Clinical Quality Measures Research Articles

Using a stakeholder-engaged approach to develop and validate electronic clinical quality measures.

To describe the stakeholder-engaged processes used to develop, specify, and validate 2 oral health care electronic clinical quality measures. A broad range of stakeholders were engaged from conception through testing to develop measures and test feasibility, reliability, and validity following National Quality Forum guidance. We assessed data element feasibility through semistructured interviews with key stakeholders using a National Quality Forum-recommended scorecard. We created test datasets of synthetic patients to test measure implementation feasibility and reliability within and across electronic health record (EHR) systems. We validated implementation with automated reporting of EHR clinical data against manual record reviews, using the kappa statistic. A stakeholder workgroup was formed and guided all development and testing processes. All critical data elements passed feasibility testing. Four test datasets, representing 577 synthetic patients, were developed and implemented within EHR vendors' software, demonstrating measure implementation feasibility. Measure reliability and validity were established through implementation at clinical practice sites, with kappa statistic values in the "almost perfect" agreement range of 0.80-0.99 for all but 1 measure component, which demonstrated "substantial" agreement. The 2 validated measures were published in the United States Health Information Knowledgebase. The stakeholder-engaged processes used in this study facilitated a successful measure development and testing cycle. Engaging stakeholders early and throughout development and testing promotes early identification of and attention to potential threats to feasibility, reliability, and validity, thereby averting significant resource investments that are unlikely to be fruitful.

Evidence for the Contemporary Clinical Pathway Quality Measures: Literature Review

Objectives: Clinical pathway effect on improving the healthcare quality still underestimated. An overview on clinical pathway current practice overview will be presented in the context of this paper. Methods/Statistical Analysis: A search for the medical literature was conducted to screen the contemporary evidence. We focused on finding the gold standard for clinical pathway quality measures and the current status of clinical pathway in Malaysia. Various databases (MEDLINE, EMBASE, Pub Med, COHRANCE library and CINAHEL) have been screened for 2000 to 2015 studies. Checklist of the preferred reporting items for systematic reviews and meta-analyses has been used as a guide for reporting. Findings: The search has identified 720 documents, of which 315 (44%) met the inclusion criteria of this study. Twenty meta-analysis studies have been found, while only nine have completely fulfilled the search inclusion criteria. Mostly they discussed the length of stay, patient outcome and cost from RCT (Randomized Clinical Trial) or EXP (experimental design) evidence as a quality indicator for the clinical pathway which may under represent the complexity nature of clinical pathway and ignored many aspects of care. The majority of publications in Malaysia found from (UKM) with similar heterogeneous focus on the outcome measures to what have been found in the overall literature. Interestingly, we found that no meta-analysis study has been conducted in Malaysia for clinical pathway. Application/Improvements: Current research on clinical pathway still limited. More Studies on the effect of clinical pathway on quality of care still needed with emphasize on the cofounding factors.

Methods for Developing the American College of Rheumatology's Electronic Clinical Quality Measures.

Methods for Developing the American College of Rheumatology's Electronic Clinical Quality Measures.

PS 11-27 CARDIOMETABOLIC RISK GUIDELINES IN PRIMARY CARE

Objective: Cardiometabolic risk (CMR), such as hypertension, diabetes mellitus, dyslipidemia, obesity and smoking used to coexist. More than half of patients are presenting with 3 or more conditions in the primary care (PHC). Thus, it mandates primary care provider (PCP) to follow multiple guidelines for one patient. The complexity and multiplicity of these guidelines are major barriers to implementation. This effort tries to address these barriers, and develop a translational guideline for CMR management. Design and Method: The process involved a broad group of PHC professionals, including physicians, nurses, pharmacists, educationists, dietitians and guideline developers. Key Evidence-based reviews, meta-analyses and guidelines from the last 5 years were used as reference. ADAPTE methods and AGREE instrument were used in the development and assessment of each aspect of the guidelines, respectively. Results: The scope of the guideline is to provide a comprehensive approach to the management of CMR factors; include nutrition therapy, physical activity recommendations, pharmacologic therapy, self-management, as well as prevention and diagnosis of CMR-associated complications; provide suggestions to the management of the delivery system, the clinical information and the quality of care. Sections of the guideline include Methodology; General Algorithms; Screening; Assessment; Control; Non-pharmacologic management; Tools and tips for the PHC provider; and Quality Management. Self-management, screening of depression, building a nurse-led clinic, effective use of clinical information and quality measures are among the tools provided. The guideline pays special attention to CVR reduction, foot care, renal assessment, and screen of all > 45 years of age and obese for CMR. Conclusions: Cardiometabolic risk Guideline is a novel tool to overcome complexity of taking care after hypertensive patients and its comorbidities. It offers a common reference for assessment, management and total quality care.

New Unintended Adverse Consequences of Electronic Health Records.

Although the health information technology industry has made considerable progress in the design, development, implementation, and use of electronic health records (EHRs), the lofty expectations of the early pioneers have not been met. In 2006, the Provider Order Entry Team at Oregon Health & Science University described a set of unintended adverse consequences (UACs), or unpredictable, emergent problems associated with computer-based provider order entry implementation, use, and maintenance. Many of these originally identified UACs have not been completely addressed or alleviated, some have evolved over time, and some new ones have emerged as EHRs became more widely available. The rapid increase in the adoption of EHRs, coupled with the changes in the types and attitudes of clinical users, has led to several new UACs, specifically: complete clinical information unavailable at the point of care; lack of innovations to improve system usability leading to frustrating user experiences; inadvertent disclosure of large amounts of patient-specific information; increased focus on computer-based quality measurement negatively affecting clinical workflows and patient-provider interactions; information overload from marginally useful computer-generated data; and a decline in the development and use of internally-developed EHRs. While each of these new UACs poses significant challenges to EHR developers and users alike, they also offer many opportunities. The challenge for clinical informatics researchers is to continue to refine our current systems while exploring new methods of overcoming these challenges and developing innovations to improve EHR interoperability, usability, security, functionality, clinical quality measurement, and information summarization and display.

Commentaries on health services research

Commentaries on health services research

Health‐related quality of life at healing in individuals with chronic venous or mixed‐venous leg ulceration: a longitudinal assessment

To identify factors associated with longitudinal changes in health-related quality of life in community-dwelling individuals with venous or mixed-venous leg ulcers. Most care focuses primarily on healing; this research may additionally lead to strategies to improve quality of life. Data from four studies (2000-2009) were combined (2012) to examine quality of life in community-dwelling individuals referred for care of chronic leg wounds. Data collection occurred at baseline, 3, 6, 9 and 12months or at healing and included a comprehensive clinical assessment and measures of pain (McGill), functional autonomy (EQ-5D™ ) and quality of life (SF-12TM ). Physical Component Summary and Mental Component Summary scores (SF-12TM ) were categorized by whether or not there was an improvement (≥3 points) over time. Multivariable logistic regression modelling was used to identify factors associated with improvement. Mean age of this sample (n=519) was 67·5years, 55·9% were women. Median time to healing was 70days. Mean Physical Component Summary score increased from 37·0-41·5; factors associated with improvement included independent mobility, family history, problems with usual activities, fewer comorbidities and higher baseline pain. Mean Mental Component Summary score improved from 50·5-53·7; factors associated with improvement included anxiety or depression at baseline and living with others. Chronic leg ulceration has a substantial, negative impact on health-related quality of life. Ascertaining characteristics associated with changes in quality of life will contribute to the development of comprehensive strategies for prevention, care and improved quality of life.

Developing a data element repository to support EHR-driven phenotype algorithm authoring and execution

The Quality Data Model (QDM) is an information model developed by the National Quality Forum for representing electronic health record (EHR)-based electronic clinical quality measures (eCQMs). In conjunction with the HL7 Health Quality Measures Format (HQMF), QDM contains core elements that make it a promising model for representing EHR-driven phenotype algorithms for clinical research. However, the current QDM specification is available only as descriptive documents suitable for human readability and interpretation, but not for machine consumption. The objective of the present study is to develop and evaluate a data element repository (DER) for providing machine-readable QDM data element service APIs to support phenotype algorithm authoring and execution. We used the ISO/IEC 11179 metadata standard to capture the structure for each data element, and leverage Semantic Web technologies to facilitate semantic representation of these metadata. We observed there are a number of underspecified areas in the QDM, including the lack of model constraints and pre-defined value sets. We propose a harmonization with the models developed in HL7 Fast Healthcare Interoperability Resources (FHIR) and Clinical Information Modeling Initiatives (CIMI) to enhance the QDM specification and enable the extensibility and better coverage of the DER. We also compared the DER with the existing QDM implementation utilized within the Measure Authoring Tool (MAT) to demonstrate the scalability and extensibility of our DER-based approach.

Laboratory Order Errors Before and After Implementation of Electronic Health Record

An analysis of laboratory order entry errors on randomly selected inpatient records was conducted comparing errors 12 months before and after implementation of an electronic health record (EHR) at a 571-bed community health system. Methods: A total of 720 medical records were reviewed with 10,176 orders before EHR implementation and 11,455 orders after. Errors evaluated included unsigned, duplicate, illegible, and omitted orders, results with no order, and transcription errors. Data analysis included the independent-samples t-test and Pearson Chi-square test. Results: There was a significant difference in laboratory order entry errors before and after EHR implementation (p Conclusion: Implementation of an EHR appears to improve clinical laboratory order entry. ABBREVIATIONS:CMS- Centers for Medicare & Medicaid Services, CPOE- computerized physician order entry, CQM- clinical quality measures, EHR- electronic health record, LOS- length of stay, LIS- laboratory information system, HIS- hospital information system, HIM- health information management, HPF- horizon patient folder, QI- quality indicator, IQR- Interquartile Range.

Quality of life, well-being and wellness: Measuring subjective health for foods and other products

This paper introduces the conceptualization and measurement of quality of life, well-being, and wellness. Wellness, quality of life and well-being refer to the positive, subjective state that is opposite to illness. Thus, wellness is not the just absence of disease and the absence of illness; it is a separate positive state. Quality of life, well-being, and wellness are often discussed and described in terms of a multidimensional model. The strongest dimensions are physical, social, emotional/psychological, intellectual, and spiritual. The measurement of these positive dimensions of health have produced literally thousands of different measures, but most of them have been developed in a clinical setting and have been applied to specific disease conditions. Many of the existing clinical measures of wellness, well-being, and quality of life are very long, often over 100 items, and not suited to consumer research. Measures of quality of life and of well-being have focused on overall functioning. Quality of life of measures have been developed by the World Health Organization (WHO) and translated in many languages. Subjective well being has been defined as the combination of positive-negative affect balance and satisfaction with life, and is measured with two standard measures of these attributes. Wellness has largely been measured in the fields of clinical and counseling psychology; one new product oriented measure is the WellSense™ Profile (King et al., 2015). Wellness, well-being, and quality of life can be important additions to the measures studied in consumer perception of food and other consumer products.

2016 ACC/AHA Clinical Performance and Quality Measures for Adults With Atrial Fibrillation or Atrial Flutter: A Report of the American College of Cardiology/American Heart Association Task Force on Performance Measures.

The American College of Cardiology (ACC)/American Heart Association (AHA) clinical performance and quality measure sets serve as vehicles to accelerate translation of scientific evidence into clinical practice. Measure sets developed by the ACC/AHA are intended to provide practitioners and

Nursing Informatics Research Priorities for the Future: Recommendations from an International Survey.

We present one part of the results of an international survey exploring current and future nursing informatics (NI) research trends. The study was conducted by the International Medical Informatics Association Nursing Informatics Special Interest Group (IMIA-NISIG) Student Working Group. Based on findings from this cross-sectional study, we identified future NI research priorities. We used snowball sampling technique to reach respondents from academia and practice. Data were collected between August and September 2015. Altogether, 373 responses from 44 countries were analyzed. The identified top ten NI trends were big data science, standardized terminologies (clinical evaluation/implementation), education and competencies, clinical decision support, mobile health, usability, patient safety, data exchange and interoperability, patient engagement, and clinical quality measures. Acknowledging these research priorities can enhance successful future development of NI to better support clinicians and promote health internationally.

Improving Interprofessional and Coproductive Outcomes of Care for Patients with Chronic Obstructive Pulmonary Disease

In the U.S., suboptimal care quality for patients with chronic obstructive pulmonary disease (COPD) is reflected by high rates of emergency department visits and hospital readmissions, as well as excessive...

Using health information exchanges to calculate clinical quality measures: A study of barriers and facilitators

BackgroundA key motivation for the large national investment in electronic health record systems is to promote electronic reporting of quality measures that can be used as the basis for moving to value-based payment. Given the fragmented delivery system, robust quality reporting requires aggregating data across sites of care. Health information exchanges (HIEs) have emerged to facilitate exchange of clinical data across provider organizations and, therefore, should be well-positioned to support clinical quality measure reporting. MethodsBy interviewing representatives from 36 HIEs across the United States, we aimed to determine whether HIEs are capable of computing National Quality Forum measures for 6 cardiovascular disease preventive services. ResultsEleven HIEs (30%) reported computing at least one CQM; six computed one or more of the measures, and no HIE computed a measure in each of the 6 areas. Barriers to computing CQMs included data quality, completeness, sharing, and transmission issues; organizational structure, maturity, and sustainability issues; and vendor issues. ConclusionsThe ability to compute CQMs at the HIE level is still yet to be developed; currently, very few HIEs are able to do so for a variety of reasons. As HIE services expand and HIEs mature organizationally, the viability and utility of CQM reporting at the HIE level will increase. ImplicationsAs the healthcare system migrates towards a value-based payment system these broad challenges will need to be addressed. Level of EvidenceCross-sectional semi-structured qualitative interviews

The adoption of an electronic health record did not improve A1c values in Type 2 diabetes

BackgroundA major justification for the clinical adoption of electronic health records (EHRs) was the expectation that it would improve the quality of medical care. No longitudinal study has tested this...

The problem with red, amber, green: the need to avoid distraction by random variation in organisational performance measures

‘The Problem with…’ series covers controversial topics related to efforts to improve healthcare quality, including widely recommended but deceptively difficult strategies for improvement and pervasive problems that seem to resist...

Treatment of anxiety in patients with coronary heart disease: Rationale and design of the UNderstanding the benefits of exercise and escitalopram in anxious patients WIth coroNary heart Disease (UNWIND) randomized clinical trial

Anxiety is highly prevalent among patients with coronary heart disease (CHD), and there is growing evidence that high levels of anxiety are associated with worse prognosis. However, few studies have evaluated the efficacy of treating anxiety in CHD patients for reducing symptoms and improving clinical outcomes. Exercise and selective serotonin reuptake inhibitors have been shown to be effective in treating patients with depression, but have not been studied in cardiac patients with high anxiety. The UNWIND trial is a randomized clinical trial of patients with CHD who are at increased risk for adverse events because of comorbid anxiety. One hundred fifty participants with CHD and elevated anxiety symptoms and/or with a diagnosed anxiety disorder will be randomly assigned to 12 weeks of aerobic exercise (3×/wk, 35 min, 70%-85% VO2peak), escitalopram (5-20 mg qd), or placebo. Before and after 12 weeks of treatment, participants will undergo assessments of anxiety symptoms and CHD biomarkers of risk, including measures of inflammation, lipids, hemoglobin A1c, heart rate variability, and vascular endothelial function. Primary outcomes include post-intervention effects on symptoms of anxiety and CHD biomarkers. Secondary outcomes include clinical outcomes (cardiovascular hospitalizations and all-cause death) and measures of quality of life. The UNWIND trial (ClinicalTrials.gov NCT02516332) will evaluate the efficacy of aerobic exercise and escitalopram for improving anxiety symptoms and reducing risk for adverse clinical events in anxious CHD patients.

Validation of Stroke Meaningful Use Measures in a National Electronic Health Record System

The Meaningful Use (MU) program has increased the national emphasis on electronic measurement of hospital quality. To evaluate stroke MU and one VHA stroke electronic clinical quality measure (eCQM) in national VHA data and determine sources of error in using centralized electronic health record (EHR) data. Our study is a retrospective cross-sectional study of stroke quality measure eCQMs vs. chart review in a national EHR. We developed local SQL algorithms to generate the eCQMs, then modified them to run on VHA Central Data Warehouse (CDW) data. eCQM results were generated from CDW data in 2130 ischemic stroke admissions in 11 VHA hospitals. Local and CDW results were compared to chart review. We calculated the raw proportion of matching cases, sensitivity/specificity, and positive/negative predictive values (PPV/NPV) for the numerators and denominators of each eCQM. To assess overall agreement for each eCQM, we calculated a weighted kappa and prevalence-adjusted bias-adjusted kappa statistic for a three-level outcome: ineligible, eligible-passed, or eligible-failed. In five eCQMs, the proportion of matched cases between CDW and chart ranged from 95.4 %-99.7 % (denominators) and 87.7 %-97.9 % (numerators). PPVs tended to be higher (range 96.8 %-100 % in CDW) with NPVs less stable and lower. Prevalence-adjusted bias-adjusted kappas for overall agreement ranged from 0.73-0.95. Common errors included difficulty in identifying: (1) mechanical VTE prophylaxis devices, (2) hospice and other specific discharge disposition, and (3) contraindications to receiving care processes. Stroke MU indicators can be relatively accurately generated from existing EHR systems (nearly 90 % match to chart review), but accuracy decreases slightly in central compared to local data sources. To improve stroke MU measure accuracy, EHRs should include standardized data elements for devices, discharge disposition (including hospice and comfort care status), and recording contraindications.

The Nursing Home Compare Report Card: Perceptions of Residents and Caregivers Regarding Quality Ratings and Nursing Home Choice

To evaluate the perceived usefulness of publicly reported nursing home quality indicators. Primary data were collected from October 2013 to August 2014 among a convenience sample of persons (or family member) recently admitted or anticipating admission to a nursing home within 75 miles of the city of Philadelphia. Structured interviews were conducted to assess the salience of data on the Medicare Nursing Home Compare website, including star ratings, clinical quality measures, and benchmarking of individual nursing home quality with state and national data. Interviews were transcribed verbatim, independently coded by two reviewers, and agreement determined. A thematic analysis of transcripts was undertaken. Thirty-five interviews were completed. Eighty-three percent (n=29) were caregivers and 17 percent (n=6) were residents. Star ratings, clinical quality measures, and benchmarking information were salient to decision making, with preferred formats varying across participants. Participants desired additional information on the source of quality data. Confusion was evident regarding the relationship between domain-specific and overall star quality ratings. The Nursing Home Compare website provides salient content and formats for consumers. Increased awareness of this resource and clarity regarding the definition of measures could further support informed decision making regarding nursing home choice.

Green House Adoption and Nursing Home Quality.

To evaluate the impact of the Green House (GH) model on nursing home resident-level quality of care measures. Resident-level minimum data set (MDS) assessments merged with Medicare inpatient claims for the period 2005 through 2010. Using a difference-in-differences framework, we compared changes in care quality and outcomes in 15 nursing homes that adopted the GH model relative to changes over the same time period in 223 matched nursing homes that had not adopted the GH model. For individuals residing in GH homes, adoption of the model lowered readmissions and several MDS measures of poor quality, including bedfast residents, catheter use, and pressure ulcers, but these results were not present across the entire GH organization, suggesting possible offsetting effects for residents of non-GH "legacy" units within the GH organization. GH adoption led to improvement in rehospitalizations and certain nursing home quality measures for individuals residing in a GH home. The absence of evidence of a decline in other clinical quality measures in GH nursing homes should reassure anyone concerned that GH might have sacrificed clinical quality for improved quality of life.