BACKGROUND: Administrative data are used to determine performance for publicly reported in health plan "report cards," accreditation status, and reimbursement. However, it is unclear how performance based on administrative data and medical records compare. METHODS: We compared applicability, eligibility, and performance on 182 measures of health care quality using medical records and administrative data during a 13-month period for a random sample of 399 vulnerable older patients enrolled in managed care. RESULTS: Of 182 quality indicators (QIs) spanning 22 conditions, 145 (80%) were applicable only to medical records and 37 (20%) to either medical records or administrative data. Among 48 QIs specific to geriatric conditions, all were applicable to medical records; 2 of these also were applicable to administrative data. Eligibility for the 37 QIs that were applicable to both medical records and administrative data was similar for both data sources (94% agreement, kappa = 0.74). With the use of medical records, 152 of the 182 the QIs that were applicable to medical records were triggered and yielded an overall performance of 55%. Using administrative data, 30 of the 37 QIs that were applicable to administrative data were triggered and yielded overall performance of 83% (P < 0.05 vs. medical records). Restricting to QIs applicable to both data sources, overall performance was 84% and 83% (P = 0.21) for medical records and administrative data, respectively. CONCLUSIONS: The number and spectrum of QIs that can be measured for vulnerable elderly patients is far greater for medical records than for administrative data. Although summary estimates of health care quality derived from administrative data and medical records do not differ when using identical measures, summary scores from these data sources vary substantially when the totality of care that can be measured by each data source is measured.
BACKGROUND: Administrative data are used to determine performance for publicly reported in health plan "report cards," accreditation status, and reimbursement. However, it is unclear how performance based on administrative data and medical records compare. METHODS: We compared applicability, eligibility, and performance on 182 measures of health care quality using medical records and administrative data during a 13-month period for a random sample of 399 vulnerable older patients enrolled in managed care. RESULTS: Of 182 quality indicators (QIs) spanning 22 conditions, 145 (80%) were applicable only to medical records and 37 (20%) to either medical records or administrative data. Among 48 QIs specific to geriatric conditions, all were applicable to medical records; 2 of these also were applicable to administrative data. Eligibility for the 37 QIs that were applicable to both medical records and administrative data was similar for both data sources (94% agreement, kappa = 0.74). With the use of medical records, 152 of the 182 the QIs that were applicable to medical records were triggered and yielded an overall performance of 55%. Using administrative data, 30 of the 37 QIs that were applicable to administrative data were triggered and yielded overall performance of 83% (P < 0.05 vs. medical records). Restricting to QIs applicable to both data sources, overall performance was 84% and 83% (P = 0.21) for medical records and administrative data, respectively. CONCLUSIONS: The number and spectrum of QIs that can be measured for vulnerable elderly patients is far greater for medical records than for administrative data. Although summary estimates of health care quality derived from administrative data and medical records do not differ when using identical measures, summary scores from these data sources vary substantially when the totality of care that can be measured by each data source is measured.
Authors: John Heintzman; Steffani R Bailey; Megan J Hoopes; Thuy Le; Rachel Gold; Jean P O'Malley; Stuart Cowburn; Miguel Marino; Alex Krist; Jennifer E DeVoe Journal: J Am Med Inform Assoc Date: 2014-02-07 Impact factor: 4.497
Authors: William Gardner; Suzanne Morton; Sepheen C Byron; Aldo Tinoco; Benjamin D Canan; Karen Leonhart; Vivian Kong; Sarah Hudson Scholle Journal: Health Serv Res Date: 2014-01-29 Impact factor: 3.402
Authors: Steffani R Bailey; John D Heintzman; Miguel Marino; Megan J Hoopes; Brigit A Hatch; Rachel Gold; Stuart C Cowburn; Christine A Nelson; Heather E Angier; Jennifer E DeVoe Journal: Am J Prev Med Date: 2016-08-10 Impact factor: 5.043
Authors: Eric M Cheng; Carolyn J Crandall; Christopher T Bever; Barbara Giesser; Jodie K Haselkorn; Ron D Hays; Paul Shekelle; Barbara G Vickrey Journal: Mult Scler Date: 2010-06-18 Impact factor: 6.312