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

Author(s):  
Stephen D. Persell ◽  
Tiffany Brown ◽  
Jason N. Doctor ◽  
Craig R. Fox ◽  
Noah J. Goldstein ◽  
...  
2012 ◽  
Vol 42 (11) ◽  
pp. 51
Author(s):  
CHRISTOPHER NOTTE ◽  
NEIL SKOLNIK

2021 ◽  
Vol 12 ◽  
pp. 215145932199274
Author(s):  
Sanjit R. Konda ◽  
Joseph R. Johnson ◽  
Nicket Dedhia ◽  
Erin A. Kelly ◽  
Kenneth A. Egol

Introduction: This study sought to investigate whether a validated trauma triage tool can stratify hospital quality measures and inpatient cost for middle-aged and geriatric trauma patients with isolated proximal and midshaft humerus fractures. Materials and Methods: Patients aged 55 and older who sustained a proximal or midshaft humerus fracture and required inpatient treatment were included. Patient demographic, comorbidity, and injury severity information was used to calculate each patient’s Score for Trauma Triage in the Geriatric and Middle-Aged (STTGMA). Based on scores, patients were stratified to create minimal, low, moderate, and high risk groups. Outcomes included length of stay, complications, operative management, ICU/SDU-level care, discharge disposition, unplanned readmission, and index admission costs. Results: Seventy-four patients with 74 humerus fractures met final inclusion criteria. Fifty-eight (78.4%) patients presented with proximal humerus and 16 (21.6%) with midshaft humerus fractures. Mean length of stay was 5.5 ± 3.4 days with a significant difference among risk groups (P = 0.029). Lower risk patients were more likely to undergo surgical management (P = 0.015) while higher risk patients required more ICU/SDU-level care (P < 0.001). Twenty-six (70.3%) minimal risk patients were discharged home compared to zero high risk patients (P = 0.001). Higher risk patients experienced higher total inpatient costs across operative and nonoperative treatment groups. Conclusion: The STTGMA tool is able to reliably predict hospital quality measures and cost outcomes that may allow hospitals and providers to improve value-based care and clinical decision-making for patients presenting with proximal and midshaft humerus fractures. Level of Evidence: Prognostic Level III.


2021 ◽  
Vol 9 (3) ◽  
pp. e000853
Author(s):  
Michael Topmiller ◽  
Jessica McCann ◽  
Jennifer Rankin ◽  
Hank Hoang ◽  
Joshua Bolton ◽  
...  

ObjectiveThis paper explores the impact of service area-level social deprivation on health centre clinical quality measures.DesignCross-sectional data analysis of Health Resources and Services Administration (HRSA)-funded health centres. We created a weighted service area social deprivation score for HRSA-funded health centres as a proxy measure for social determinants of health, and then explored adjusted and unadjusted clinical quality measures by weighted service area Social Deprivation Index quartiles for health centres.SettingsHRSA-funded health centres in the USA.ParticipantsOur analysis included a subset of 1161 HRSA-funded health centres serving more than 22 million mostly low-income patients across the country.ResultsHigher levels of social deprivation are associated with statistically significant poorer outcomes for all clinical quality outcome measures (both unadjusted and adjusted), including rates of blood pressure control, uncontrolled diabetes and low birth weight. The adjusted and unadjusted results are mixed for clinical quality process measures as higher levels of social deprivation are associated with better quality for some measures including cervical cancer screening and child immunisation status but worse quality for other such as colorectal cancer screening and early entry into prenatal care.ConclusionsThis research highlights the importance of incorporating community characteristics when evaluating clinical outcomes. We also present an innovative method for capturing health centre service area-level social deprivation and exploring its relationship to health centre clinical quality measures.


2018 ◽  
Vol 34 (2) ◽  
pp. 119-126
Author(s):  
Christiane T. LaBonte ◽  
Perry Payne ◽  
William Rollow ◽  
Mark W. Smith ◽  
Abdul Nissar ◽  
...  

2019 ◽  
Vol 2 (8) ◽  
pp. e198569
Author(s):  
Kyle E. Knierim ◽  
Tristen L. Hall ◽  
L. Miriam Dickinson ◽  
Donald E. Nease ◽  
Dionisia R. de la Cerda ◽  
...  

2013 ◽  
Vol 158 (2) ◽  
pp. 77 ◽  
Author(s):  
Lisa M. Kern ◽  
Sameer Malhotra ◽  
Yolanda Barrón ◽  
Jill Quaresimo ◽  
Rina Dhopeshwarkar ◽  
...  

2020 ◽  
Vol 33 (4) ◽  
pp. 620-625
Author(s):  
Michael L. Parchman ◽  
Melissa L. Anderson ◽  
Robert B. Penfold ◽  
Elena Kuo ◽  
David A. Dorr

2020 ◽  
Vol 11 (01) ◽  
pp. 023-033
Author(s):  
Robert C. McClure ◽  
Caroline L. Macumber ◽  
Julia L. Skapik ◽  
Anne Marie Smith

Abstract 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.


PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242844
Author(s):  
Nadereh Pourat ◽  
Xiao Chen ◽  
Connie Lu ◽  
Weihao Zhou ◽  
Hank Hoang ◽  
...  

Background In the United States, there are nearly 1,400 Health Resources and Services Administration-funded health centers (HCs) serving low-income and underserved populations and more than 600 of these HCs are located in rural areas. Disparities in quality of medical care in urban vs. rural areas exist but data on such differences between urban and rural HCs is limited in the literature. We examined whether urban and rural HCs differed in their performance on clinical quality measures before and after controlling for patient, organizational, and contextual characteristics. Methods and findings We used the 2017 Uniform Data System to examine performance on clinical quality measures between urban and rural HCs (n = 1,373). We used generalized linear regression models with the logit link function and binomial distribution, controlling for confounding factors. After adjusting for potential confounders, we found on par performance between urban and rural HCs in all but one clinical quality measure. Rural HCs had lower rates of linking patients newly diagnosed with HIV to care (74% [95% CI: 69%, 80%] vs. 83% [95% CI: 80%, 86%]). We identified control variables that systematically accounted for eliminating urban vs. rural differences in performance on clinical quality measures. We also found that both urban and rural HCs had some clinical quality performance measures that were lower than available national benchmarks. Main limitations included potential discrepancy of urban or rural designation across all HC sites within a HC organization. Conclusions Findings highlight HCs’ contributions in addressing rural disparities in quality of care and identify opportunities for improvement. Performance in both rural and urban HCs may be improved by supporting programs that increase the availability of providers, training, and provision of technical resources.


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