Electronic Clinical Quality Measures Research Articles

Sustainable Approach to Justice, Equity, Diversity, and Inclusion Through Better Quality Measurement.

The US health care industry has broadly adopted performance and quality measures that are extracted from electronic health records and connected to payment incentives that hope to improve declining life expectancy and health status and reduce costs. While the development of a quality measurement infrastructure based on electronic health record data was an important first step in addressing US health outcomes, these metrics, reflecting the average performance across diverse populations, do not adequately adjust for population demographic differences, social determinants of health, or ecosystem vulnerability. Like society as a whole, health care must confront the powerful impact that social determinants of health, race, ethnicity, and other demographic variations have on key health care performance indicators and quality metrics. Tools that are currently available to capture and report the health status of Americans lack the granularity, complexity, and standardization needed to improve health and address disparities at the local level. In this article, we discuss the current and future state of electronic clinical quality measures through a lens of equity.

Cost Savings Associated With Implementing 4 Total Joint Replacement Electronic Clinical Quality Measures Nationally: 2020-2040.

Digital transformation using widely available electronic data is a key component to improving health outcomes and customer choice and decreasing cost and measurement burden. Despite these benefits, existing information on the potential cost savings from electronic clinical quality measures (eCQMs) is limited. We assessed the costs of implementing 4 eCQMs related to total hip and/or total knee arthroplasty into electronic health record systems across healthcare systems in the United States. We used published literature and technical expert panel consultation to calculate low-, mid-, and high-range hip and knee arthroplasty surgery projections, and used empirical testing, literature, and technical expert panel consultation to develop an economic model to assess projected cost savings of eCQMs when implemented nationally. Low-, mid-, and high-range projected cost savings for year's 2020, 2030, and 2040 were calculated for 4 orthopedic eCQMs. Mid-range projected cost savings for 2020 ranged from $7.9 to $31.9 million per measure per year. A breakeven of between 0.5% and 5.1% of adverse events (measure dependent) must be averted for cost savings to outweigh implementation costs. All measures demonstrated potential cost savings. These findings suggest that eCQMs have the potential to lower healthcare costs and improve patient outcomes without adding to physician documentation burden. The Centers for Medicare and Medicaid Services' investment in eCQMs is an opportunity to reduce adverse outcomes and excess costs in orthopedics.

CodeX quality measures for cancer: Leveraging FHIR and mCODE to support digital quality measures.

466 Background: CodeX is a member-driven Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) Accelerator, building a community to fast-track interoperable data modeling and implementation of new FHIR data standards leading to step-change improvements in patient care and research. The Quality Measures for Cancer project asks the question, “Can oncology quality measures be effectively authored and executed using minimal Common Oncology Data Elements (mCODE) — an initiative intended to assemble a core set of structured data elements for oncology electronic health records (EHRs) — and FHIR, given that current measures must be manually collected and abstracted?” The Quality Measures for Cancer project aims to provide a less burdensome path for all parties involved in the quality measure lifecycle to share standardized oncology quality measure data. Methods: The Quality Measures for Cancer project has assembled a team of leading healthcare professionals from American Society of Clinical Oncology (ASCO), American Society for Radiation Oncology (ASTRO), Telligen, Evernorth, and MITRE to create a solution that demonstrates the ability to author and evaluate digital quality measures using FHIR standards, along with mCODE profiles and extensions, for value-based programs and clinical quality improvement in the oncology domain. More specifically, the initial project scope focuses efforts to: Develop a process for identifying the required discrete data to be used for data collection and aggregation; and prove that measures can be authored, generate accurate results, and can be executed using mCODE and FHIR. Results: Over the first year of project work, the Quality Measures for Cancer team has demonstrated that mCODE and FHIR can be used to author and execute oncology measures. The team has fully authored and tested two proof of concept ASCO antiemetic electronic clinical quality measures (eCQMs) using mCODE, FHIR, and the Quality Measures Implementation Guide. A demonstration is available to show the team's recent success with testing the feasibility and value of authoring and executing the two ASCO antiemetic eCQMs using mCODE and FHIR, evaluating the measure, and discussing how burden is reduced for providers, as well as submitters and receivers of quality measures. Conclusions: Long-term, the CodeX Quality Measures for Cancer team posits that by leveraging mCODE and FHIR to assist in the development and authoring of innovative oncology quality measures, real and impactful insight and assessment into true quality measures could be made if mCODE and other FHIR Implementation Guides are implemented and leveraged by an ecosystem of health organizations. This project is acting as an exemplar showing what can be achieved by leveraging several quality-specific FHIR Implementation Guides to create and roll out new, innovative quality measures.

Electronic Clinical Quality Measures of Health Information Technology and Hospital Performance: Evidence of U.S. Hospitals

Electronic Clinical Quality Measures of Health Information Technology and Hospital Performance: Evidence of U.S. Hospitals

Adapted Kaizen: Multi-Organizational Complex Process Redesign for Adapting Clinical Guidelines for the Digital Age.

The need for a method to examine complex, multidisciplinary processes involving many diverse organizations initially led multiple US federal agencies to adopt the traditional Kaizen, a Lean process improvement method typically used within a single organization, to encompass multiple organizations each with its own leadership and priorities. First, the Centers for Medicare and Medicaid Services and the Office of the National Coordinator for Health Information Technology adapted Kaizen to federal agency processes for the development of electronic clinical quality measures. Later, the Centers for Disease Control and Prevention (CDC) further modified this adapted Kaizen during its Adapting Clinical Guidelines for the Digital Age (ACG) initiative, which aimed to improve the broader scope of guideline development and implementation. This is a methods article to document the adapted Kaizen method for future use in similar complex processes, illustrating how to apply the adapted Kaizen through CDC's ACG initiative and showing the reach achieved by using the adapted Kaizen method. The adapted Kaizen includes pre-Kaizen planning, a Kaizen event, and post-Kaizen implementation that accommodate multidisciplinary and multi-organizational participation. ACG included 5 workgroups that each developed products to support their respective scope: Guideline Creation, Informatics Framework, Translation and Implementation, Communication and Dissemination, and Evaluation. Despite challenges gathering diverse perspectives and balancing the competing priorities of multiple organizations, the ACG participants produced interrelated standards, processes, and tools-further described in separate publications-that programs and partners have leveraged. Use of a siloed approach may not have supported the development and dissemination of these products.

Developing electronic clinical quality measures to assess the cancer diagnostic process.

Measures of diagnostic performance in cancer are underdeveloped. Electronic clinical quality measures (eCQMs) to assess quality of cancer diagnosis could help quantify and improve diagnostic performance. We developed 2 eCQMs to assess diagnostic evaluation of red-flag clinical findings for colorectal (CRC; based on abnormal stool-based cancer screening tests or labs suggestive of iron deficiency anemia) and lung (abnormal chest imaging) cancer. The 2 eCQMs quantified rates of red-flag follow-up in CRC and lung cancer using electronic health record data repositories at 2 large healthcare systems. Each measure used clinical data to identify abnormal results, evidence of appropriate follow-up, and exclusions that signified follow-up was unnecessary. Clinicians reviewed 100 positive and 20 negative randomly selected records for each eCQM at each site to validate accuracy and categorized missed opportunities related to system, provider, or patient factors. We implemented the CRC eCQM at both sites, while the lung cancer eCQM was only implemented at the VA due to lack of structured data indicating level of cancer suspicion on most chest imaging results at Geisinger. For the CRC eCQM, the rate of appropriate follow-up was 36.0% (26 746/74 314 patients) in the VA after removing clinical exclusions and 41.1% at Geisinger (1009/2461 patients; P < .001). Similarly, the rate of appropriate evaluation for lung cancer in the VA was 61.5% (25 166/40 924 patients). Reviewers most frequently attributed missed opportunities at both sites to provider factors (84 of 157). We implemented 2 eCQMs to evaluate the diagnostic process in cancer at 2 large health systems. Health care organizations can use these eCQMs to monitor diagnostic performance related to cancer.

Centers for Medicare & Medicaid Services' Hospital Harm Measures for Severe Hypoglycemia and Hyperglycemia: Is Your Hospital Ready?

Poor inpatient glycemic management is associated with increased lengths of stay and in-hospital morbidity and mortality. Improving inpatient glycemic outcomes can be difficult because there are no standardized benchmarks, and many hospitals lack the capacity to electronically extract and analyze glucose data. The Centers for Medicare & Medicaid Services recently proposed new electronic clinical quality measures to be incorporated into its mandatory Hospital Inpatient Quality Reporting Program. Among these measures is an assessment of hospital harm from severe hypoglycemia and severe hyperglycemia. Hospitals must be ready to collect the necessary data for these new measures by January 2023. The new measures could bring welcome attention to the need to implement guideline-based inpatient glycemic management. However, some hospitals that serve high-risk populations may be at risk for losing funding if they are unable to comply.

Reprint of: Development and Evaluation of a Global Malnutrition Composite Score.

Reprint of: Development and Evaluation of a Global Malnutrition Composite Score.

Generating and Reporting Electronic Clinical Quality Measures from Electronic Health Records: Strategies from EvidenceNOW Cooperatives.

Electronic clinical quality measures (eCQMs) from electronic health records (EHRs) are a key component of quality improvement (QI) initiatives in small-to-medium size primary care practices, but using eCQMs for QI can be challenging. Organizational strategies are needed to effectively operationalize eCQMs for QI in these practice settings. This study aimed to characterize strategies that seven regional cooperatives participating in the EvidenceNOW initiative developed to generate and report EHR-based eCQMs for QI in small-to-medium size practices. A qualitative study comprised of 17 interviews with representatives from all seven EvidenceNOW cooperatives was conducted. Interviewees included administrators were with both strategic and cooperative-level operational responsibilities and external practice facilitators were with hands-on experience helping practices use EHRs and eCQMs. A subteam conducted 1-hour semistructured telephone interviews with administrators and practice facilitators, then analyzed interview transcripts using immersion crystallization. The analysis and a conceptual model were vetted and approved by the larger group of coauthors. Cooperative strategies consisted of efforts in four key domains. First, cooperative adaptation shaped overall strategies for calculating eCQMs whether using EHRs, a centralized source, or a "hybrid strategy" of the two. Second, the eCQM generation described how EHR data were extracted, validated, and reported for calculating eCQMs. Third, practice facilitation characterized how facilitators with backgrounds in health information technology (IT) delivered services and solutions for data capture and quality and practice support. Fourth, performance reporting strategies and tools informed QI efforts and how cooperatives could alter their approaches to eCQMs. Cooperatives ultimately generated and reported eCQMs using hybrid strategies because they determined neither EHRs alone nor centralized sources alone could operationalize eCQMs for QI. This required cooperatives to devise solutions and utilize resources that often are unavailable to typical small-to-medium-sized practices. The experiences from EvidenceNOW cooperatives provide insights into how organizations can plan for challenges and operationalize EHR-based eCQMs.

Development of High-Risk Geriatric Polypharmacy Electronic Clinical Quality Measures and a Pilot Test of EHR Nudges Based on These Measures.

Inappropriate polypharmacy, prevalent among older patients, is associated with substantial harms. To develop measures of high-risk polypharmacy and pilot test novel electronic health record (EHR)-based nudges grounded in behavioral science to promote deprescribing. We developed and validated seven measures, then conducted a three-arm pilot from February to May 2019. Validation used data from 78,880 patients from a single large health system. Six physicians were pre-pilot test environment users. Sixty-nine physicians participated in the pilot. Rate of high-risk polypharmacy among patients aged 65 years or older. High-risk polypharmacy was defined as being prescribed ≥5 medications and satisfying ≥1 of the following high-risk criteria: drugs that increase fall risk among patients with fall history; drug-drug interactions that increase fall risk; thiazolidinedione, NSAID, or non-dihydropyridine calcium channel blocker in heart failure; and glyburide, glimepiride, or NSAID in chronic kidney disease. Physicians received EHR alerts when renewing or prescribing certain high-risk medications when criteria were met. One practice received a "commitment nudge" that offered a chance to commit to addressing high-risk polypharmacy at the next visit. One practice received a "justification nudge" that asked for a reason when high-risk polypharmacy was present. One practice received both. Among 55,107 patients 65 and older prescribed 5 or more medications, 6256 (7.9%) had one or more high-risk criteria. During the pilot, the mean (SD) number of nudges per physician per week was 1.7 (0.4) for commitment, 0.8 (0.5) for justification, and 1.9 (0.5) for both interventions. Physicians requested to be reminded to address high-risk polypharmacy for 236/833 (28.3%) of the commitment nudges and acknowledged 441 of 460 (95.9%) of justification nudges, providing a text response for 187 (40.7%). EHR-based measures and nudges addressing high-risk polypharmacy were feasible to develop and implement, and warrant further testing.

Comparison of electronic versus manual abstraction for 2 standardized perinatal care measures.

Given that electronic clinical quality measures (eCQMs) are playing a central role in quality improvement applications nationwide, a stronger evidence base demonstrating their reliability is critically needed. To assess the reliability of electronic health record-extracted data elements and measure results for the Elective Delivery and Exclusive Breast Milk Feeding measures (vs manual abstraction) among a national sample of US acute care hospitals, as well as common sources of discrepancies and change over time. eCQM and chart-abstracted data for the same patients were matched and compared at the data element and measure level for hospitals submitting both sources of data to The Joint Commission between 2017 and 2019. Sensitivity, specificity, and kappa statistics were used to assess reliability. Although eCQM denominator reliability had moderate to substantial agreement for both measures and both improved over time (Elective Delivery: kappa = 0.59 [95% confidence interval (CI), 0.58-0.61] in 2017 and 0.84 [95% CI, 083-0.85] in 2019; Exclusive Breast Milk Feeding: kappa = 0.58 [95% CI, 0.54-0.62] in 2017 and 0.70 [95% CI, 0.67-0.73] in 2019), the numerator status reliability was poor for Elective Delivery (kappa = 0.08 [95% CI, 0.03-0.12] in 2017 and 0.10 [95% CI, 0.05-0.15] in 2019) but near perfect for Exclusive Breast Milk Feeding (kappa = 0.85 [0.83, 0.87] in 2017 and 0.84 [0.83, 0.85] in 2019). The failure of the eCQM to accurately capture estimated gestational age, conditions possibly justifying elective delivery, active labor, and medical induction were the main reasons for the discrepancies. Although eCQM denominator reliability for the Elective Delivery and Exclusive Breast Milk Feeding measures had moderate agreement when compared to medical record review, the numerator status reliability was poor for Elective Delivery, but near perfect for Exclusive Breast Milk Feeding. Improvements in eCQM data capture of some key data elements would greatly improve the reliability.

Preliminary study of patient safety and quality use cases for ICD-11 MMS.

This study investigated how well-suited the International Classification of Diseases, 11th Revision, for Mortality and Morbidity Statistics, (ICD-11 MMS) is for 2 morbidity use cases, patient safety and quality, examining the level of detail captured, and evaluating the necessity for the development of a US clinical modification (CM). Utilizing the 5 NCVHS-specified perspectives plus the consumer perspective, a framework was created of International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) use cases. Analysis yielded candidate source criteria for use in case evaluation. Patient safety and quality were chosen because they are relevant across all perspectives.Granularity differences and content coverage of ICD-11 MMS entities were assessed pre- and post-coordination to determine suitability for the 2 use cases. Pressure ulcers, a common condition across 3 patient safety applications, became the focus for comparing ICD-10-CM codes to ICD-11 MMS codes. For 3 electronic clinical quality measures (eCQMs), the evaluation centered on specified value sets for ischemic stroke, hypertension, and diabetes. For pressure ulcers, the ICD-11 MMS was found to exceed ICD-10-CM capabilities via post-coordinated extension codes. For the 3 eCQM value sets explored, the ICD-11 MMS fully represented the disease concepts when post-coordinated code clusters were used. The examples from the patient safety and quality use cases evaluated in this study are appropriate for ICD-11 MMS. It captures greater detail than ICD-10-CM, and ICD-11 MMS specificity would benefit both use cases. The authors believe this preliminary study indicates the US should invest resources to explore adopting the WHO ICD-11 MMS and tooling and guidelines to implement post-coordination.

Development and Evaluation of a Global Malnutrition Composite Score

Development and Evaluation of a Global Malnutrition Composite Score

How a Malnutrition Quality Improvement Initiative Furthers Malnutrition Measurement and Care: Results From a Hospital Learning Collaborative.

BackgroundMalnutrition in hospitalized patients can adversely affect health outcomes and increase the cost of care. Real‐world strategies are needed for prompt identification and treatment of patients at risk of malnutrition.ObjectivesThe aim of this quality improvement (QI) study was to measure the impact of a nutrition‐focused program on the malnutrition care processes of participating hospitals. Secondary objectives were to determine whether improvements in these nutrition‐related processes reduced hospital readmissions and length of stay (LOS) in patients ≥65 years.MethodsA group of 27 US hospitals (“The Collaborative”) implemented the Malnutrition Quality Improvement Initiative (MQii), as guided by a Malnutrition QI Toolkit and 4 electronic clinical quality measures (eCQMs), including (1) nutrition screening; (2) nutrition assessment following detection of malnutrition risk; (3) nutrition care plan for patients identified as malnourished after completed nutrition assessment; and (4) documentation of malnutrition diagnoses. Multivariate analyses identified the variables best correlated with patient outcomes.ResultsImprovements were observed for all 4 eCQMs. The greatest improvements were achieved as a result of timely nutrition assessment (P = .06) and malnutrition diagnosis (P = .02). Patients ≥65 years with a malnutrition diagnosis and nutrition care plan had a 24% lower likelihood of 30‐day readmission but a longer mean LOS than did those without a care plan.ConclusionsIn this study, the implementation of MQii practices significantly improved the identification of malnutrition. The prompt identification and treatment of patients at malnutrition risk can improve patient care and health, as well as reduce costly readmissions.

The effect of number of healthcare visits on study sample selection in electronic health record data.

IntroductionFew studies have addressed how to select a study sample when using electronic health record (EHR) data.ObjectiveTo examine how changing criterion for number of visits in EHR data required for inclusion in a study sample would impact one basic epidemiologic measure: estimates of disease period prevalence.MethodsYear 2016 EHR data from three Midwestern health systems (Northwestern Medicine in Illinois, University of Iowa Health Care, and Froedtert & the Medical College of Wisconsin, all regional tertiary health care systems including hospitals and clinics) was used to examine how alternate definitions of the study sample, based on number of healthcare visits in one year, affected measures of disease period prevalence. In 2016, each of these health systems saw between 160,000 and 420,000 unique patients. Curated collections of ICD-9, ICD-10, and SNOMED codes (from CMS-approved electronic clinical quality measures) were used to define three diseases: acute myocardial infarction, asthma, and diabetic nephropathy). ResultsAcross all health systems, increasing the minimum required number of visits to be included in the study sample monotonically increased crude period prevalence estimates. The rate at which prevalence estimates increased with number of visits varied across sites and across diseases.ConclusionIn addition to providing thorough descriptions of case definitions, when using EHR data authors must carefully describe how a study sample is identified and report data for a range of sample definitions, including minimum number of visits, so that others can assess the sensitivity of reported results to sample definition in EHR data.Key wordsElectronic Health Records, Sampling Studies, Prevalence, Methods

Clinician-Level Variation in Three Measures Representing Overuse Based on the American Geriatrics Society Choosing Wisely Statement.

The extent of clinician-level variation in the overuse of testing or treatment in older adults is not well understood. To examine clinician-level variation for three new measures of potentially inappropriate use of medical services in older adults. Retrospective analysis of overall means and clinician-level variation in performance on three new measures. Adults aged 65years and older who had office visits with outpatient primary or immediate care clinicians within a single academic medical center health system between July 1, 2016, and June 30, 2017. Two electronic clinical quality measures representing potentially inappropriate use of medical services in older adults: prostate-specific antigen testing against guidelines (PSA) in men aged 76 and older; urinalysis or urine culture for non-specific reasons in women aged 65 and older; and one intermediate outcome measure: hemoglobin A1c less than 7.0 in adults aged 75 and older with diabetes mellitus treated with insulin or oral hypoglycemic medication. Sixty-nine clinicians and 2009 patients contributed observations to the PSA measure, 144 clinicians and 5933 patients contributed to the urinalysis/urine culture measure, and 42 clinicians and 665 patients contributed to the diabetes measure. Meaningful clinician-level performance variation was greatest for the PSA measure (intraclass correlation coefficient [ICC] = 0.27), followed by the urinalysis/urine culture measure (ICC = 0.18), and the diabetes measure (ICC = 0.024). The range of possible overuse across clinician quartiles was 8-54% for the PSA measure, 3-35% for the urinalysis/urine culture measure, and 13-49% for the diabetes measure. The odds ratios of overuse in the highest quartile compared with the lowest for the PSA, urinalysis/urine culture, and diabetes measures were 99.3 (95% CI 43 to 228), 15.7 (10 to 24), and 6.0 (3.3 to 11), respectively. Within the same health system, rates of potential overuse in elderly patients varied greatly across clinicians, particularly for the process measures examined.

Igniting Harmonized Digital Clinical Quality Measurement through Terminology, CQL, and FHIR.

Background Electronic clinical quality measures (eCQMs) seek to quantify the adherence of health care to evidence-based standards. This requires a high level of consistency to reduce the effort of data collection and ensure comparisons are valid. Yet, there is considerable variability in local data capture, in the use of data standards and in implemented documentation processes, so organizations struggle to implement quality measures and extract data reliably for comparison across patients, providers, and systems. Objective In this paper, we discuss opportunities for harmonization within and across eCQMs; specifically, at the level of the measure concept, the logical clauses or phrases, the data elements, and the codes and value sets. Methods The authors, experts in measure development, quality assurance, standards and implementation, reviewed measure structure and content to describe the state of the art for measure analysis and harmonization. Our review resulted in the identification of four measure component levels for harmonization. We provide examples for harmonization of each of the four measure components based on experience with current quality measurement programs including the Centers for Medicare and Medicaid Services eCQM programs. Results In general, there are significant issues with lack of harmonization across measure concepts, logical phrases, and data elements. This magnifies implementation problems, confuses users, and requires more elaborate data mapping and maintenance. Conclusion Comparisons using semantically equivalent data are needed to accurately measure performance and reduce workflow interruptions with the aim of reducing evidence-based care gaps. It comes as no surprise that electronic health record designed for purposes other than quality improvement and used within a fragmented care delivery system would benefit greatly from common data representation, measure harmony, and consistency. We suggest that by enabling measure authors and implementers to deliver consistent electronic quality measure content in four key areas; the industry can improve quality measurement.

Challenges to electronic clinical quality measurement using third-party platforms in primary care practices: the healthy hearts in the heartland experience.

Third-party platforms have emerged to support small primary care practices for calculating and reporting electronic clinical quality measures (eCQM) for federal programs like The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) and Merit-based Incentive Payment System (MIPS). Yet little is known about the capabilities and limitations of electronic health record systems (EHRs) to enable data access for these programs. We connected 116 small- to medium-sized practices with seven different EHRs to popHealth, an open-source eCQM platform. We identified the prevalence of following problems with eCQM data for data extraction in seven different EHRs: (1) Lack of coded data in five of seven; (2) Incorrectly categorized data in four of seven; (3) Isosemantic data (data within the incorrect context) in four of seven; (4) Coding that could not be directly evaluated in six of seven; (5) Errors in date assignment and labeled as historical values in five of seven; and (6) Inadequate data to assign the correct code in two of seven. We recommend specific enhancements to EHR systems that can promote effective eCQM implementation and reporting to MACRA and MIPS.

Using Electronic Clinical Quality Measures (eCQMs) to Perform a Venous Thromboembolism Prophylaxis Rapid Cycle Quality Improvement Initiative

At one institution, a clinical decision support (CDS) alert for venous thromboembolism (VTE) prophylaxis burdened providers but was considered vital to patient safety. Electronic clinical quality measures (eCQMs) incentivized the translation of quality measures into data elements within the electronic health record (EHR) and facilitated hospitalwide performance monitoring during CDS improvement. The aim was to reduce VTE alerts by 50% without compromising eCQM performance. This quality improvement initiative was performed at a tertiary care academic medical center using an integrated EHR. Alert firings were revised in three rounds over a four-week transition period while monitoring VTE eCQM performance weekly. Postimplementation data were recorded for 12 weeks. Primary outcomes were VTE alerts per 100 admissions and VTE eCQM performance. Secondary outcomes were alert effectiveness (desired responses/patients), alert efficiency (desired responses/alerts), and dwell time (time between alert firing and provider addressing the alert). Alerts decreased from 157 to 74 per 100 admissions, a 52.9% reduction (p < 0.001). There was no change in eCQM compliance or the percentage of inpatients excluded from the VTE eCQM. Provider dwell time across the hospital dropped between 2.9 and 7.2 hours per day. After the interventions, alert effectiveness increased (66.1% to 73.3%; p < 0.001), but alert efficiency decreased (17.5% to 16.2%; p = 0.007) due to an increase in providers delaying definitive responses. Altering VTE alert criteria did not affect compliance with providing VTE prophylaxis to patients while reducing alert burden by more than 50%. Using preexisting quality data like eCQMs can facilitate near-time patient safety monitoring during quality improvement projects.

Nurses' Attitudes Toward Electronic Clinical Quality Measures: A Descriptive Study.

Electronic clinical quality measures (eCQMs) are a method that automatically extract data from electronic health records (EHRs) and compute and generate the results to report and track the quality of care and patient outcomes. The purpose of this study was to explore nurses' attitudes toward eCQMs and the factors influencing this attitude. A descriptive cross-sectional study was conducted using a closed-ended questions survey of 92 nurses in a teaching hospital. The average score for nurses' attitudes toward eCQMs was 3.47 out of 4. Participants with a master's degree had more positive attitudes than those with a baccalaureate degree. Head nurses had more positive attitudes than staff nurses. The nurses in the study hospital have a positive attitude toward eCQMs. Health care organizations should strengthen the attitudes of nurses toward eCQMs.