Mary M. Hogan

Comparison of Quality of Care for Patients in the Veterans Health Administration and Patients in a National Sample

As methods for measuring the quality of medical care have matured, widespread quality problems have become increasingly evident (1, 2). The solution to these problems is much less obvious, however, particularly with regard to large delivery systems. Many observers have suggested that improved information systems, systematic performance monitoring, and coordination of care are necessary to enhance the quality of medical care (3). Although the use of integrated information systems (including electronic medical records) and performance indicators has become more common throughout the U.S. health care system, most providers are not part of a larger integrated delivery system and continue to rely on traditional information systems (4). An exception is the Veterans Health Administration (VHA). As the largest delivery system in the United States, the VHA has been recognized as a leader in developing a more coordinated system of care. Beginning in the early 1990s, VHA leadership instituted both a sophisticated electronic medical record system and a quality measurement approach that holds regional managers accountable for several processes in preventive care and in the management of common chronic conditions (5, 6). Other changes include a system-wide commitment to quality improvement principles and a partnership between researchers and managers for quality improvement (7). As Jha and colleagues (8) have shown, since these changes have been implemented, VHA performance has outpaced that of Medicare in the specific areas targeted. Nevertheless, whether this improvement has extended beyond the relatively narrow scope of the performance measures is unknown. Beyond that study, the data comparing VHA care with other systems of care are sparse and mixed. For example, patients hospitalized at VHA hospitals were more likely than Medicare patients to receive angiotensin-converting enzyme inhibitors and thrombolysis after myocardial infarction (9). On the other hand, VHA patients were less likely to receive angiography when indicated and had higher mortality rates after coronary artery bypass grafting than patients in community hospitals (10, 11). Kerr and colleagues found that care for diabetes was better in almost every dimension in the VHA system than in commercial managed care (12). More extensive comparisons, especially of outpatient care, are lacking. To address these issues, a more comprehensive assessment of quality is needed. Using a broad measure of quality of care that is based on medical record review and was developed outside the VHA, we compared the quality of outpatient and inpatient care among 2 samples: 1) a national sample of patients drawn from 12 communities and 2) VHA patients from 26 facilities in 12 health care systems located in the southwestern and midwestern United States (13). We analyzed performance in the years after the institution of routine performance measurement and the electronic medical record. Using the extensive set of quality indicators included in the measurement system, we compared the overall quality of care delivered in the VHA system and in the United States, as well as the quality of acute, chronic, and preventive care across 26 conditions. In addition, we evaluated whether VHA performance was better in the specific areas targeted by the VHA quality management system. Methods Development of Quality Indicators For this study, we used quality indicators from RANDs Quality Assessment Tools system, which is described in more detail elsewhere (14-17). The indicators included in the Quality Assessment Tools system are process quality measures, are more readily actionable than outcomes measures, require less risk adjustment, and follow the structure of national guidelines (18, 19). After reviewing established national guidelines and the medical literature, we chose a subset of quality indicators from the Quality Assessment Tools system that represented the spectrum of outpatient and inpatient care (that is, screening, diagnosis, treatment, and follow-up) for acute and chronic conditions and preventive care processes representing the leading causes of morbidity, death, and health care use among older male patients. The Appendix Table lists the full indicator set, which was determined by four 9-member, multispecialty expert panels. These panels assessed the validity of the proposed indicators using the RAND/University of California, Los Angelesmodified Delphi method. The experts rated the indicators on a 9-point scale (1 = not valid; 9 = very valid), and we accepted indicators that had a median validity score of 7 or higher. This method of selecting indicators is reliable and has been shown to have content, construct, and predictive validity (20-23). Of the 439 indicators in the Quality Assessment Tools system, we included 348 indicators across 26 conditions in our study and excluded 91 indicators that were unrelated to the target population (for example, those related to prenatal care and cesarean sections). Of the 348 indicators, 21 were indicators of overuse (for example, patients with moderate to severe asthma should not receive -blocker medications) and 327 were indicators of underuse (for example, patients who have been hospitalized for heart failure should have follow-up contact within 4 weeks of discharge). Appendix Table. Comparison of Performance of the Veterans Health Administration Sample and the National Sample by Indicator Two physicians independently classified each indicator according to the type of care delivered; the function of the indicated care (screening, diagnosis, treatment, and follow-up); and whether the indicator was supported by a randomized, controlled trial, another type of controlled trial, or other evidence. Type of care was classified as acute (for example, in patients presenting with dysuria, presence or absence of fever and flank pain should be elicited), chronic (for example, patients with type 2 diabetes mellitus in whom dietary therapy has failed should receive oral hypoglycemic therapy), or preventive (for example, all patients should be screened for problem drinking). In addition, we further classified the indicators into 3 mutually exclusive categories according to whether they corresponded to the VHA performance indicators that were in use in fiscal year 1999. Twenty-six indicators closely matched the VHA indicators, 152 involved conditions that were targeted by the VHA indicators but were not among the 26 matches, and 170 did not match the VHA measures or conditions. We performed a similar process to produce a list of 15 indicators that matched contemporaneous Health Plan Employer Data and Information Set (HEDIS) performance measures (24). Table 1 shows the conditions targeted by the indicators, and Table 2 gives an example indicator for each of the conditions or types of care for which condition- or type-specific comparisons were possible. Table 1. Conditions and Number of Indicators Used in Comparisons Table 2. Example Indicators of Quality of Care Identifying Participants Patients were drawn from 2 ongoing quality-of-care studies: a study of VHA patients and a random sample of adults from 12 communities (13). The VHA patients were drawn from 26 clinical sites in 12 health care systems located in 2 Veterans Integrated Service Networks in the midwestern and southwestern United States. These networks closely match the overall Veterans Affairs system with regard to medical record review and survey-based quality measures (25, 26). We selected patients who had had at least 2 outpatient visits in each of the 2 years between 1 October 1997 and 30 September 1999. A total of 106576 patients met these criteria. We randomly sampled 689, oversampling for chronic obstructive pulmonary disease (COPD), hypertension, and diabetes, and were able to locate records for 664 patients (a record location rate of 96%). Because of resource constraints, we reviewed a random subset of 621 of these records. Since this sample contained only 20 women and 4 patients younger than 35 years of age, we further restricted the sample to men older than 35 years of age. Thus, we included 596 VHA patients in the analysis. All of these patients had complete medical records. The methods we used to obtain the national sample have been described elsewhere (13) and are summarized here. As part of a nationwide study, residents of 12 large metropolitan areas (Boston, Massachusetts; Cleveland, Ohio; Greenville, South Carolina; Indianapolis, Indiana; Lansing, Michigan; Little Rock, Arkansas; Miami, Florida; Newark, New Jersey; Orange County, California; Phoenix, Arizona; Seattle, Washington; and Syracuse, New York) were contacted by using random-digit dialing and were asked to complete a telephone survey (27). To ensure comparability with the VHA sample, we included only men older than 35 years of age. Between October 1998 and August 2000, we telephoned 4086 of these participants and asked for permission to obtain copies of their medical records from all providers (both individual and institutional) that they had visited within the past 2 years. We received verbal consent from 3138 participants (77% of those contacted by telephone). We mailed consent forms and received written permission from 2351 participants (75% of those who had given verbal permission). We received at least 1 medical record for 2075 participants (88% of those who had returned consent forms). We excluded participants who had not had at least 2 medical visits in the past 2 years to further ensure comparability with the VHA sample. Thus, our final national sample included 992 persons. The rolling abstraction period (October 1996 to August 2000) substantially overlapped the VHA sampling period. The average overlap was 70%, and all records had at least 1 year of overlap. Seven hundred eight (71%) of the 992 persons in the national sample had complete medical records. On the basis of data from the original telephone survey, we det

When More Is Not Better Treatment Intensification Among Hypertensive Patients With Poor Medication Adherence

Background— Hypertension may be poorly controlled because patients do not take their medications (poor adherence) or because providers do not increase medication when appropriate (lack of medication intensification, or “clinical inertia”). We examined the prevalence of and relationship between patient adherence and provider treatment intensification. Methods and Results— We used a retrospective cohort study of hypertensive patients who had filled prescriptions for 1 or more blood pressure (BP) medications at Veterans’ Affairs (VA) healthcare facilities in a Midwestern VA administrative region. Our sample included all patients who received at least 2 outpatient BP medication refills during 2004 and had 1 or more outpatient primary care visits with an elevated systolic BP >140 but <200 mm Hg or diastolic BP >90 mm Hg during 2005 (n=38 327). For each episode of elevated BP during 2005 (68 610 events), we used electronic pharmacy refill data to examine patients’ BP medication adherence over the prior 12 months and whether providers increased doses or added BP medications (“intensification”). Multivariate analyses accounted for the clustering of elevated BP events within patients and adjusted for patient age, comorbidities, number of BP medications, encounter systolic BP, and average systolic BP over the prior year. Providers intensified medications in 30% of the 68 610 elevated BP events, with almost no variation in intensification regardless of whether patients had good or poor BP medication adherence. After adjustment, intensification rates were 31% among patients who had “gaps” of <20% (days on which patients should have had medication but no medication was available because medications had not been refilled), 34% among patients with refill gaps of 20% to 59%, and 32% among patients with gaps of 60% or more. Conclusions— Intensification of medications occurred in fewer than one third of visits in which patients had an elevated BP. Patients’ prior medication adherence had little impact on providers’ decisions about intensifying medications, even at very high levels of poor adherence. Addressing both patient adherence and provider intensification simultaneously would most likely result in better BP control.

The Role of Clinical Uncertainty in Treatment Decisions for Diabetic Patients with Uncontrolled Blood Pressure

Context Why do clinicians fail to intensify antihypertensive therapy when a patients blood pressure is elevated? Contribution This study involved 1169 diabetic patients seen by 92 primary care providers at 9 Veterans Affairs facilities. All had elevated triage blood pressures, but only half received antihypertensive treatment intensification by providers. Patient reports of home blood pressures or repeated blood pressures by providers within normal limits and discussion of medication issues decreased the likelihood of antihypertensive intensification at clinic visits. Implication Uncertainty about true blood pressure values may underlie many reasons why physicians do not intensify antihypertensive therapy. The Editors Despite some recent improvements in blood pressure control, the number of patients with inadequate control remains high and contributes to excess morbidity and mortality, especially among patients at high risk from complications of hypertension (18). Several studies have suggested that clinical inertiathe failure by providers to initiate or intensify therapy (medication intensification) in the face of apparent need to do sois a main contributor to poor control of hypertension (912). Although the failure to intensify treatment medications for patients with elevated blood pressures at visits has been well documented (5, 6, 1218), factors underlying what seems to be clinical inertia have been studied less systematically. When providers are queried after clinic visits about the lack of medication intensification for elevated blood pressure, they variously report that the patients true blood pressure was lower than the clinic blood pressure reading, that other patient concerns precluded attention on blood pressure management, and that patient adherence should be improved before medication intensification (6, 17). Some studies have examined the role of various clinical and patient factors in intensification decisions (6, 8, 17, 19, 20), but no study has used a detailed conceptual model to comprehensively examine the relative contribution of a broad array of potential patient, provider, organizational, and visit-specific contributors to a medication intensification decision. In addition, although a frequently cited reason for deferring medication changes is that the clinic blood pressure does not reflect the patients true blood pressure (21, 22), this clinical uncertainty and its effects have not been explored. To better understand factors underlying apparent clinical inertia for hypertension, we designed the ABATe (Addressing Barriers to Treatment for Hypertension) study to examine treatment change decisions for diabetic primary care patients with elevated triage blood pressures before a primary care visit. We defined elevated blood pressure for this population to be 140/90 mm Hg, a value well above guideline targets for diabetic patients and one clearly requiring some type of action (4). Our goals were to assess how often patients presenting with an elevated triage blood pressure received medication intensification or were scheduled for close follow-up and the role that clinical uncertainty about blood pressure, competing demands and prioritization, medication-related factors, and care organization play in treatment change decisions. Methods Conceptual Model On the basis of theories of patient, provider, and organization behavior (2336), we developed a conceptual modelthe hypertension clinical action modelto examine decisions that drive treatment change (medication intensification or prompt blood pressure follow-up) for elevated blood pressure (Figure 1). The model, developed by 2 internists and 3 PhD-level methodologists in conjunction with development of ABATe and before data collection, proposes such treatment change decisions at a visit are based on 4 main conceptual domains: clinical uncertainty (Is the patients blood pressure truly elevated? Does the clinic blood pressure reflect the true blood pressure?), competing demands and prioritization (What other problems need to be addressed at this visit? Is blood pressure management a priority for this particular patient? Does the provider place priority on blood pressure management in general?), medication-related factors (Should adherence be addressed first? Is the medication regimen too complex? Will the patient accept another medication?), and care organization (Is there sufficient time to address hypertension? Are staff available for follow-up?). In addition, as part of grant development, we hypothesized that the following factors would lead to a lower probability of treatment change: uncertainty about whether the patients visit blood pressure reflected their true blood pressure (clinical uncertainty), comorbid conditions unrelated to hypertension and diabetes (37), a lower priority placed by the provider on the importance of treating elevated blood pressure, a higher number of baseline medications, perceived medication adherence problems, shorter clinic visit times, and lack of staff to follow up for blood pressure medication adjustment. Figure 1. Hypertension clinical action model. Design Setting We conducted a prospective cohort study of patients with scheduled primary care visits at 9 Veterans Affairs facilities located in 3 midwestern states. These facilities varied in size and structure, with 3 large academic-affiliated medical centers, 1 large nonacademic medical center, and 1 large and 4 small community-based outpatient clinics. From 15 February 2005 to 14 February 2006, approximately 33500 diabetic patients visited primary care providers (including residents) in these facilities (range per facility, 1050 to 9200 diabetic patients). The institutional review boards of all participating facilities approved the study protocol. Both patients and providers gave written informed consent before participating. Providers received a

Building a better quality measure: are some patients with 'poor quality' actually getting good care?

50 bookstore gift card, and patients received a

Sins of omission : Getting too little medical care may be the greatest threat to patient safety

10 department store gift card for completing initial surveys. Providers were told that the study was about diabetes and hypertension, with the purpose being to study challenges in treating patients with diabetes and ways to overcome these challenges so that quality of care can be enhanced. Primary Care Providers We invited all nonresident primary care providers with patient care responsibility at least 2 half-days per week to participate in the study. Of the eligible 126 providers approached, 104 consented to participate, for an overall recruitment rate of 83% (median facility-level recruitment rate, 88%). By the time recruitment started, 12 providers had stopped working at their facility or changed their patient care responsibilities, leaving 92 primary care providers still eligible to participate (range per facility, 2 to 28 providers; median, 8). Patients As specified by our institutional review board protocols, potentially eligible patients were referred to study staff by triage personnel. During the enrollment periods at each facility, study staff screened all referred patients who presented for a scheduled visit to participating primary care providers and whose lowest triage systolic blood pressure was 140 mm Hg or greater or whose lowest triage diastolic blood pressure was 90 mm Hg or greater (Figure 2). In each of the facilities, triage staff routinely used an electronic cuff to check the patients blood pressure before the provider visit. Triage policies specified that a second blood pressure measurement should be obtained if the first blood pressure was elevated. In addition to the triage blood pressure, study staff screened patients for the following inclusion criteria: the participant confirmed a diagnosis of diabetes, the participating provider was the primary provider of diabetes care for the participant, and the participant spoke English. Patients with impaired decision-making ability (for example, dementia and traumatic brain injury) or terminal disease and residents of nursing homes were excluded. Of the 1556 patients approached by study staff, 213 were ineligible (Figure 2) and 1169 provided written informed consent to participate in the study (approached and eligible, 87%; median facility-level recruitment rate, 89%). We enrolled a median of 14 patients per provider (range, 1 to 16 patients) from February 2005 to March 2006. Recruitment time per facility varied from 4 to 12 months. Figure 2. Study flow diagram. PCP = primary care provider. *Diabetic patients presenting for a primary care visit to 1 of 92 participating providers were referred for eligibility assessment if their lowest triage blood pressure was140/90 mm Hg. *Number of responses varied by individual item. Our prespecified sample size calculations stipulated that we needed at least 11 patients from 80 physicians across 8 sites (that is, 880 patients) to detect a moderate difference in treatment change (about 12%) when competing demands were or were not present. Data Sources We included data from 5 sources in our analysis (Table 1). First, a baseline survey completed by all providers provided variables assessing provider prioritization to blood pressure management, general provider characteristics, and availability of ancillary support for blood pressure management. Second, providers completed a brief visit survey for each patient after the same clinic session in which they saw the patient (completion rate, 99%). This survey provided information on which issues were discussed during the visit, the providers blood pressure goal for the patient, and whether medications were changed during the visit. Third, a patient survey conducted at enrollment provided sociodemographic characteristics, self-reported adherence and difficulty with medications, and self-management practices (completion rate, 91%). Fourth, review of electronic medical records documented free text blood pressure values and notes on actions taken at the enrollment visit. Finally, patient age, prescribed medications and th

Comparing Clinical Automated, Medical Record, and Hybrid Data Sources for Diabetes Quality Measures

Background. National performance measures monitor the proportion of diabetic patients with low-density lipoprotein (LDL) levels ≥130 mg/dL, but such simple intermediate outcomes measure poor control, not necessarily poor care. “Tightly linked” quality measures define good quality either by a good intermediate outcome (LDL <130 mg/dL) or by evidence of appropriate responses to poor control (eg, starting or optimizing medications for high LDL or not doing so in the face of contraindications). Objectives. We examined hyperlipidemia therapy for patients with diabetes to determine the relative accuracy of quality assessment using simple intermediate outcome versus tightly linked quality measures. Research Design. Retrospective longitudinal cohort. Subjects. A total of 1154 diabetic patients with an LDL test done between October 1, 1998, and March 31, 1999, in 2 large VA facilities. M>easures. LDL levels, medication treatment, and explanations for poor quality. Results. Although 27% (307 of 1154) of patients had an LDL ≥130 mg/dL using the simple intermediate outcome measure, only 13% (148 of 1154) were classified as having substandard quality using the tightly linked measure. Among the 159 reclassified to adequate quality, 117 had lipid-lowering medication started or increased within 6 months of an LDL ≥130 mg/dL, 8 were already on high-dose medication, 12 had a repeat LDL <130 mg/dL, and 22 had contraindications to treatment. Conclusion. Simple intermediate outcome measures can be an inaccurate reflection of true quality of care, and many patients classified as having substandard quality by “poor control” might actually be receiving good quality of care.

Monitoring Performance for Blood Pressure Management Among Patients With Diabetes Mellitus: Too Much of a Good Thing?

AbstractBACKGROUND: Little is known about the relative incidence of serious errors of omission versus errors of commission. OBJECTIVE: To identify the most common substantive medical errors identified by medical record review. DESIGN: Retrospective cohort study. SETTING: Twelve Veterans Affairs health care systems in 2 regions. PARTICIPANTS: Stratified random sample of 621 patients receiving care over a 2-year period. MAIN OUTCOME MEASURE: Classification of reported quality problems. METHODS: Trained physicians reviewed the full inpatient and outpatient record and described quality problems, which were then classified as errors of omission versus commission. RESULTS: Eighty-two percent of patients had at least 1 error reported over a 13-month period. The average number of errors reported per case was 4.7 (95% confidence intervals [CI]: 4.4, 5.0). Overall, 95.7% (95% CI: 94.9%, 96.4%) of errors were identified as being problems with under-use. Inadequate care for people with chronic illnesses was particularly common. Among errors of omission, obtaining insufficient information from histories and physicals (25.3%), inadequacies in diagnostic testing (33.9%), and patients not receiving needed medications (20.7%) were all common. Out of the 2,917 errors identified, only 27 were rated as being highly serious, and 26 (96%) of these were errors of omission. CONCLUSIONS: While preventing iatrogenic injury resulting from medical errors is a critically important part of quality improvement, we found that the overwhelming majority of substantive medical errors identifiable from the medical record were related to people getting too little medical care, especially for those with chronic medical conditions.

Profiling quality of care: Is there a role for peer review?

BACKGROUND Little is known about the relative reliability of medical record and clinical automated data, sources commonly used to assess diabetes quality of care. The agreement between diabetes quality measures constructed from clinical automated versus medical record data sources was compared, and the performance of hybrid measures derived from a combination of the two data sources was examined. METHODS Medical records were abstracted for 1,032 patients with diabetes who received care from 21 facilities in 4 Veterans Integrated Service Networks. Automated data were obtained from a central Veterans Health Administration diabetes registry containing information on laboratory tests and medication use. RESULTS Success rates were higher for process measures derived from medical record data than from automated data, but no substantial differences among data sources were found for the intermediate outcome measures. Agreement for measures derived from the medical record compared with automated data was moderate for process measures but high for intermediate outcome measures. Hybrid measures yielded success rates similar to those of medical record-based measures but would have required about 50% fewer chart reviews. CONCLUSIONS Agreement between medical record and automated data was generally high. Yet even in an integrated health care system with sophisticated information technology, automated data tended to underestimate the success rate in technical process measures for diabetes care and yielded different quartile performance rankings for facilities. Applying hybrid methodology yielded results consistent with the medical record but required less data to come from medical record reviews.

Study protocol: the Adherence and Intensification of Medications (AIM) study--a cluster randomized controlled effectiveness study.

BACKGROUND Performance measures that reward achieving blood pressure (BP) thresholds may contribute to overtreatment. We developed a tightly linked clinical action measure designed to encourage appropriate medical management and a marker of potential overtreatment, designed to monitor overly aggressive treatment of hypertension in the face of low diastolic BP. METHODS We conducted a retrospective cohort study in 879 Department of Veterans Affairs (VA) medical centers and smaller community-based outpatient clinics. The clinical action measure for hypertension was met if the patient had a passing index BP at the visit or had an appropriate action. We examined the rate of passing the action measure and of potential overtreatment in the Veterans Health Administration during 2009-2010. RESULTS There were 977,282 established VA patients, 18 years and older, with diabetes mellitus (DM). A total of 713,790 patients were eligible for the action measure; 94% passed the measure (82% because they had a BP <140/90 mm Hg at the visit and an additional 12% with a BP ≥140/90 mm Hg and appropriate clinical actions). Facility pass rates varied from 77% to 99% (P < .001). Among all patients with DM, 197,291 (20%) had a BP lower than 130/65 mm Hg; of these, 80 903 (8% of all patients with DM) had potential overtreatment. Facility rates of potential overtreatment varied from 3% to 20% (P < .001). Facilities with higher rates of meeting the current threshold measure (<140/90 mm Hg) had higher rates of potential overtreatment (P < .001). CONCLUSIONS While 94% of diabetic veterans met the action measure, rates of potential overtreatment are currently approaching the rate of undertreatment, and high rates of achieving current threshold measures are directly associated with overtreatment. Implementing a clinical action measure for hypertension management, as the Veterans Health Administration is planning to do, may result in more appropriate care and less overtreatment.

Test of the Health Promotion Model as a Causal Model of Construction Workers' Use of Hearing Protection

BackgroundWe sought to develop a more reliable structured implicit chart review instrument for use in assessing the quality of care for chronic disease and to examine if ratings are more reliable for conditions in which the evidence base for practice is more developed.MethodsWe conducted a reliability study in a cohort with patient records including both outpatient and inpatient care as the objects of measurement. We developed a structured implicit review instrument to assess the quality of care over one year of treatment. 12 reviewers conducted a total of 496 reviews of 70 patient records selected from 26 VA clinical sites in two regions of the country. Each patient had between one and four conditions specified as having a highly developed evidence base (diabetes and hypertension) or a less developed evidence base (chronic obstructive pulmonary disease or a collection of acute conditions). Multilevel analysis that accounts for the nested and cross-classified structure of the data was used to estimate the signal and noise components of the measurement of quality and the reliability of implicit review.ResultsFor COPD and a collection of acute conditions the reliability of a single physician review was quite low (intra-class correlation = 0.16–0.26) but comparable to most previously published estimates for the use of this method in inpatient settings. However, for diabetes and hypertension the reliability is significantly higher at 0.46. The higher reliability is a result of the reviewers collectively being able to distinguish more differences in the quality of care between patients (p < 0.007) and not due to less random noise or individual reviewer bias in the measurement. For these conditions the level of true quality (i.e. the rating of quality of care that would result from the full population of physician reviewers reviewing a record) varied from poor to good across patients.ConclusionsFor conditions with a well-developed quality of care evidence base, such as hypertension and diabetes, a single structured implicit review to assess the quality of care over a period of time is moderately reliable. This method could be a reasonable complement or alternative to explicit indicator approaches for assessing and comparing quality of care. Structured implicit review, like explicit quality measures, must be used more cautiously for illnesses for which the evidence base is less well developed, such as COPD and acute, short-course illnesses.