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BackgroundMedicine 2.0—the adoption of Web 2.0 technologies such as social networks in health care—creates the need for apps that can find other patients with similar experiences and health conditions based on a patient’s electronic health record (EHR). Concurrently, there is an increasing number of longitudinal EHR data sets with rich information, which are essential to fulfill this need. ObjectiveThis study aimed to evaluate the hypothesis that we can leverage similar EHRs to predict possible future medical concepts (eg, disorders) from a patient’s EHR. MethodsWe represented patients’ EHRs using time-based prefixes and suffixes, where each prefix or suffix is a set of medical concepts from a medical ontology. We compared the prefixes of other patients in the collection with the state of the current patient using various interpatient distance measures. The set of similar prefixes yields a set of suffixes, which we used to determine probable future concepts for the current patient’s EHR. ResultsWe evaluated our methods on the Multiparameter Intelligent Monitoring in Intensive Care II data set of patients, where we achieved precision up to 56.1% and recall up to 69.5%. For a limited set of clinically interesting concepts, specifically a set of procedures, we found that 86.9% (353/406) of the true-positives are clinically useful, that is, these procedures were actually performed later on the patient, and only 4.7% (19/406) of true-positives were completely irrelevant. ConclusionsThese initial results indicate that predicting patients’ future medical concepts is feasible. Effectively predicting medical concepts can have several applications, such as managing resources in a hospital.

BackgroundInfection prevention (IP) measures are designed to mitigate the transmission of pathogens in healthcare. Using large-scale viral genomic and social network analyses, we determined if IP measures used during the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic were adequate in protecting healthcare workers (HCWs) and patients from acquiring SARS-CoV-2.MethodsWe performed retrospective cross-sectional analyses of viral genomics from all available SARS-CoV-2 viral samples collected at UC San Diego Health and social network analysis using the electronic medical record to derive temporospatial overlap of infections among related viromes and supplemented with contact tracing data. The outcome measure was any instance of healthcare transmission, defined as cases with closely related viral genomes and epidemiological connection within the healthcare setting during the infection window. Between November 2020 through January 2022, 12 933 viral genomes were obtained from 35 666 patients and HCWs.ResultsAmong 5112 SARS-CoV-2 viral samples sequenced from the second and third waves of SARS-CoV-2 (pre-Omicron), 291 pairs were derived from persons with a plausible healthcare overlap. Of these, 34 pairs (12%) were phylogenetically linked: 19 attributable to household and 14 to healthcare transmission. During the Omicron wave, 2106 contact pairs among 7821 sequences resulted in 120 (6%) related pairs among 32 clusters, of which 10 were consistent with healthcare transmission. Transmission was more likely to occur in shared spaces in the older hospital compared with the newer hospital (2.54 vs 0.63 transmission events per 1000 admissions, P < .001).ConclusionsIP strategies were effective at identifying and preventing healthcare SARS-CoV-2 transmission.

ImportanceProcedural proficiency is a core competency for graduate medical education; however, procedural reporting often relies on manual workflows that are duplicative and generate data whose validity and accuracy are difficult to assess. Failure to accurately gather these data can impede learner progression, delay procedures, and negatively impact patient safety.ObjectiveTo examine accuracy and procedure logging completeness of a system that extracts procedural data from an electronic health record system and uploads these data securely to an application used by many residency programs for accreditation.Design, setting, and participantsThis quality improvement study of all emergency medicine resident physicians at University of California, San Diego Health was performed from May 23, 2023, to June 25, 2023.ExposuresAutomated system for procedure data extraction and upload to a residency management software application.Main outcomes and measuresThe number of procedures captured by the automated system when running silently compared with manually logged procedures in the same timeframe, as well as accuracy of the data upload.ResultsForty-seven residents participated in the initial silent assessment of the extraction component of the system. During a 1-year period (May 23, 2022, to May 7, 2023), 4291 procedures were manually logged by residents, compared with 7617 procedures captured by the automated system during the same period, representing a 78% increase. During assessment of the upload component of the system (May 8, 2023, to June 25, 2023), a total of 1353 procedures and patient encounters were evaluated, with the system operating with a sensitivity of 97.4%, specificity of 100%, and overall accuracy of 99.5%.Conclusions and relevanceIn this quality improvement study of emergency medicine resident physicians, an automated system demonstrated that reliance on self-reported procedure logging resulted in significant procedural underreporting compared with the use of data obtained at the point of performance. Additionally, this system afforded a degree of reliability and validity heretofore absent from the usual after-the-fact procedure logging workflows while using a novel application programming interface-based approach. To our knowledge, this system constitutes the first generalizable implementation of an automated solution to a problem that has existed in graduate medical education for decades.

IntroductionElectronic referral (e-referral) to quitlines helps connect tobacco-using patients to free, evidence-based cessation counseling. Little has been published about the real-world implementation of e-referrals across U.S. health systems, their maintenance over time, and the outcomes of e-referred patients.Aims and methodsBeginning in 2014, the University of California (UC)-wide project called UC Quits scaled up quitline e-referrals and related modifications to clinical workflows from one to five UC health systems. Implementation strategies were used to increase site readiness. Maintenance was supported through ongoing monitoring and quality improvement programs. Data on e-referred patients (n = 20 709) and quitline callers (n = 197 377) were collected from April 2014 to March 2021. Analyses of referral trends and cessation outcomes were conducted in 2021-2022.ResultsOf 20 709 patients referred, the quitline contacted 47.1%, 20.6% completed intake, 15.2% requested counseling, and 10.9% received it. In the 1.5-year implementation phase, 1813 patients were referred. In the 5.5-year maintenance phase, volume was sustained, with 3436 referrals annually on average. Among referred patients completing intake (n = 4264), 46.2% were nonwhite, 58.8% had Medicaid, 58.7% had a chronic disease, and 48.8% had a behavioral health condition. In a sample randomly selected for follow-up, e-referred patients were as likely as general quitline callers to attempt quitting (68.5% vs. 71.4%; p = .23), quit for 30 days (28.3% vs. 26.9%; p = .52), and quit for 6 months (13.6% vs. 13.9%; p = .88).ConclusionsWith a whole-systems approach, quitline e-referrals can be established and sustained across inpatient and outpatient settings with diverse patient populations. Cessation outcomes were similar to those of general quitline callers.ImplicationsThis study supports the broad implementation of tobacco quitline e-referrals in health care. To the best of our knowledge, no other paper has described the implementation of e-referrals across multiple U.S. health systems or how they were sustained over time. Modifying electronic health records systems and clinical workflows to enable and encourage e-referrals, if implemented and maintained appropriately, can be expected to improve patient care, make it easier for clinicians to support patients in quitting, increase the proportion of patients using evidence-based treatment, provide data to assess progress on quality goals, and help meet reporting requirements for tobacco screening and prevention.

ObjectiveWe aimed to develop a distributed, immutable, and highly available cross-cloud blockchain system to facilitate federated data analysis activities among multiple institutions.Materials and methodsWe preprocessed 9166 COVID-19 Structured Query Language (SQL) code, summary statistics, and user activity logs, from the GitHub repository of the Reliable Response Data Discovery for COVID-19 (R2D2) Consortium. The repository collected local summary statistics from participating institutions and aggregated the global result to a COVID-19-related clinical query, previously posted by clinicians on a website. We developed both on-chain and off-chain components to store/query these activity logs and their associated queries/results on a blockchain for immutability, transparency, and high availability of research communication. We measured run-time efficiency of contract deployment, network transactions, and confirmed the accuracy of recorded logs compared to a centralized baseline solution.ResultsThe smart contract deployment took 4.5 s on an average. The time to record an activity log on blockchain was slightly over 2 s, versus 5-9 s for baseline. For querying, each query took on an average less than 0.4 s on blockchain, versus around 2.1 s for baseline.DiscussionThe low deployment, recording, and querying times confirm the feasibility of our cross-cloud, blockchain-based federated data analysis system. We have yet to evaluate the system on a larger network with multiple nodes per cloud, to consider how to accommodate a surge in activities, and to investigate methods to lower querying time as the blockchain grows.ConclusionBlockchain technology can be used to support federated data analysis among multiple institutions.

Patient care in intensive care environments is complex, time-sensitive, and data-rich, factors that make these settings particularly well-suited to clinical decision support (CDS). A wide range of CDS interventions have been used in intensive care unit environments. The field needs well-designed studies to identify the most effective CDS approaches. Evolving artificial intelligence and machine learning models may reduce information-overload and enable teams to take better advantage of the large volume of patient data available to them. It is vital to effectively integrate new CDS into clinical workflows and to align closely with the cognitive processes of frontline clinicians.

IntroductionPatient outcome prediction models are underused in clinical practice because of lack of integration with real-time patient data. The electronic health record (EHR) has the ability to use machine learning (ML) to develop predictive models. While an EHR ML model has been developed to predict clinical deterioration, it has yet to be validated for use in trauma. We hypothesized that the Epic Deterioration Index (EDI) would predict mortality and unplanned intensive care unit (ICU) admission in trauma patients.MethodsA retrospective analysis of a trauma registry was used to identify patients admitted to a level 1 trauma center for >24 hours from October 2019 to July 2020. We evaluated the performance of the EDI, which is constructed from 125 objective patient measures within the EHR, in predicting mortality and unplanned ICU admissions. We performed a 5 to 1 match on age because it is a major component of EDI, then examined the area under the receiver operating characteristic curve (AUROC), and benchmarked it against Injury Severity Score (ISS) and new injury severity score (NISS).ResultsThe study cohort consisted of 1,325 patients admitted with a mean age of 52.5 years and 91% following blunt injury. The in-hospital mortality rate was 2%, and unplanned ICU admission rate was 2.6%. In predicting mortality, the maximum EDI within 24 hours of admission had an AUROC of 0.98 compared with 0.89 of ISS and 0.91 of NISS. For unplanned ICU admission, the EDI slope within 24 hours of ICU admission had a modest performance with an AUROC of 0.66.ConclusionEpic Deterioration Index appears to perform strongly in predicting in-patient mortality similarly to ISS and NISS. In addition, it can be used to predict unplanned ICU admissions. This study helps validate the use of this real-time EHR ML-based tool, suggesting that EDI should be incorporated into the daily care of trauma patients.Level of evidencePrognostic, level III.

BACKGROUND: Diagnostic errors are a major source of preventable harm but the science of reducing them remains underdeveloped. OBJECTIVE: To identify and prioritize research questions to advance the field of diagnostic safety in the next 5 years. PARTICIPANTS: Ninety-seven researchers and 42 stakeholders were involved in the identification of the research priorities. DESIGN: We used systematic prioritization methods based on the Child Health and Nutrition Research Initiative (CHNRI) methodology. We first invited a large international group of expert researchers in various disciplines to submit research questions while considering five prioritization criteria: (1) usefulness, (2) answerability, (3) effectiveness, (4) potential for translation, and (5) maximal potential for effect on diagnostic safety. After consolidation, these questions were prioritized at an in-person expert meeting in April 2019. Top-ranked questions were subsequently reprioritized through scoring on the five prioritization criteria using an online questionnaire. We also invited non-research stakeholders to assign weights to the five criteria and then used these weights to adjust the final prioritization score for each question. KEY RESULTS: Of the 207 invited researchers, 97 researchers responded and 78 submitted 333 research questions which were then consolidated. Expert meeting participants (n = 21) discussed questions in different breakout sessions and prioritized 50, which were subsequently reduced to the top 20 using the online questionnaire. The top 20 questions addressed mostly system factors (e.g., implementation and evaluation of information technologies), teamwork factors (e.g., role of nurses and other health professionals in the diagnostic process), and strategies to engage patients in the diagnostic process. CONCLUSIONS: Top research priorities for advancing diagnostic safety in the short-term include strengthening systems and teams and engaging patients to support diagnosis. High-priority areas identified using these systematic methods can inform an actionable research agenda for reducing preventable diagnostic harm.

ObjectiveTo utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online.Materials and methodsWe developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data.ResultsOur public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems.Discussion and conclusionsWe present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems.

BACKGROUND: Computerized insulin infusion protocols have demonstrated higher staff satisfaction, better compliance with protocols, and increased time with glucose in range compared to paper protocols. At University of California San Diego Health (UCSDH), we implemented an insulin infusion computer calculator (IICC) and transitioned it from a web-based platform directly into the electronic medication administration record (eMAR) of our primary electronic health record (EHR). METHODS: This is a retrospective analysis of 6306 adult patients at UCSDH receiving intravenous (IV) insulin infusion from March 7, 2013 to May 30, 2019. We created three periods of the study-(1) the pre-eMAR integration period; (2) the eMAR integration period; and (3) the post-eMAR integration period-and looked at the percentage of readings within goal range (90-150 mg/dL for intensive care unit [ICU], 90-180 mg/dL for non-ICU) in patients with and without hyperglycemic emergencies. As our safety endpoints, we elected to look at incidence of blood glucose (BG) readings

OBJECTIVE: Clinical decision support (CDS) can contribute to quality and safety. Prior work has shown that errors in CDS systems are common and can lead to unintended consequences. Many CDS systems use Boolean logic, which can be difficult for CDS analysts to specify accurately. We set out to determine the prevalence of certain types of Boolean logic errors in CDS statements. METHODS: Nine health care organizations extracted Boolean logic statements from their Epic electronic health record (EHR). We developed an open-source software tool, which implemented the Espresso logic minimization algorithm, to identify three classes of logic errors. RESULTS: Participating organizations submitted 260,698 logic statements, of which 44,890 were minimized by Espresso. We found errors in 209 of them. Every participating organization had at least two errors, and all organizations reported that they would act on the feedback. DISCUSSION: An automated algorithm can readily detect specific categories of Boolean CDS logic errors. These errors represent a minority of CDS errors, but very likely require correction to avoid patient safety issues. This process found only a few errors at each site, but the problem appears to be widespread, affecting all participating organizations. CONCLUSION: Both CDS implementers and EHR vendors should consider implementing similar algorithms as part of the CDS authoring process to reduce the number of errors in their CDS interventions.

BackgroundPublic reporting of Clostridioides difficile infection (CDI) using laboratory-identified events has led some institutions to revert from molecular-based tests to less sensitive testing modalities. At one academic medical center, researchers chose to use nucleic acid amplification test alone in CDI diagnosis with institutional protocols aimed at diagnostic stewardship.MethodsA single-center, quasi-experimental study was conducted to introduce and analyze the effects of various diagnostic stewardship interventions. In April 2017 an order report was created to inform providers of patients' recent bowel movements, laxative use, and prior Clostridioides difficile (CD) testing (Intervention 1). In November 2017 nursing staff were empowered to not send nondiarrheal stools for testing (Intervention 2). In February 2019, an interruptive alert was implemented to prevent testing that was not indicated (Intervention 3). CD testing rates and healthcare facility-onset CDI (HO-CDI) rates were compared before and after the interventions using one-way analysis of variance (ANOVA).ResultsAt baseline, testing for CD after 3 days of admission was performed at mean ± standard deviation of 15.9 ± 1.7 tests/1,000 patient-days. After Intervention 1, it decreased to 12.1 ± 1.1 tests. This further decreased to 10.6 ± 0.8 after Intervention 2 and to 8.1 ± 0.1 after Intervention 3 (p < 0.001). HO-CDI cases per 10,000 patient-days declined from 12.7 ± 1.4 cases at baseline to 10.7 ± 1.2 after Intervention 1, to 8.7 ± 2.4 after Intervention 2, and to 5.8 ± 0.2 after Intervention 3 (p = 0.03).ConclusionA multidisciplinary approach optimizing electronic health record support tools and leveraging nursing education can reduce both testing and HO-CDI rates while using the most sensitive testing modality.

BACKGROUND: As electronic health record (EHR) use becomes more widespread, detailed records of how users interact with the EHR, known as EHR audit logs, are being used to characterize the clinical workflows of physicians including residents. After-hours EHR use is of particular interest given its known association with physician burnout. Several studies have analyzed EHR audit logs for residents in other fields, such as internal medicine, but none thus far in ophthalmology. Here, we focused specifically on EHR use during on-call shifts outside of normal clinic hours. METHODS: In this retrospective study, we analyzed raw EHR audit log data from on-call shifts for 12 ophthalmology residents at a single institution over the course of a calendar year. Data were analyzed to characterize total time spent using the EHR, clinical volume, diagnoses of patients seen on call, and EHR tasks. RESULTS: Across all call shifts, the median and interquartile range (IQR) of the time spent logged into the EHR per shift were 88 and 131 minutes, respectively. The median (IQR) unique patient charts accessed per shift was 7 (9) patients. When standardized to per-hour measures, weekday evening shifts were the busiest call shifts with regard to both EHR use time and clinical volume. Total EHR use time and clinical volume were greatest in the summer months (July to September). Chart review comprised a majority (63.4%) of ophthalmology residents on-call EHR activities. CONCLUSION: In summary, EHR audit logs demonstrate substantial call burden for ophthalmology residents outside of regular clinic hours. These data and future studies can be used to further characterize the clinical exposure and call burden of ophthalmology residents and could potentially have broader implications in the fields of physician burnout and education policy.

ObjectiveTo evaluate informatics-enabled quality improvement (QI) strategies for promoting time spent on face-to-face communication between ophthalmologists and patients.MethodsThis prospective study involved deploying QI strategies during implementation of an enterprise-wide vendor electronic health record (EHR) in an outpatient academic ophthalmology department. Strategies included developing single sign-on capabilities, activating mobile- and tablet-based applications, EHR personalization training, creating novel workflows for team-based orders, and promoting problem-based charting to reduce documentation burden. Timing data were collected during 648 outpatient encounters. Outcomes included total time spent by the attending ophthalmologist on the patient, time spent on documentation, time spent on examination, and time spent talking with the patient. Metrics related to documentation efficiency, use of personalization features, use of team-based orders, and note length were also measured from the EHR efficiency portal and compared with averages for ophthalmologists nationwide using the same EHR.ResultsTime spent on exclusive face-to-face communication with patients initially decreased with EHR implementation (2.9 to 2.3 minutes, p = 0.005) but returned to the paper baseline by 6 months (2.8 minutes, p = 0.99). Observed participants outperformed national averages of ophthalmologists using the same vendor system on documentation time per appointment, number of customized note templates, number of customized order lists, utilization of team-based orders, note length, and time spent after-hours on EHR use.ConclusionInformatics-enabled QI interventions can promote patient-centeredness and face-to-face communication in high-volume outpatient ophthalmology encounters. By employing an array of interventions, time spent exclusively talking with the patient returned to levels equivalent to paper charts by 6 months after EHR implementation. This was achieved without requiring EHR redesign, use of scribes, or excessive after-hours work. Documentation efficiency can be achieved using interventions promoting personalization and team-based workflows. Given their efficacy in preserving face-to-face physician-patient interactions, these strategies may help alleviate risk of physician burnout.

OBJECTIVE: The study sought to determine availability and use of structured override reasons for drug-drug interaction (DDI) alerts in electronic health records. MATERIALS AND METHODS: We collected data on DDI alerts and override reasons from 10 clinical sites across the United States using a variety of electronic health records. We used a multistage iterative card sort method to categorize the override reasons from all sites and identified best practices. RESULTS: Our methodology established 177 unique override reasons across the 10 sites. The number of coded override reasons at each site ranged from 3 to 100. Many sites offered override reasons not relevant to DDIs. Twelve categories of override reasons were identified. Three categories accounted for 78% of all overrides: will monitor or take precautions, not clinically significant, and benefit outweighs risk. DISCUSSION: We found wide variability in override reasons between sites and many opportunities to improve alerts. Some override reasons were irrelevant to DDIs. Many override reasons attested to a future action (eg, decreasing a dose or ordering monitoring tests), which requires an additional step after the alert is overridden, unless the alert is made actionable. Some override reasons deferred to another party, although override reasons often are not visible to other users. Many override reasons stated that the alert was inaccurate, suggesting that specificity of alerts could be improved. CONCLUSIONS: Organizations should improve the options available to providers who choose to override DDI alerts. DDI alerting systems should be actionable and alerts should be tailored to the patient and drug pairs.

PurposeTo assess time requirements for patient encounters and estimate after-hours demands of paper-based clinical workflows in ophthalmology.DesignTime-and-motion study with a structured survey.MethodsThis study was conducted in a single academic ophthalmology department. A convenience sample consisted of 7 attending ophthalmologists from 6 subspecialties observed during 414 patient encounters for the time-motion analysis and 12 attending ophthalmologists for the survey. Outcome measurements consisted of total time spent by attending ophthalmologists per patient and time spent on documentation, examination, and talking with patients. The survey assessed time requirements of documentation-related activities performed outside of scheduled clinic hours.ResultsAmong the 7 attending ophthalmologists observed (6 men and 1 woman), mean ± SD age 43.9 ± 7.1 years, during encounters with 414 patients (57.8 ± 24.6 years of age), total time spent per patient was 8.1 ± 4.8 minutes, with 2.8 ± 1.4 minutes (38%) for documentation, 1.2 ± 0.9 minutes (17%) for examination, and 3.3 ± 3.1 minutes (37%) for talking with patients. New patient evaluations required significantly more time than routine follow-up visits and postoperative visits. Higher clinical volumes were associated with less time per patient. Survey results indicated that paper-based documentation was associated with minimal after-hours work on weeknights and weekends.ConclusionsPaper-based documentation takes up a substantial portion of the total time spent for patient care in outpatient ophthalmology clinics but is associated with minimal after-hours work. Understanding paper-based clinical workflows may help inform targeted strategies for improving electronic health record use in ophthalmology.

ObjectiveElectronic health records (EHRs) are widely adopted, but the time demands of EHR use on ophthalmology trainees are not well understood. This study evaluated ophthalmology trainee time spent on clinical activities in an outpatient clinic undergoing EHR implementation.DesignProspective, manual time-motion observations of ophthalmology trainees in 2018.ParticipantsEleven ophthalmology residents and fellows observed during 156 patient encounters.MethodsProspective time-motion study of ophthalmology trainees 2 weeks before and 6 weeks after EHR implementation in an academic ophthalmology department. Manual time-motion observations were conducted for 11 ophthalmology trainees in 6 subspecialty clinics during 156 patient encounters. Time spent documenting, examining, and talking with patients were recorded. Factors influencing time requirements were evaluated using linear mixed effects models.Main outcome measuresTotal time spent by ophthalmology residents and fellows per patient, time spent on documentation, examination, and talking with patients.ResultsSeven ophthalmology residents and four ophthalmology fellows with mean (standard deviation) postgraduate year of 3.7 (1.2) were observed during 156 patient encounters. Using paper charts, mean total time spent on each patient was 11.6 (6.5) minutes, with 5.4 (3.5) minutes spent documenting (48%). After EHR implementation, mean total time spent on each patient was 11.8 (6.9) minutes, with 6.8 (4.7) minutes spent documenting (57%). Total time expenditure per patient did not significantly change after EHR implementation (+0.17 minutes, 95% confidence interval [CI] for difference in means: -2.78, 2.45; p = 0.90). Documentation time did not change significantly after EHR implementation in absolute terms (+1.42 minutes, 95% CI: -3.13, 0.29; p = 0.10), but was significantly greater as a proportion of total time (48% on paper to 57% on EHR; +9%, 95% CI: 2.17, 15.83; p = 0.011).ConclusionTotal time spent per patient and absolute time spent on documentation was not significantly different whether ophthalmology trainees used paper charts or the recently implemented EHR. Percentage of total time spent on documentation increased significantly with early EHR use. Evaluating EHR impact on ophthalmology trainees may improve understanding of how trainees learn to use the EHR and may shed light on strategies to address trainee burnout.

IntroductionWhile early diagnostic decision support systems were built around knowledge bases, more recent systems employ machine learning to consume large amounts of health data. We argue curated knowledge bases will remain an important component of future diagnostic decision support systems by providing ground truth and facilitating explainable human-computer interaction, but that prototype development is hampered by the lack of freely available computable knowledge bases.MethodsWe constructed an open access knowledge base and evaluated its potential in the context of a prototype decision support system. We developed a modified set-covering algorithm to benchmark the performance of our knowledge base compared to existing platforms. Testing was based on case reports from selected literature and medical student preparatory material.ResultsThe knowledge base contains over 2000 ICD-10 coded diseases and 450 RX-Norm coded medications, with over 8000 unique observations encoded as SNOMED or LOINC semantic terms. Using 117 medical cases, we found the accuracy of the knowledge base and test algorithm to be comparable to established diagnostic tools such as Isabel and DXplain. Our prototype, as well as DXplain, showed the correct answer as "best suggestion" in 33% of the cases. While we identified shortcomings during development and evaluation, we found the knowledge base to be a promising platform for decision support systems.ConclusionWe built and successfully evaluated an open access knowledge base to facilitate the development of new medical diagnostic assistants. This knowledge base can be expanded and curated by users and serve as a starting point to facilitate new technology development and system improvement in many contexts.

BackgroundPediatric providers are key players in the treatment of childhood obesity, yet rates of obesity management in the primary care setting are low. The goal of this study was to examine the views of pediatric providers on conducting obesity management in the primary care setting, and identify potential resources and care models that could facilitate delivery of this care.MethodsA mixed methods approach was utilized. Four focus groups were conducted with providers from a large pediatric network in San Diego County. Based on a priori and emerging themes, a questionnaire was developed and administered to the larger group of providers in this network.ResultsBarriers to conducting obesity management fell into four categories: provider-level/individual (e.g., lack of knowledge and confidence), practice-based/systems-level (e.g., lack of time and resources), parent-level (e.g., poor motivation and follow-up), and environmental (e.g., lack of access to resources). Solutions centered around implementing a team approach to care (with case managers and health coaches) and electronic medical record changes to include best practice guidelines, increased ease of documentation, and delivery of standardized handouts/resources. Survey results revealed only 23.8% of providers wanted to conduct behavioral management of obesity. The most requested support was the introduction of a health educator in the office to deliver a brief behavioral intervention.ConclusionWhile providers recognize the importance of addressing weight during a well-child visit, they do not want to conduct obesity management on their own. Future efforts to improve health outcomes for pediatric obesity should consider implementing a collaborative care approach.

Background Learning patient outcomes is recognized as crucial for ongoing refinement of clinical decision-making, but is often difficult in fragmented care with frequent handoffs. Data on resident habits of seeking outcome feedback after handoffs are lacking. Methods We performed a mixed-methods study including (1) an analysis of chart re-access rates after handoffs performed using access logs of the electronic health record (EHR); and (2) a web-based survey sent to internal medicine (IM) and emergency medicine (EM) residents about their habits of and barriers to learning the outcomes of patients after they have handed them off to other teams. Results Residents on ward rotations were often able to re-access charts of patients after handoffs, but those on EM or night admitting rotations did so

Precision Medicine involves the delivery of a targeted, personalized treatment for a given patient. By harnessing the power of electronic health records (EHRs), we are increasingly able to practice precision medicine to improve patient outcomes. In this article, we introduce the scientific community at large to important building blocks for personalized treatment, such as terminology standards that are the foundation of the EHR and allow for exchange of health information across systems. We briefly review different types of clinical decision support (CDS) and present the current state of CDS, which is already improving the care patients receive with genetic profile-based tailored recommendations regarding diagnostic and treatment plans. We also report on limitations of current systems, which are slowly beginning to integrate new genomic data into patient records but still present many challenges. Finally, we discuss future directions and how the EHR can evolve to increase the capacity of the healthcare system in delivering Precision Medicine at the point of care. WIREs Syst Biol Med 2017, 9:e1378. doi: 10.1002/wsbm.1378 For further resources related to this article, please visit the WIREs website.

Adoption of Electronic Health Record (EHR) systems has led to collection of massive healthcare data, which creates oppor- tunities and challenges to study them. Computational phenotyping offers a promising way to convert the sparse and complex data into meaningful concepts that are interpretable to healthcare givers to make use of them. We propose a novel su- pervised nonnegative tensor factorization methodology that derives discriminative and distinct phenotypes. We represented co-occurrence of diagnoses and prescriptions in EHRs as a third-order tensor, and decomposed it using the CP algorithm. We evaluated discriminative power of our models with an Intensive Care Unit database (MIMIC-III) and demonstrated superior performance than state-of-the-art ICU mortality calculators (e.g., APACHE II, SAPS II). Example of the resulted phenotypes are sepsis with acute kidney injury, cardiac surgery, anemia, respiratory failure, heart failure, cardiac arrest, metastatic cancer (requiring ICU), end-stage dementia (requiring ICU and transitioned to comfort-care), intraabdominal conditions, and alcohol abuse/withdrawal.

ObjectivesIncrease parent/caregiver ability to correctly identify the attending in charge and define terminology of treatment team members (TTMs). We hypothesized that correct TTM identification would increase with use of an electronic communication tool. Secondary aims included assessing subjects' satisfaction with and trust of TTM and interest in computer activities during hospitalization.MethodsTwo similar groups of parents/legal guardians/primary caregivers of children admitted to the Pediatric Hospital Medicine teaching service with an unplanned first admission were surveyed before (Phase 1) and after (Phase 2) implementation of a novel electronic medical record (EMR)-based tool with names, photos, and definitions of TTMs. Physicians were also surveyed only during Phase 1. Surveys assessed TTM identification, satisfaction, trust, and computer use.ResultsMore subjects in Phase 2 correctly identified attending physicians by name (71% vs. 28%, P < .001) and correctly defined terms intern, resident, and attending (P ≤ .03) compared with Phase 1. Almost all subjects (>79%) and TTMs (>87%) reported that subjects' ability to identify TTMs moderately or strongly impacted satisfaction and trust. The majority of subjects expressed interest in using computers to understand TTMs in each phase.ConclusionsSubjects' ability to correctly identify attending physicians and define TTMs was significantly greater for those who used our tool. In our study, subjects reported that TTM identification impacted aspects of the TTM relationship, yet few could correctly identify TTMs before tool use. This pilot study showed early success in engaging subjects with the EMR in the hospital and suggests that families would engage in computer-based activities in this setting.

BackgroundCentralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner.ObjectiveThe objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies.Materials and methodsBased on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network.ResultsThe authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws.Discussion and conclusionFederated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage requirements to participate in sophisticated analyses based on federated research networks.

Diagnostic errors are common and costly, but difficult to detect. “Trigger” tools have promise to facilitate detection, but have not been applied specifically for inpatient diagnostic error. We performed a scoping review to collate all individual “trigger” criteria that have been developed or validated that may indicate that an inpatient diagnostic error has occurred. We searched three databases and screened 8568 titles and abstracts to ultimately include 33 articles. We also developed a conceptual framework of diagnostic error outcomes using real clinical scenarios, and used it to categorize the extracted criteria. Of the multiple criteria we found related to inpatient diagnostic error and amenable to automated detection, the most common were death, transfer to a higher level of care, arrest or “code”, and prolonged length of hospital stay. Several others, such as abrupt stoppage of multiple medications or change in procedure, may also be useful. Validation for general adverse event detection was done in 15 studies, but only one performed validation for diagnostic error specifically. Automated detection was used in only two studies. These criteria may be useful for developing diagnostic error detection tools.

Objectives: Emergency medicine (EM) residents desire, but often lack, reliable feedback of patient outcomes following handoffs to other providers. This gap is a substantial barrier to calibrating their diagnostic decision making and learning. To address this educational priority, we developed and evaluated the Post‐Handoff Reports of Outcomes (PHAROS) system—an automated system within our electronic health record (EHR) to deliver provider‐specific patient outcome feedback. Methods: PHAROS includes: (1) individualized lists of patients seen and brief summaries of each case, (2) flags for important posthandoff events, and (3) links to charts to facilitate review. Starting June 2020, we coupled PHAROS with a resident educational session and individualized emails every 2 weeks outlining patients seen, number of posthandoff events, and instructions on how to access the PHAROS system. Results: From July 2017 through April 2022, we measured the proportion of handoffs followed by reaccessing patients' charts between 2 and 14 days posthandoff—a proxy for following up on the patient's outcomes. We performed an interrupted time series analysis on this outcome to determine if PHAROS was associated with a significant change in the trend of our outcome over time. Our secondary outcome was the number of times PHAROS was viewed each month. Our primary outcome had a significant increase in the slope over time (+0.13%/month, p = 0.03) after the introduction of the personalized reports and a nonsignificant change (−1.6%, p = 0.07) at the time of the intervention. The median (IQR) number of views of PHAROS per month was 33.2 (23.75–38.75). Conclusions: The PHAROS system was associated with a significant increase in the rate of posthandoff chart reaccess among EM residents over time. The PHAROS project demonstrated the feasibility of harnessing the capabilities of the EHR to create an automated system to support EM trainee feedback of patient outcomes—a key component of diagnostic calibration and learning. [ABSTRACT FROM AUTHOR]

Multiple independent frameworks to support continuous improvement have been proposed to guide healthcare organizations. Two of the most visible are High‐reliability Health care, (Chassin et al., 2013) which is emphasized by The Joint Commission, and Learning Health Systems, (Institute of Medicine, 2011) highlighted by the National Academy of Medicine. We propose that organizations consider tightly linking these two models, creating a "Highly‐reliable Learning Health System." We describe several efforts at our organization that has resulted from this combined model and have helped our organization weather the COVID‐19 pandemic. The organizational changes created using this framework will enable our health system to support a culture of quality across our teams and better fulfill our tripartite mission of high‐quality care, effective education of trainees, and dissemination of important innovations. [ABSTRACT FROM AUTHOR]