 2004 and 2013 were identified. Demographic and clinical data were extracted from an electronic medical record (EMR). Laboratory tests, diagnostic codes and natural language processing (NLP) identified hypoglycemia. We compared multiple logistic regression, classification and regression trees (CART), and random forest. Models were evaluated on an independent test set or through cross-validation. Results: The 38,780 patients had mean age 57 years; 56% were female, 40% African-American and 39% uninsured. Hypoglycemia occurred in 8128 (539 identified only by NLP). In logistic regression, factors positively associated with hypoglycemia included infection, non-long-acting insulin, dementia and recent hypoglycemia. Negatively associated factors included long-acting insulin plus sulfonylurea, and age 75 or older. The models’ area under curve was similar (logistic regression, 89%; CART, 88%; random forest, 90%, with ten-fold cross-validation). Conclusions: NLP improved identification of hypoglycemia. Non-long-acting insulin was an important risk factor. Decreased risk with age may reflect treatment or diminished awareness of hypoglycemia. More complex models did not improve prediction

The use of clinical, behavioral, and social determinants of health to improve identification of patients in need of advanced care for depression

Indiana University-Purdue University Indianapolis (IUPUI)Depression is the most commonly occurring mental illness the world over. It poses a significant health and economic burden across the individual and community. Not all occurrences of depression require the same level of treatment. However, identifying patients in need of advanced care has been challenging and presents a significant bottleneck in providing care. We developed a knowledge-driven depression taxonomy comprised of features representing clinical, behavioral, and social determinants of health (SDH) that inform the onset, progression, and outcome of depression. We leveraged the depression taxonomy to build decision models that predicted need for referrals across: (a) the overall patient population and (b) various high-risk populations. Decision models were built using longitudinal, clinical, and behavioral data extracted from a population of 84,317 patients seeking care at Eskenazi Health of Indianapolis, Indiana. Each decision model yielded significantly high predictive performance. However, models predicting need of treatment across high-risk populations (ROC’s of 86.31% to 94.42%) outperformed models representing the overall patient population (ROC of 78.87%). Next, we assessed the value of adding SDH into each model. For each patient population under study, we built additional decision models that incorporated a wide range of patient and aggregate-level SDH and compared their performance against the original models. Models that incorporated SDH yielded high predictive performance. However, use of SDH did not yield statistically significant performance improvements. Our efforts present significant potential to identify patients in need of advanced care using a limited number of clinical and behavioral features. However, we found no benefit to incorporating additional SDH into these models. Our methods can also be applied across other datasets in response to a wide variety of healthcare challenges

Electronic Medical Records: A Case Study to Improve Patient Safety at Royal Victoria Teaching Hospital

Most countries in Europe and the USA are increasingly using an electronic medical record (EMR) system to help improve healthcare quality. Unfortunately, The Gambia government faces a series of health crises including but not limited to HIV/AIDS, malaria, diabetes and tuberculosis. These diseases threaten the lives of thousands of people. Lack of infrastructure and trained, experienced staff are considered important barriers to scaling up treatment for these diseases. The contribution of this field project outlines the benefits of an EMR system at Royal Victoria Teaching Hospital (RVTH) and how it will improve patient safety. This is a descriptive study using interview questionnaires from officials at the Royal Victoria Teaching Hospital. The study also looks into other facilities in similar developing countries with advanced systems, but not so advanced as to be at the level of state-of-the-art facilities in the U.S. Results from this study indicates the importance of an EMR system at RVTH to facilitate effective and efficient data collection, data entry, information retrieval and report generation. As a catalyst for development, the implementation of an EMR system at RVTH may make it one on the best hospitals in the West African region

A randomized study on the usefulness of an electronic outpatient hypoglycemia risk calculator for clinicians of patients with diabetes in a safety-net institution

Objective: Hypoglycemia (HG) occurs in up to 60% of patients with diabetes mellitus (DM) each year. We assessed a HG alert tool in an electronic health record system, and determined its effect on clinical practice and outcomes. Methods: The tool applied a statistical model, yielding patient-specific information about HG risk. We randomized outpatient primary-care providers (PCPs) to see or not see the alerts. Patients were assigned to study group according to the first PCP seen during four months. We assessed prescriptions, testing, and HG. Variables were compared by multinomial, logistic, or linear model. ClinicalTrials.gov ID: NCT04177147 (registered on 22 November 2019). Results: Patients (N = 3350) visited 123 intervention PCPs; 3395 patients visited 220 control PCPs. Intervention PCPs were shown 18,645 alerts (mean of 152 per PCP). Patients’ mean age was 55 years, with 61% female, 49% black, and 49% Medicaid recipients. Mean baseline A1c and body mass index were similar between groups. During follow-up, the number of A1c and glucose tests, and number of new, refilled, changed, or discontinued insulin prescriptions, were highest for patients with highest risk. Per 100 patients on average, the intervention group had fewer sulfonylurea refills (6 vs. 8; p < .05) and outpatient encounters (470 vs. 502; p < .05), though the change in encounters was not significant. Frequency of HG events was unchanged. Conclusions: Informing PCPs about risk of HG led to fewer sulfonylurea refills and visits. Longer-term studies are needed to assess potential for long-term benefits

Clinical foundations and information architecture for the implementation of a federated health record service

Clinical care increasingly requires healthcare professionals to access patient record information that may be distributed across multiple sites, held in a variety of paper and electronic formats, and represented as mixtures of narrative, structured, coded and multi-media entries. A longitudinal person-centred electronic health record (EHR) is a much-anticipated solution to this problem, but its realisation is proving to be a long and complex journey. This Thesis explores the history and evolution of clinical information systems, and establishes a set of clinical and ethico-legal requirements for a generic EHR server. A federation approach (FHR) to harmonising distributed heterogeneous electronic clinical databases is advocated as the basis for meeting these requirements. A set of information models and middleware services, needed to implement a Federated Health Record server, are then described, thereby supporting access by clinical applications to a distributed set of feeder systems holding patient record information. The overall information architecture thus defined provides a generic means of combining such feeder system data to create a virtual electronic health record. Active collaboration in a wide range of clinical contexts, across the whole of Europe, has been central to the evolution of the approach taken. A federated health record server based on this architecture has been implemented by the author and colleagues and deployed in a live clinical environment in the Department of Cardiovascular Medicine at the Whittington Hospital in North London. This implementation experience has fed back into the conceptual development of the approach and has provided "proof-of-concept" verification of its completeness and practical utility. This research has benefited from collaboration with a wide range of healthcare sites, informatics organisations and industry across Europe though several EU Health Telematics projects: GEHR, Synapses, EHCR-SupA, SynEx, Medicate and 6WINIT. The information models published here have been placed in the public domain and have substantially contributed to two generations of CEN health informatics standards, including CEN TC/251 ENV 13606

Tool for populating eXtensible Markup Language documents with UMLS concept unique identifiers of current medications

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.Includes bibliographical references (p. 113-117).TagMeds is a system that recognizes and marks textual descriptions of a patient's current medications in the unstructured textual content of consultations letters. Medications are found based on their names and on linguistic patterns describing their dose, form of administration, etc. The UMLS is used as the underlying database of terms, and detected medications are encoded into XML tags consistent with and making use of the Health Level 7 (HL7) Clinical Document Architecture. The specific aims of this research are: (1) to review the literature in order to determine the state of the art in tagging free text for search and utilization, (2) to construct a tool that will reliably generate UMLS Concept Unique Identifier tags of current medications within free text. The methods involved are: (1) creating Perl procedures to recognize patterns in free text to retrieve the UMLS Concept Unique Identifiers and to insert these unique identifiers into XML tagging of the text and (2) statistical analysis of the use of TagMeds on a data base of consultation letters from the Endocrinology Clinic of the Children's Hospital of Boston as compared to manual markup by a group of physicians. The performance of an NLP system is found to be at least as sensitive as the performance of physicians in the extraction of current medications and their attributes. The tagged current medication information has the potential to support a personal electronic medical record system, such as PING. Additional development of TagMeds is likely to bring significant improvements, with modest expenditure of time and effort. TagMeds demonstrates that great utility can be achieved with a medical natural language processing system using simple and unsophisticated techniques.by Andrew S. Nakrin.S.M