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Automatic population of eMeasurements from EHR systems for inpatient falls.

ABSTRACT: Objective:Representing nursing data sets in a standard way will help to facilitate sharing relevant information across settings. We aimed to populate nursing process and outcome metrics with electronic health record (EHR) data and then compare the results with event reporting systems. Methods:We used the "eMeasure" development process of the National Quality Forum adopted by the American Nurses Association. We used operational definitions of quality measures from the American Nurses Association and the US Institute for Healthcare Improvement and employed concept mapping of local data elements to 2 controlled vocabularies to define a standard data dictionary: (1) Logical Observation Identifiers Names and Codes and (2) International Classification for Nursing Practice. We assessed feasibility using the nursing data set of 7829 and 8199 patients from 2 general hospitals with different EHR systems. Using inpatient falls as a use case, we compared the populated measures with results from the event reporting systems. Results:We identified 17 care components and 118 unique concepts and matched them with data elements in the EHRs. Including suboptimal mapping, 98% of the assessment concepts mapped to Logical Observation Identifiers Names and Codes and 52.9% of intervention concepts mapped to International Classification for Nursing Practice. While not all process indicators were available from event reporting systems, we successfully populated 9 fall prevention process indicators and the fall rate outcome indicator from the 2 EHRs. We were unable to populate the falls with an injury rate indicator. Conclusions:EHR data can populate fall prevention process measure metrics and at least one inpatient fall prevention outcome metric.

SUBMITTER: Cho I

PROVIDER: S-EPMC7647033 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Automatic population of eMeasurements from EHR systems for inpatient falls.

Cho Insook I Boo Eun-Hee EH Lee Soo-Youn SY Dykes Patricia C PC

Journal of the American Medical Informatics Association : JAMIA 20180601 6

<h4>Objective</h4>Representing nursing data sets in a standard way will help to facilitate sharing relevant information across settings. We aimed to populate nursing process and outcome metrics with electronic health record (EHR) data and then compare the results with event reporting systems.<h4>Methods</h4>We used the "eMeasure" development process of the National Quality Forum adopted by the American Nurses Association. We used operational definitions of quality measures from the American Nurs ...[more]

PMID: 29659868

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Project description:ImportanceDespite the broad adoption and optimization of electronic health record (EHR) systems across the continuum of care, serious usability and safety problems persist.ObjectiveTo assess whether EHR safety performance is associated with EHR frontline user experience in a national sample of hospitals.Design, setting, and participantsThis cross-sectional study included all US adult hospitals that used the National Quality Forum Leapfrog Health IT Safety Measure and also used the ARCH Collaborative EHR User experience survey from January 1, 2017, to January 1, 2019. Data analysis was performed from September 2020 to November 2022.Main outcomes and measuresThe primary outcomes were hospital performance on the Leapfrog Health IT Safety measure (overall and 10 subcomponents) and the ARCH collaborative frontline user experience scores (overall and 8 subcomponents). Ordinary least squares models with survey responses clustered by hospital were used to assess associations between the overall measures and their subcomponents.ResultsThere were 112 hospitals and 5689 frontline user surveys included in the study. Hospitals scored a mean of 0.673 (range, 0.297-0.973) on the Leapfrog Health IT safety measure; the mean ARCH EHR user experience score was 3.377 (range, 1 [best] to 5 [worst]). The adjusted β coefficient between the overall safety score and overall user experience score was 0.011 (95% CI, 0.006-0.016). The ARCH overall score was also significantly associated with 10 subcategory scores of the Leapfrog Health IT safety score, and the overall Leapfrog score was associated with the 8 subcategory scores of the ARCH user experience score.Conclusions and relevanceThis cross-sectional study found a positive association between frontline user-rated EHR usability and EHR safety performance. This finding suggests that improving EHR usability, which is a current well-known pain point for EHR users, could have direct benefits in terms of improved EHR safety.

Project description:A frequently occurring job during on-call and out-of-hours shifts is reviewing a patient following a fall with this often being the responsibility of the most junior and inexperienced doctors. Following a pilot audit we identified inconsistencies in medical assessment and documentation, with 50% of expected data points not recorded. Failure to complete a thorough assessment can lead to missed injuries, prolonged length of stay, and litigation. Using the plan, do, study, act (PDSA) cycle model this project sought to address this through providing teaching to junior doctors and the development of a pro-forma. Three style cycles of data collection were performed; a formal baseline dataset, after delivering a teaching session to new junior doctors and following the trial of the new fall pro-forma. We selected 15 to 17 patient notes to review at random during a one month period for each data collection cycle and compared the medical assessment to the standards outlined by the National Patient Safety Agency (NPSA) guidelines.[1] There were two key areas of improvement identified following the teaching session and introduction of the proforma. Documentation of a fall history was improved by nearly 30% being recorded in 100% of cases after the interventions. Documentation of a thorough musculoskeletal examination was improved from being recorded in just 54% of cases to 77% of cases; it was recorded in 100% of the cases where the proforma was used. The project demonstrated the need to improve documentation and assessment of a patient who has fallen. Initial data collection has shown that assessment and documentation were improved providing teaching to junior doctors and by use of the document. The pro-forma has since been incorporated into hospital policy and now forms the compulsory documentation expected of the doctors and nurses managing patients following a fall. Ensuring easy access to the proforma and re-auditing after editing the document will be the next steps.

Project description:AimsUnderstanding atypical forms of diabetes (AD) may advance precision medicine, but methods to identify such patients are needed. We propose an electronic health record (EHR)-based algorithmic approach to identify patients who may have AD, specifically those with insulin-sufficient, non-metabolic diabetes, in order to improve feasibility of identifying these patients through detailed chart review.MethodsPatients with likely T2D were selected using a validated machine-learning (ML) algorithm applied to EHR data. "Typical" T2D cases were removed by excluding individuals with obesity, evidence of dyslipidemia, antibody-positive diabetes, or cystic fibrosis. To filter out likely type 1 diabetes (T1D) cases, we applied six additional "branch algorithms," relying on various clinical characteristics, which resulted in six overlapping cohorts. Diabetes type was classified by manual chart review as atypical, not atypical, or indeterminate due to missing information.ResultsOf 114,975 biobank participants, the algorithms collectively identified 119 (0.1%) potential AD cases, of which 16 (0.014%) were confirmed after expert review. The branch algorithm that excluded T1D based on outpatient insulin use had the highest percentage yield of AD (13 of 27; 48.2% yield). Together, the 16 AD cases had significantly lower BMI and higher HDL than either unselected T1D or T2D cases identified by ML algorithms (P<0.05). Compared to the ML T1D group, the AD group had a significantly higher T2D polygenic score (P<0.01) and lower hemoglobin A1c (P<0.01).ConclusionOur EHR-based algorithms followed by manual chart review identified collectively 16 individuals with AD, representing 0.22% of biobank enrollees with T2D. With a maximum yield of 48% cases after manual chart review, our algorithms have the potential to drastically improve efficiency of AD identification. Recognizing patients with AD may inform on the heterogeneity of T2D and facilitate enrollment in studies like the Rare and Atypical Diabetes Network (RADIANT).

Dataset Information

Automatic population of eMeasurements from EHR systems for inpatient falls.

Publications

Automatic population of eMeasurements from EHR systems for inpatient falls.

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