Literature DB >> 19330887

Improving hypertension quality measurement using electronic health records.

Stephen D Persell1, Abel N Kho, Jason A Thompson, David W Baker.   

Abstract

BACKGROUND: Simple hypertension outcome measures may not indicate which patients receive poor care. This could be problematic as incentives increase.
OBJECTIVE: Compare measured quality using simple outcome measures to more sophisticated measures utilizing data available within an electronic health record.
DESIGN: Cross-sectional study.
SUBJECTS: A total of 5905 hypertensive adults with 3 or more clinic visits between July 1, 2005 and December 31, 2006 at an internal medicine clinic. MEASURES: We measured simple control as the proportion of diagnosed hypertension patients with their last blood pressure below goal (<140/90 mm Hg or <130/80 if diabetic). We compared this to sequentially more complex measures.
RESULTS: Among nondiabetic patients, baseline measurement of control was 58.1% [95% confidence interval (CI), 56.5-59.6]. Counting patients as having adequate care whose last or mean blood pressure was at or below goal raised performance to 75.4%. Accounting for patients prescribed aggressive treatment raised it to 82.5%. Accounting for low diastolic blood pressure raised it to 83.6%. Including patients with undiagnosed hypertension lowered it to 80.5%. For diabetes patients, baseline measurement of control was 29.9% (95% CI, 27.6-32.3) and changed to 46.4%, 72.8%, 76.7%, and 73.6%, respectively.
CONCLUSIONS: It is possible to use electronic health record data to devise hypertension measures that may better reflect who has actionable uncontrolled blood pressure, do not penalize clinicians treating resistant hypertension patients, reduce the encouragement of potentially unsafe practices, and identify patients possibly receiving poor care with no hypertension diagnosis. This could improve the detection of true quality problems and remove incentives to over treat or stop caring for patients with resistant hypertension.

Entities:  

Mesh:

Year:  2009        PMID: 19330887     DOI: 10.1097/mlr.0b013e31818b070c

Source DB:  PubMed          Journal:  Med Care        ISSN: 0025-7079            Impact factor:   2.983


  15 in total

1.  Developing a natural language processing application for measuring the quality of colonoscopy procedures.

Authors:  Henk Harkema; Wendy W Chapman; Melissa Saul; Evan S Dellon; Robert E Schoen; Ateev Mehrotra
Journal:  J Am Med Inform Assoc       Date:  2011-09-21       Impact factor: 4.497

2.  Mining Electronic Health Records to Extract Patient-Centered Outcomes Following Prostate Cancer Treatment.

Authors:  Tina Hernandez-Boussard; Panagiotis D Kourdis; Tina Seto; Michelle Ferrari; Douglas W Blayney; Daniel Rubin; James D Brooks
Journal:  AMIA Annu Symp Proc       Date:  2018-04-16

3.  A technology-based quality innovation to identify undiagnosed hypertension among active primary care patients.

Authors:  Michael K Rakotz; Bernard G Ewigman; Menaka Sarav; Ruth E Ross; Ari Robicsek; Chad W Konchak; Thomas F Gavagan; David W Baker; David J Hyman; Kenneth P Anderson; Christopher M Masi
Journal:  Ann Fam Med       Date:  2014-07       Impact factor: 5.166

4.  Tracking the delivery of prevention-oriented care among primary care providers who have adopted electronic health records.

Authors:  Samantha F De Leon; Sarah C Shih
Journal:  J Am Med Inform Assoc       Date:  2011-08-19       Impact factor: 4.497

5.  Meaningful measurement: developing a measurement system to improve blood pressure control in patients with chronic kidney disease.

Authors:  Jeffrey O Greenberg; Nirav Vakharia; Lara E Szent-Gyorgyi; Sonali P Desai; Alexander Turchin; John Forman; Joseph V Bonventre; Allen Kachalia
Journal:  J Am Med Inform Assoc       Date:  2013-01-23       Impact factor: 4.497

6.  A clinically guided approach for improving performance measurement for hypertension.

Authors:  Michael A Steinman; Sei J Lee; Carolyn A Peterson; Kathy Z Fung; Mary K Goldstein
Journal:  Med Care       Date:  2012-05       Impact factor: 2.983

7.  Identifying patients with hypertension: a case for auditing electronic health record data.

Authors:  Adam Baus; Michael Hendryx; Cecil Pollard
Journal:  Perspect Health Inf Manag       Date:  2012-04-01

8.  Improving the quality of quality measurement: the tinkerer, the tailor and the candlestick maker.

Authors:  Monika M Safford
Journal:  Med Care       Date:  2009-04       Impact factor: 2.983

9.  Performance measurement for people with multiple chronic conditions: conceptual model.

Authors:  Erin R Giovannetti; Sydney Dy; Bruce Leff; Christine Weston; Karen Adams; Tom B Valuck; Aisha T Pittman; Caroline S Blaum; Barbara A McCann; Cynthia M Boyd
Journal:  Am J Manag Care       Date:  2013-10-01       Impact factor: 2.229

10.  Accuracy of blood pressure measurements reported in an electronic medical record during routine primary care visits.

Authors:  Paul A Fishman; Melissa L Anderson; Andrea J Cook; James D Ralston; Sheryl L Catz; Jim Carlson; Eric B Larson; Beverly B Green
Journal:  J Clin Hypertens (Greenwich)       Date:  2011-09-13       Impact factor: 3.738

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.