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With growing concerns that big data will only augment the problem of unreliable research, the Laboratory of Computational Physiology at the Massachusetts Institute of Technology organized the Critical Data Conference in January 2014. Thought leaders from academia, government, and industry across disciplines—including clinical medicine, computer science, public health, informatics, biomedical research, health technology, statistics, and epidemiology—gathered and discussed the pitfalls and challenges of big data in health care. The key message from the conference is that the value of large amounts of data hinges on the ability of researchers to share data, methodologies, and findings in an open setting. If empirical value is to be from the analysis of retrospective data, groups must continuously work together on similar problems to create more effective peer review. This will lead to improvement in methodology and quality, with each iteration of analysis resulting in more reliability.

The research world is undergoing a transformation into one in which data, on massive levels, is freely shared. In the clinical world, the capture of data on a consistent basis has only recently begun. We propose an operational vision for a digitally based care system that incorporates data-based clinical decision making. The system would aggregate individual patient electronic medical data in the course of care; query a universal, de-identified clinical database using modified search engine technology in real time; identify prior cases of sufficient similarity as to be instructive to the case at hand; and populate the individual patient's electronic medical record with pertinent decision support material such as suggested interventions and prognosis, based on prior outcomes. Every individual's course, including subsequent outcomes, would then further populate the population database to create a feedback loop to benefit the care of future patients.

Objectives Missed and delayed cancer diagnoses are common, harmful, and often preventable. We previously validated a digital quality measure (dQM) of emergency presentation (EP) of lung cancer in 2 US health systems. This study aimed to apply the dQM to a new national electronic health record (EHR) database and examine demographic associations. Materials and Methods We applied the dQM (emergency encounter followed by new lung cancer diagnosis within 30 days) to Epic Cosmos, a deidentified database covering 184 million US patients. We examined dQM associations with sociodemographic factors. Results The overall EP rate was 19.6%. EP rate was higher in Black vs White patients (24% vs 19%, P  < .001) and patients with younger age, higher social vulnerability, lower-income ZIP code, and self-reported transport difficulties. Discussion We successfully applied a dQM based on cancer EP to the largest US EHR database. Conclusion This dQM could be a marker for sociodemographic vulnerabilities in cancer diagnosis. [ABSTRACT FROM AUTHOR]

Pragmatic clinical trials (PCTs) have an established presence in clinical research and yet have only recently garnered attention within the landscape of genomic medicine. Using the PRagmatic‐Explanatory Continuum Indicator Summary 2 (PRECIS‐2) as a framework, this paper illustrates the application of PCT principles to The Integrating Pharmacogenetics In Clinical Care (I‐PICC) Study, a trial of pharmacogenetic testing prior to statin initiation for cardiovascular disease prevention in primary care. The trial achieved high engagement with providers (85% enrolled of those approached) and enrolled a representative sample of participants for which statin therapy would be recommended. The I‐PICC Study has a high level of pragmatism, which should enhance the generalizability of its findings. The PRECIS‐2 may be useful in the design and evaluation of PCTs of genomic medicine interventions, contributing to the generation of evidence that can bridge the gap between genomics innovation and clinical adoption. [ABSTRACT FROM AUTHOR]

Importance: Missed diagnosis can lead to preventable patient harm., Objective: To develop and implement a portfolio of electronic triggers (e-triggers) and examine their performance for identifying missed opportunities in diagnosis (MODs) in emergency departments (EDs)., Design, Setting, and Participants: In this retrospective medical record review study of ED visits at 1321 Veterans Affairs health care sites, rules-based e-triggers were developed and implemented using a national electronic health record repository. These e-triggers targeted 6 high-risk presentations for MODs in treat-and-release ED visits. A high-risk stroke e-trigger was applied to treat-and-release ED visits from January 1, 2016, to December 31, 2020. A symptom-disease dyad e-trigger was applied to visits from January 1, 2018, to December 31, 2019. High-risk abdominal pain, unexpected ED return, unexpected hospital return, and test result e-triggers were applied to visits from January 1, 2019, to December 31, 2019. At least 100 randomly selected flagged records were reviewed by physician reviewers for each e-trigger. Data were analyzed between January 2024 and April 2024., Exposures: Treat-and-release ED visits involving high-risk stroke, symptom-disease dyads, high-risk abdominal pain, unexpected ED return, unexpected hospital return, and abnormal test results not followed up after initial ED visit., Main Outcomes and Measures: Trained physician reviewers evaluated the presence/absence of MODs at ED visits and recorded data on patient and clinician characteristics, types of diagnostic process breakdowns, and potential harm from MODs., Results: The high-risk stroke e-trigger was applied to 8 792 672 treat-and-release ED visits (4 967 283 unique patients); the symptom-disease dyad e-trigger was applied to 3 692 454 visits (2 070 979 patients); and high-risk abdominal pain, unexpected ED return, unexpected hospital return, and test result e-triggers were applied to 1 845 905 visits (1 032 969 patients), overall identifying 203, 1981, 170, 116 785, 14 879, and 2090 trigger-positive records, respectively. Review of 625 randomly selected patient records (mean [SD] age, 62.5 [15.2] years; 553 [88.5%] male) showed the following MOD counts and positive predictive values (PPVs) within each category: 47 MODs (PPV, 47.0%) for stroke, 31 MODs (PPV, 25.8%) for abdominal pain, 11 MODs (PPV, 11.0%) for ED returns, 23 MODs (PPV, 23.0%) for hospital returns, 18 MODs (PPV, 18.0%) for symptom-disease dyads, and 55 MODs (PPV, 52.4%) for test results. Patients with MODs were slightly older than those without (mean [SD] age, 65.6 [14.5] vs 61.2 [15.3] years; P < .001). Reviewer agreement was favorable (range, 72%-100%). In 108 of 130 MODs (83.1%; excluding MODs related to the test result e-trigger), the most common diagnostic process breakdown involved the patient-clinician encounter. In 185 total MODs, 20 patients experienced severe harm (10.8%), and 54 patients experienced moderate harm (29.2%)., Conclusions and Relevance: In this retrospective medical record review study, rules-based e-triggers were useful for post hoc detection of MODs in ED visits. Interventions to target ED work system factors are urgently needed to support patient-clinician encounters and minimize harm from diagnostic errors.

Introduction: Researchers are increasingly developing algorithms that impact patient care, but algorithms must also be implemented in practice to improve quality and safety., Objective: We worked with clinical operations personnel at two US health systems to implement algorithms to proactively identify patients without timely follow-up of abnormal test results that warrant diagnostic evaluation for colorectal or lung cancer. We summarise the steps involved and lessons learned., Methods: Twelve sites were involved across two health systems. Implementation involved extensive software documentation, frequent communication with sites and local validation of results. Additionally, we used automated edits of existing code to adapt it to sites' local contexts., Results: All sites successfully implemented the algorithms. Automated edits saved sites significant work in direct code modification. Documentation and communication of changes further aided sites in implementation., Conclusion: Patient safety algorithms developed in research projects were implemented at multiple sites to monitor for missed diagnostic opportunities. Automated algorithm translation procedures can produce more consistent results across sites., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY. Published by BMJ.)

Pragmatic clinical trials (PCTs) have an established presence in clinical research and yet have only recently garnered attention within the landscape of genomic medicine. Using the PRagmatic-Explanatory Continuum Indicator Summary 2 (PRECIS-2) as a framework, this paper illustrates the application of PCT principles to The Integrating Pharmacogenetics In Clinical Care (I-PICC) Study, a trial of pharmacogenetic testing prior to statin initiation for cardiovascular disease prevention in primary care. The trial achieved high engagement with providers (85% enrolled of those approached) and enrolled a representative sample of participants for which statin therapy would be recommended. The I-PICC Study has a high level of pragmatism, which should enhance the generalizability of its findings. The PRECIS-2 may be useful in the design and evaluation of PCTs of genomic medicine interventions, contributing to the generation of evidence that can bridge the gap between genomics innovation and clinical adoption., (© 2019 The Authors. Clinical and Translational Science published by Wiley Periodicals, Inc. on behalf of the American Society for Clinical Pharmacology and Therapeutics.)

Laboratory data collected in the electronic health record as part of routine care can be used in secondary research. For example, the US Department of Veterans Affairs maintains a data warehouse covering over 20 million individuals and 6.6 billion lab tests. However, data aggregation in such a data warehouse can be difficult. In order to retrieve all or nearly all of one type of lab result with a high degree of precision, we perform clinical concept adjudication, which is the process of an expert determining which database records correspond to a target clinical concept. In this work, we develop an interactive machine learning tool to "extend the reach" of expert laboratory test adjudicators. Our tool provides access to automatic laboratory classification in a user-facing front end that covers all steps in an adjudication workflow, in order to lower barriers to collaboration, increase transparency of adjudication, and to promote efficiencies and data reuse.