Applied Clinical Informatics最新文献

Appropriate Use Criteria Clinical Decision Support (AUC CDS) was legislatively mandated in the United States in 2014, and multiple CDS vendors were designated as qualified Clinical Decision Support Mechanisms by the Centers for Medicare and Medicaid Services. Little is known about the costs and benefits of these systems in real-world settings.We evaluated the effectiveness of an AUC CDS system and the time costs it imposes on clinicians at an academic medical center.Our U.S. academic medical center's enterprise data warehouse was queried for AUC CDS alert events and timestamps occurring between July 1, 2021, and June 30, 2022. We calculated the percentage of altered orders and alert-related timespans, and used these to calculate CDS positive predictive value (PPV), time costs, and the cost-benefit ratio of minutes of provider time per altered order. Based on the medical literature and expert opinion on well-performing CDS, we hypothesized a CDS PPV of 8%.Overall PPV was 1%, leading us to reject our hypothesis that our CDS was well-performing (p < 0.001). Median time costs per alert were high (12 seconds load time, 2 seconds dwell time), yielding a CDS cost-benefit ratio of 38 provider minutes per altered order.Despite using one of three market-leading AUC CDS tools, our CDS demonstrated long load times, short dwell times, and low PPV. Provider attention is not free-policymakers should consider both CDS effectiveness and costs (including time costs) when designing AUC policy.

Nurses are the largest group of electronic health record (EHR) users in Canada, yet their experiences with documentation burden remain underexplored. While EHR-generated usage data, such as audit logs and time-motion metrics, have been used to quantify documentation time, they are rarely used to better understand EHR inefficiencies and identify potential changes for nursing documentation and workflows. This approach may help address instances of documentation demands detracting from direct patient care and contributing to burnout, which has been largely reported by nurses.This study aimed to: (1) examine EHR utilization patterns and time spent by nurses across clinical venues and nurse types; (2) identify EHR areas contributing most to nursing workload; (3) determine predictors of EHR time; and (4) assess differences in usage patterns across venues.We analyzed 12 months of EHR usage data from nurses at Canada's largest academic mental health hospital using Cerner Advance (Oracle Health). Seven metrics were selected in collaboration with a Nursing Advisory Council. Regression and least-squares means comparisons were conducted using R, with venue and nurse type as predictors.Data from 840 nurses revealed significant differences in EHR usage across venues and nurse types. Mean active time per patient per shift was highest in inpatient (19.3 minutes), followed by emergency (14.8 minutes), and ambulatory settings (6.3 minutes). Registered Practical Nurses (RPNs) averaged more active EHR time (20.1 minutes) than Registered Nurses (16.4 minutes). Documentation time per patient was significantly different across venues (F [3,832] = 71.97, p < 0.001) and nurse types (p = 0.0018). PowerForms time also varied significantly (F [3,818] = 102.1, p < 0.001). These findings support targeted EHR optimization efforts based on clinical context and role.Significant variation exists in how nurses interact with EHRs, with documentation representing a substantial time burden, especially for RPNs and inpatient settings. These findings emphasize the need for venue and role-specific optimization strategies and underscore the importance of including nurses' voices in EHR design and quality improvement initiatives.

Health equity is greatly impacted by the systems and processes with which health systems deliver care. Given the minimal guidance on measurement and reporting of health inequities specific to key population health outcomes, a solution for measurement of health equity is proposed.The concept of a lens of equity was adopted to disaggregate common measures such as breast cancer screening rates to expose inequities across neighborhoods and races in populations served. Two measures were introduced into the corporate measurement systems, race/ethnicity as measured in the electronic health record, and a surrogate measure of family income.An equity category was added to system scorecards and counted toward corporate goals along with data insights and discovery tools to support the efforts of the breast cancer screening improvement teams. Over a 1-year timeframe, Endeavor Health not only met but exceeded its breast cancer screening equity goal, increasing mammography adherence from 73 to 82.6% among residents in the lowest-income neighborhoods served.The analytics and data systems that support complex health care measurement tools require diligent and thoughtful design to meet external reporting requirements and support the internal teams who aim to improve the care of populations served. The analytic approach presented may be readily extended to populations with other potentially impactful differences in social determinants and health status. A "lens-of-equity" tool may be established along similar lines, allowing policy and strategy initiatives to be appropriately targeted and successfully implemented.

End-of-life care (EoLC), such as advance care planning, advance directives, hospice, and palliative care consults, can improve patient quality of life and reduce costs, yet such interventions are underused. Machine learning-based prediction models show promise in identifying patients who may be candidates for EoLC based on increased risk of short-term (less than 1 year) mortality. Clinical decision support systems using these models can identify candidate patients at a time during their care when care teams can increase the provision of EoLC.Evaluate changes in the provision of EoLC with implementation of a machine learning-based mortality prediction model in an academic health center.A clinical decision support system based on a random forest machine learning mortality prediction model is described. The system was implemented in an academic health system, first in the medical intensive care unit, then house-wide. An interrupted time series analysis was performed over the 16 weeks prior to and 43 weeks after the implementations. Primary outcomes were the rates of documentation of advance directives, palliative care consultations, and do not attempt resuscitation (DNAR) orders among encounters with an alert for PRISM score over 50% (PRISM positive) compared with those without an alert (PRISM negative).Following a steep preintervention decline, the rate of advance directive documentation improved immediately after implementation. However, the implementations were not associated with improvements in any of the other primary outcomes. The model discrimination was substantially worse than that observed in model development, and after 16 months, it was withdrawn from production.A clinical decision support system based on a machine learning mortality prediction model failed to provide clinically meaningful improvements in EoLC measures. Possible causes for the failure include system-level factors, clinical decision support system design, and poor model performance.

Electronic health records (EHRs) are intended to improve clinical practice, but excessive alerts can be counterproductive, leading to workarounds. The Mortality Surprise Question (MSQ), a tool for identifying patients who might benefit from early end-of-life discussions, was integrated into the Emergency Department (ED) EHR admission process.This study investigated how the staged implementation of a clinical decision support tool at the point of admission order entry affected ED clinician admission order practices.This retrospective cohort study examined ED admission orders from 2023 across three EDs. Clinicians used either the Quicklist or Disposition tab in the Epic EHR for admissions. The MSQ was introduced in two phases, first to the Quicklist on May 31, 2023, and then to the Disposition tab on September 11, 2023. Admissions from both tabs were analyzed pre- and post-MSQ implementation. Statistical analysis included chi-square testing to compare the admission source in the EHR after each phase of implementation of the MSQ to examine changes in the clinicians' admission workflow, with further categorization based on clinician EHR experience.Overall, 53,897 patients were admitted from the ED, with 29,542 (55%) admissions via the Quicklist and 24,355 (45%) via the Disposition tab. A statistically significant difference was found in Quicklist admission proportions before and after MSQ implementation in both workflows. As compared with clinicians with less than 2 years of experience with the EHR, clinicians with 2 to 4 years of EHR use were less likely to use the Quicklist after MSQ implementation, whereas those with over 4 years of use were more likely to use it.The MSQ disrupted established workflows, prompting clinicians to initially adopt more effortful alternatives to avoid the new cognitive task. Embedding the MSQ into these alternatives reduced resistance, highlighting that removing optionality promotes adoption. Accounting for clinician habits and potential workarounds can enhance the integration and efficiency of new quality improvement measures.

Clinical decision support (CDS) systems have been widely adopted across clinical settings to promote evidence-based practice for clinicians. CDS malfunctions often affect the user experience and indirectly or directly interfere with patient care. To enhance optimal performance, it is critical to constantly monitor the performance of the tool and react promptly when malfunctions are identified.This study aimed to describe malfunctions identified in the development and implementation of a CDS alert as well as lessons learned.A pragmatic randomized controlled trial of a CDS alert for primary care patients with chronic kidney disease and uncontrolled blood pressure was conducted. The alert included prechecked default orders for medication initiation or titration, basic metabolic panel, and nephrology electronic consult. Alert monitoring involved retrospective chart review and review of alert firing reports.Eight CDS malfunctions were identified. The most common causes of malfunctions were due to conceptualization and build errors. Provider feedback and retrospective chart review were the primary methods of identifying the root cause of malfunctions.Our findings highlight the need for CDS interventions to be continuously monitored through chart review, alert firing reports, and opportunities for provider feedback. Lessons learned from CDS malfunctions can be implemented to improve provider trust in automated electronic health record-based alerts, reduce administrative burden, and prevent inappropriate alert recommendations that can negatively affect patient outcomes. This study is registered with Clinivaltrials.gov (identifier: NCT03679247).

Many academic medical centers (AMCs) rely on systems like the Vizient Quality and Accountability Scorecard to track quality metrics such as the observed-to-expected (O/E) mortality ratio. The O/E mortality ratio calculation relies on clinical documentation. Missed documentation of diagnoses and risk factors for mortality leads to an underestimated expected mortality, which negatively affects the O/E metric.We aimed to reduce our O/E mortality ratio from a median of 1.08 (± 0.10) to a median well below 0.90 within 12 months by improving the accuracy of clinical documentation.We used a continuous quality improvement process that began with creating a rule-based tool within a standardized documentation template. The tool was designed to pull pertinent discrete electronic health record data into clinician documentation. The tool only pulled in data that were present on admission, and it especially prioritized inclusion of frequently missed risk factors according to prior coding query data. We then formed a multidisciplinary mortality review committee where providers reviewed mortality cases, made suggestions for documentation clarification, and found potential diagnoses and risk factors that the patient had which were missing from the documentation. We then leveraged the committee's expertise and feedback to improve the rule-based clinical tool.Over the 21-month period following implementation, the median O/E mortality ratio decreased by 30%, from 1.08 (± 0.10) to 0.72 (± 0.13) and consistently remained below the prior levels. Importantly, the intervention also led to a reduction in the total number of coding queries sent to clinicians, indicating a lower administrative burden for clinicians and coders.Our interventions showed a clear improvement in the O/E mortality ratio at our AMC and in the expected mortality percentage compared with other similar institutions without significantly increasing burden on clinicians or coding specialists.

The effective operation of magnetic resonance imaging (MRI) systems relies on physical interactions with complex imaging environments, equipment, and user interfaces (UIs). However, there is limited empirical data evaluating how physical interactions with MRI equipment and accessories, workspace configuration, MRI UI design, and technical proficiency influence clinical workflow.In this study, a cross-sectional survey was conducted among MRI end-users, across public and private health facilities (n = 13), using a structured questionnaire to assess demographics, patient positioning and equipment handling, MRI workspace adequacy, interface usability (guided by Nielsen's heuristics), and self-reported MRI skill proficiency.The predominant field strength of scanners in current use was 1.5T. General Electric was the most frequently used MRI scanner brand. Most respondents received their MRI training from nonvendor sources-such as academic institutions or peer-based instruction-rather than directly from equipment manufacturers. High ease-of-use ratings were reported for patient positioning and equipment handling tasks. Workspace adequacy was mostly rated as very adequate to highly adequate. Computed Tomography-experienced users showed moderate-to-high proficiency in MRI pulse sequencing and image optimization. However, lower proficiency was noted in quality assurance and physiologic monitoring. Help documentation within the MRI interface received the lowest usability scores. No significant differences in usability or proficiency were found between those trained by vendors versus nonvendors (U = 8.5-15.0; p = 0.376-0.921).Opportunities exist to enhance clinical workflow and patient throughput by refining error-handling features, improving support documentation, reinforcing ongoing professional development, and re-evaluating training delivery by incorporating iterative, multimedia-based learning modules and regular postinstallation refresher sessions. End-user input in UI design and user feedback analysis should be prioritized to improve system usability and clinical efficiency.

The use of real-time clinical decision support (CDS), such as Our Practice Advisory (OPAs), augments clinical decisions while helping to reduce errors and ensuring compliance with organizational best practices.1 In complex large health systems, processes for standardization and adherence to emergency department (ED)-based suicide screening practices are challenging and may benefit from the use of CDS-based tools adhering to the five rights of CDS.2 To improve suicide screening compliance for the ED to 95% by implementing a contextually appropriate CDS-based tool within the electronic health record (EHR).A multidisciplinary group of quality and ED nursing leadership aimed to develop a chief complaint-driven OPA that improved adherence to and completion of suicide screening in the ED. Using an iterative design process over 3 months, a series of two distinct suicide screening OPAs were developed with varying levels of interruption, but both relied on rule-based logic to identify if an ED patient met one of the 57 predefined "Reasons for Visit" or chief complaints requiring suicide screening. Use of chief complaint-driving CDS removed the need for manually remembering complex criteria while contributing to meeting regulatory and organizational standards.The ED suicide screening compliance improved from 64.96 to 77.66% with the initial implementation of the noninterruptive OPA. Subsequently, an interruptive OPA (pop-up window based on a defined trigger that stops the clinician and requires a response), was introduced which further increased screenings being completed to 91.69%. The use of CDS interruptive OPAs significantly improved compliance with suicide screening by including the Columbia Suicide Severity Rating Scale tool directly in the OPA.Use of contextually relevant information, such as reason for visit or chief complaint, and interruptive CDS tools embedded into EHR workflows may improve ED-based suicide screening.

Despite its morbidity, mortality, and financial burden, in-hospital malnutrition remains underdiagnosed and undertreated. Artificial intelligence (AI) offers a promising clinical informatics solution for identifying malnutrition risk and one that can be coupled with clinician-delivered patient care.The objectives of the study were to evaluate an AI-based hospital malnutrition screening model in a large and diverse inpatient population and to compare it to the currently used clinician-delivered malnutrition screening tool.We studied the performance of a gradient-boosted decision tree model incorporating a large language model (LLM) for feature extraction using the electronic medical record data of 106,449 patients over 3.75 years.The model's area under the receiver operating curve was 0.92 (95% confidence interval [CI]: 0.91-0.92) on the first day of hospitalization and rose to 0.95 (95% CI: 0.95-0.96) using the maximum risk predicted for each patient throughout hospitalization, indexed against discharge-coded malnutrition. Similar results were observed when indexed against dietitian-recorded malnutrition. The model outperformed the nurse-administered, modified version of the Malnutrition Screening Tool (MST) that was used in practice. Patients identified by the model had higher likelihoods of readmission and death compared with patients identified by the nurse-administered screener.Our study findings provide validation for a novel model's use in the prediction of in-hospital malnutrition.