Enhancing Suicide Risk Prediction Models with Temporal Clinical Note Features.

OBJECTIVE: The objective of this study was to investigate the impact of enhancing a structured-data-based suicide attempt risk prediction model with temporal Concept Unique Identifiers (CUIs) derived from clinical notes. We aimed to examine how different temporal schemes, model types, and prediction ranges influenced the model's predictive performance. This research sought to improve our understanding of how the integration of temporal information and clinical variable transformation could enhance model predictions. MATERIALS AND METHODS: We identified modeling targets using diagnostic codes for suicide attempts within 30, 90, or 365 days following a temporally grouped visit cluster. Structured data included medications, diagnoses, procedures, and demographics, while unstructured data consisted of terms extracted with regular expressions from clinical notes. We compared models trained only on structured data (controls) to hybrid models trained on both structured and unstructured data. We used two temporalization schemes for clinical notes: fixed 90-day windows and flexible epochs. We trained and assessed random forests and hybrid LSTM neural networks using AUPRC and AUROC, with additional evaluation of sensitivity and PPV at 95% specificity. RESULTS: The training set included 2,364,183 visit clusters with 2,009 30-day suicide attempts, and the testing set contained 471,936 visit clusters with 480 suicide attempts. Models trained with temporal CUIs outperformed those trained with only structured data. The window-temporalized LSTM model achieved the highest AUPRC (0.056 ± 0.013) for the 30-day prediction range. Hybrid models generally showed better performance compared to controls across most metrics. DISCUSSION AND CONCLUSION: This study demonstrated that incorporating EHR-derived clinical note features enhanced suicide attempt risk prediction models, particularly with window-temporalized LSTM models. Our results underscored the critical value of unstructured data in suicidality prediction, aligning with previous findings. Future research should focus on integrating more sophisticated methods to continue improving prediction accuracy, which will enhance the effectiveness of future intervention.

Defining documentation burden (DocBurden) and excess DocBurden for all health professionals: A scoping review.

OBJECTIVE: Efforts to reduce documentation burden (DocBurden) for all health professionals (HP) are aligned with national initiatives to improve clinician wellness and patient safety. Yet DocBurden has not been precisely defined, limiting national conversations and rigorous, reproducible, and meaningful measures. Increasing attention to DocBurden motivated this work to establish a standard definition of DocBurden, with the emergence of excessive DocBurden as a term. METHODS: We conducted a scoping review of DocBurden definitions and descriptions, searching six databases for scholarly, peer-reviewed, and gray literature sources, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extensions for Scoping Review (PRISMA-ScR) guidance. For the concept clarification phase of work, we used the American Nursing Informatics Association (ANIA)'s 6-Domains of Burden Framework. RESULTS: A total of 153 articles were included based on a priori criteria. Most articles described a focus on DocBurden, but only 18% (n=28) provided a definition. We define excessive DocBurden as the stress and unnecessarily heavy work a HP or healthcare team experiences when usability of documentation systems and documentation activities (i.e., generation, review, analysis and synthesis of patient data) are not aligned in support of care delivery. A negative connotation was attached to burden without a neutral state in included sources, which does not align with dictionary definitions of burden. CONCLUSIONS: Existing literature does not distinguish between a baseline or required task load to conduct patient care resulting from usability issues(DocBurden), and the unnecessarily heavy tasks and requirements that contribute to excessive DocBurden. Our definition of excessive DocBurden explicitly acknowledges this distinction, to support development of meaningful measures for understanding and intervening on excessive DocBurden locally, nationally and internationally.

A multi-site randomized trial of a clinical decision support intervention to improve problem list completeness.

OBJECTIVE: To improve problem list documentation and care quality. MATERIALS AND METHODS: We developed algorithms to infer clinical problems a patient has that are not recorded on the coded problem list using structured data in the electronic health record (EHR) for 12 clinically significant heart, lung, and blood diseases. We also developed a clinical decision support (CDS) intervention which suggests adding missing problems to the problem list. We evaluated the intervention at 4 diverse healthcare systems using 3 different EHRs in a randomized trial using 3 predetermined outcome measures: alert acceptance, problem addition, and National Committee for Quality Assurance Healthcare Effectiveness Data and Information Set (NCQA HEDIS) clinical quality measures. RESULTS: There were 288 832 opportunities to add a problem in the intervention arm and the problem was added 63 777 times (acceptance rate 22.1%). The intervention arm had 4.6 times as many problems added as the control arm. There were no significant differences in any of the clinical quality measures. DISCUSSION: The CDS intervention was highly effective at improving problem list completeness. However, the improvement in problem list utilization was not associated with improvement in the quality measures. The lack of effect on quality measures suggests that problem list documentation is not directly associated with improvements in quality measured by National Committee for Quality Assurance Healthcare Effectiveness Data and Information Set (NCQA HEDIS) quality measures. However, improved problem list accuracy has other benefits, including clinical care, patient comprehension of health conditions, accurate CDS and population health, and for research. CONCLUSION: An EHR-embedded CDS intervention was effective at improving problem list completeness but was not associated with improvement in quality measures.

Phenotyping coronavirus disease 2019 during a global health pandemic: Lessons learned from the characterization of an early cohort.

From the start of the coronavirus disease 2019 (COVID-19) pandemic, researchers have looked to electronic health record (EHR) data as a way to study possible risk factors and outcomes. To ensure the validity and accuracy of research using these data, investigators need to be confident that the phenotypes they construct are reliable and accurate, reflecting the healthcare settings from which they are ascertained. We developed a COVID-19 registry at a single academic medical center and used data from March 1 to June 5, 2020 to assess differences in population-level characteristics in pandemic and non-pandemic years respectively. Median EHR length, previously shown to impact phenotype performance in type 2 diabetes, was significantly shorter in the SARS-CoV-2 positive group relative to a 2019 influenza tested group (median 3.1 years vs 8.7; Wilcoxon rank sum P = 1.3e-52). Using three phenotyping methods of increasing complexity (billing codes alone and domain-specific algorithms provided by an EHR vendor and clinical experts), common medical comorbidities were abstracted from COVID-19 EHRs, defined by the presence of a positive laboratory test (positive predictive value 100%, recall 93%). After combining performance data across phenotyping methods, we observed significantly lower false negative rates for those records billed for a comprehensive care visit (p = 4e-11) and those with complete demographics data recorded (p = 7e-5). In an early COVID-19 cohort, we found that phenotyping performance of nine common comorbidities was influenced by median EHR length, consistent with previous studies, as well as by data density, which can be measured using portable metrics including CPT codes. Here we present those challenges and potential solutions to creating deeply phenotyped, acute COVID-19 cohorts.

Patient-centered care, collaboration, communication, and coordination: a report from AMIA's 2013 Policy Meeting.

In alignment with a major shift toward patient-centered care as the model for improving care in our health system, informatics is transforming patient-provider relationships and overall care delivery. AMIA's 2013 Health Policy Invitational was focused on examining existing challenges surrounding full engagement of the patient and crafting a research agenda and policy framework encouraging the use of informatics solutions to achieve this goal. The group tackled this challenge from educational, technical, and research perspectives. Recommendations include the need for consumer education regarding rights to data access, the need for consumers to access their health information in real time, and further research on effective methods to engage patients. This paper summarizes the meeting as well as the research agenda and policy recommendations prioritized among the invited experts and stakeholders.

US and Scottish Health Professionals attitudes toward DNA biobanking.

The authors used a fifteen item survey to canvass 200 health care professionals in the United States and Scotland about their attitudes toward the potential utility of a DNA biobank. Results indicate a broadly favorable opinion in both locations. This finding seems to support further development of such a tool.

Quill: a novel approach to structured reporting.

The authors will demonstrate Quill (QUestions and Information Logically Linked), a comprehensive structured reporting environment for ambulatory care that was developed at the Vanderbilt University Medical Center. A notes capture tool was sought with the immediate hope of decreasing or eliminating transcription costs (currently around $6M/yr) and paper based processing while providing a foundation for decision support and research in the future.