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Cheryl L Damberg

Publications and source records attributed to Cheryl L Damberg.

8 recordsLinked to original sources
PubMed2006

What is the concordance between the medical record and patient self-report as data sources for ambulatory care?

BACKGROUND: The validity of quality of care assessments relies upon data quality, yet little is known about the relative completeness and validity of data sources for evaluating the quality of care. OBJECTIVES: We evaluated concordance between ambulatory medical record and patient survey data. Levels of concordance, variations by type of item, sources of disagreement between data sources, and implications for quality of care assessment efforts are discussed. DESIGN AND SUBJECTS: This was an observational study that included 1270 patients sampled from 39 West Coast medical organizations with at least 1 of the following: diabetes, ischemic heart disease, asthma or chronic obstructive pulmonary disease, or low back pain. MEASURES: Items from both data sources were grouped into 4 conceptual domains: diagnosis, clinical services delivered, counseling and referral, and medication use. We present total agreement, kappa, sensitivity, and specificity at the item and domain-levels and for all items combined. RESULTS: We found good concordance between survey and medical records overall, but there was substantial variation within and across domains. The worst concordance was in the counseling and referrals domain, the best in the medication use domain. Patients were able to report with good sensitivity on memorable items. CONCLUSIONS: Quality ratings are likely to vary in differing directions, depending on the data source used. The most appropriate data source for analyses of components of and overall quality of care must be considered in light of study objectives and resources. We recommend data collection from multiple sources to most accurately portray the patient and provider experience of medical care.

Adolescent↗
PubMed2006

Administrative versus clinical data for coronary artery bypass graft surgery report cards: the view from California.

OBJECTIVE: The objective of this study was to compare the performance of a risk model for isolated coronary artery bypass graft (CABG) surgery based on administrative data with that of a clinical risk model in predicting mortality and identifying hospital performance outliers. METHODS: Clinical data records from the California CABG Mortality Reporting Program for 38,230 isolated CABG patients undergoing surgery in 2000-2001 were linked to records in the California patient discharge data (PDD) abstract. Risk factors based on administrative data that mirrored clinical risk factors were developed using the condition present at admission indicator in the PDD to separate preoperative acute conditions from complications of surgery. Using logistic regression, risk model performance across data sources was compared along with hospital risk-adjusted mortality ranks and quality ratings. RESULTS: The administrative data showed lower prevalence of risk factors when compared with the clinical data. The clinical risk model had somewhat better discrimination (C = 0.824) than the administrative model (C = 0.799). The clinical model yielded 17 outliers and the administrative model 16 with agreement on 12 hospitals' status. Performance of the administrative risk model was minimally affected by removal of information from prior admissions and removal of risk factors not confirmed in the clinical record. CONCLUSIONS: Unique properties of the California administrative data, including the ability to distinguish acute preoperative risk factors from complications of surgery, permitted construction of an administrative risk model that predicts mortality on par with most published clinical models. Despite this, the administrative model identified slightly different hospital outliers, which may indicate somewhat biased assessments of hospital patient risk.

California↗
PubMed2005

Imputation of SF-12 health scores for respondents with partially missing data.

OBJECTIVE: To create an efficient imputation algorithm for imputing the SF-12 physical component summary (PCS) and mental component summary (MCS) scores when patients have one to eleven SF-12 items missing. STUDY SETTING: Primary data collection was performed between 1996 and 1998. STUDY DESIGN: Multi-pattern regression was conducted to impute the scores using only available SF-12 items (simple model), and then supplemented by demographics, smoking status and comorbidity (enhanced model) to increase the accuracy. A cut point of missing SF-12 items was determined for using the simple or the enhanced model. The algorithm was validated through simulation. DATA COLLECTION: Thirty-thousand-three-hundred and eight patients from 63 physician groups were surveyed for a quality of care study in 1996, which collected the SF-12 and other information. The patients were classified as "chronic" patients if they reported that they had diabetes, heart disease, asthma/chronic obstructive pulmonary disease, or low back pain. A follow-up survey was conducted in 1998. PRINCIPAL FINDINGS: Thirty-one percent of the patients missed at least one SF-12 item. Means of variance of prediction and standard errors of the mean imputed scores increased with the number of missing SF-12 items. Correlations between the observed and the imputed scores derived from the enhanced models were consistently higher than those derived from the simple model and the increments were significant for patients with > or =6 missing SF-12 items (p<.03). CONCLUSION: Missing SF-12 items are prevalent and lead to reduced analytical power. Regression-based multi-pattern imputation using the available SF-12 items is efficient and can produce good estimates of the scores. The enhancement from the additional patient information can significantly improve the accuracy of the imputed scores for patients with > or =6 items missing, leading to estimated scores that are as accurate as that of patients with <6 missing items.

Adult↗
PubMed2003

Methodological challenges associated with patient responses to follow-up longitudinal surveys regarding quality of care.

OBJECTIVE: To illustrate, using empirical data, methodological challenges associated with patient responses to longitudinal surveys regarding the quality of process of care and health status, including overall response rate, differential response rate, and stability of responses with time. DATA SOURCES/STUDY SETTING: Primary patient self-report data were collected from 30,308 patients in 1996 and 13,438 patients in 1998 as part of a two-year longitudinal study of quality of care and health status of patients receiving care delivered by 63 physician organizations (physician groups) across three West Coast states. STUDY DESIGN: We analyzed longitudinal, observational data collected by Pacific Business Group on Health (PBGH) from patients aged 18-70 using a four-page survey in 1996 and a similar survey in 1998 to assess health status, satisfaction, use of services, and self-reported process of care. A subset of patients with self-reported chronic disease in the 1996 study received an enriched survey in 1998 to more fully detail processes of care for patients with chronic disease. DATA COLLECTION/EXTRACTION METHODS: We measured response rate overall and separately for patients with chronic disease. Logistic regression was used to assess the impact of 1996 predictors on response to the follow-up 1998 survey. We compared process of care scores without and with nonresponse weights. Additionally, we measured stability of patient responses over time using percent agreement and kappa statistics, and examined rates of gender inconsistencies reported across the 1996 and 1998 surveys. PRINCIPAL FINDINGS: In 1998, response rates were 54 percent overall and 63 percent for patients with chronic disease. Patient demographics, health status, use of services, and satisfaction with care in 1996 were all significant predictors of response in 1998, highlighting the importance of analytic strategies (i.e., application of nonresponse weights) to minimize bias in estimates of care and outcomes associated with longitudinal quality of care and health outcome analyses. Process of care scores weighted for nonresponse differed from unweighted scores (p<.001). Stability of responses across time was moderate, but varied by survey item from fair to excellent. CONCLUSIONS: Longitudinal analyses involving the collection of data from the same patients at two points in time provide opportunities for analysis of relationships between process and outcomes of care that cannot occur with cross-sectional data. We present empirical results documenting the scope of the problems and discuss options for responding to these challenges. With increasing emphasis in the United States on quality reporting and use of financial incentives for quality in the health care market, it is important to identify and address methodological challenges that potentially threaten the validity of quality-of-care assessments.

Adult↗
PubMedDate not supplied

Is risk-adjustor selection more important than statistical approach for provider profiling? Asthma as an example.

OBJECTIVES: To examine how the selections of different risk adjustors and statistical approaches affect the profiles of physician groups on patient satisfaction. DATA SOURCES: Mailed patient surveys. Patients with asthma were selected randomly from each of 20 California physician groups between July 1998 and February 1999. A total of 2515 patients responded. RESEARCH DESIGN: A cross-sectional study. Patient satisfaction with asthma care was the performance indicator for physician group profiling. Candidate variables for risk-adjustment model development included sociodemographic, clinical characteristics, and self-reported health status. Statistical strategies were the ratio of observed-to-expected rate (OE), fixed effects (FE), and the random effects (RE) approaches. Model performance was evaluated using indicators of discrimination (C-statistic) and calibration (Hosmer-Lemeshow chi2). Ranking impact of using different risk adjustors and statistical approaches was based on the changes in absolute ranking (AR) and quintile ranking (QR) of physician group performance and the weighted kappa for quintile ranking. RESULTS: Variables that added significantly to the discriminative power of risk-adjustment models included sociodemographic (age, sex, prescription drug coverage), clinical (asthma severity), and health status (SF-36 PCS and MCS). Based on an acceptable goodness-of-fit (P > 0.1)and higher C-statistics, models adjusting for sociodemographic, clinical, and health status variables (Model S-C-H) using either the FE or RE approach were more favorable. However, the C-statistic (=0.68) was only fair for both models. The influence of risk-adjustor selection on change of performance ranking was more salient than choice of statistical strategy (AR: 50%-80% v. 20%-55%; QR: 10%-30% v. 0%-10%). Compared to the model adjusting for sociodemographic and clinical variables only and using OE approach, the Model S-C-H using RE approach resulted in 70% of groups changing in AR and 25% changing in QR (weighted kappa: 0.88). Compared to the Consumer Assessment of Health Plans model, the Model S-C-H using RE approach resulted in 65% of groups changing in AR and 20% changing in QR (weighted kappa: 0.88). CONCLUSIONS: In comparing the performance of physician groups on patient satisfaction with asthma care, the use of sociodemographic, clinical, and health status variables maximized risk-adjustment model performance. Selection of risk adjustors had more influence on ranking profiles than choice of statistical strategies. Stakeholders employing provider profiling should pay careful attention to the selection of both variables and statistical approach used in risk-adjustment.

Adult↗
PubMedDate not supplied

Will financial incentives stimulate quality improvement? Reactions from frontline physicians.

Pay-for-performance is being applied at the physician level to stimulate improvements in quality of care and cost efficiency; however, little is known about how physicians will respond. We interviewed physicians exposed to a financial incentive program in California to identify possible barriers to the successful application of financial incentives by exploring physicians' opinions of and experiences with pay-for-performance programs. Reasons physicians cited for quality deficiencies included insurance coverage limitations and lack of patient compliance, time, and proper physician oversight. Physicians believe that they play a significant role and have a moderate to high degree of control over quality of care and that it is important to self-monitor. Physicians expressed the need for accurate and timely data, peer comparisons, and more patient time, staff support, and consultations with colleagues to successfully monitor and deliver quality care. Many support increased pay for delivering high-quality care but question measurement accuracy, bonus payment financing, and health plan involvement.

Adult↗
PubMedDate not supplied

Pay for performance: its influence on the use of IT in physician organizations.

The introduction of information technology (IT) in physician organizations and practices is a source of great interest to physician leaders and policy makers. In this article, the authors describe what may be the nation's largest pay-for-performance program, its performance metrics, and incentives for the implementation and use of IT in medical groups and independent physician associations (IPAs). Results include the increased use of electronic clinical data, point-of-care technology, and the generation of more actionable reports for quality improvement. Noteworthy are the efforts by physician organizations to enhance data collection to demonstrate improved clinical performance and earn financial incentives.

California↗