Physician Practices and Readiness for Medical Home Reforms: Policy, Pitfalls, and Possibilities

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90086/1/hesr1332.pd

Quality by Any Other Name?: A Comparison of Three Profiling Systems for Assessing Health Care Quality

Many performance measurement systems are designed to identify differences in the quality provided by health plans or facilities. However, we know little about whether different methods of performance measurement provide similar answers about the quality of care of health care organizations. To examine this question, we used three different measurement approaches to assess quality of care delivered in veteran affairs (VA) facilities. Data Sources/Study Setting . Medical records for 621 patients at 26 facilities in two VA regions. Study Design . We examined agreements in quality conclusions using: focused explicit (38 measures for six conditions/prevention), global explicit (372 measures for 26 conditions/prevention), and structured implicit review physician-rated care (a single global rating of care for three chronic conditions and overall acute, chronic and preventive care). Trained nurse abstractors and physicians reviewed all medical records. Correlations between scores from the three systems were adjusted for measurement error in each using multilevel regression models. Results . Intercorrelations of scores were generally moderate to high across all three systems, and rose with adjustment for measurement error. Site-level correlations for prevention and diabetes care were particularly high. For example, adjusted for measurement error at the site level, prevention quality was correlated at 0.89 between the implicit and global systems, 0.67 between implicit and focused, and 0.73 between global and focused systems. Conclusions . We found moderate to high agreement in quality scores across the three profiling systems for most clinical areas, indicating that all three were measuring a similar construct called “quality.” Adjusting for measurement error substantially enhanced our ability to identify this underlying construct.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73479/1/HESR_730_sm_Appendix1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/73479/2/HESR+730+Appendix+2.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/73479/3/j.1475-6773.2007.00730.x.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/73479/4/HESR_730_sm_Appendix2.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/73479/5/HESR+730+Appendix+1.pd

Factors associated with length of stay and the risk of readmission in an acute psychiatric inpatient facility: a retrospective study

OBJECTIVE: This study was to investigate factors influencing the length of stay and predictors for the risk of readmission at an acute psychiatric inpatient unit. METHOD: Two comparative studies were embedded in a retrospective cross-sectional clinical file audit. A randomly selected 226 episodes of admissions including 178 patients during a twelve-month period were reviewed. A total of 286 variables were collected and analysed. A case control study was employed in the study of length of stay. A retrospective cohort study was used to investigate the predictors for the risk of readmission. RESULTS: Logistic regression analyses showed that 10 variables were associated with length of stay. Seclusion during the index admission, accommodation problems and living in an area lacking community services predicted longer stay. During the follow-up period 82 patients (46%) were readmitted. Cox regression analyses showed 9 variables were related to the risk of readmission. Six of these variables increased the risk of readmission, including history of previous frequent admission, risk to others at the time of the index admission and alcohol intoxication. More active and assertive treatment in the community post-discharge decreased the risk of readmission. CONCLUSIONS: Length of stay is multifactorially determined. Behavioural manifestations of illness and lack of social support structures predicted prolonged length of stay. Good clinical practice did not necessarily translate to a shorter length of stay. Therefore, length of stay is predictable, but not readily modifiable within the clinical domain. Good clinical practice within the community following discharge likely reduces the risk of readmission. Quality of inpatient care does not influence the risk of readmission, which therefore raises a question about the validity of using the rate of readmission as an outcome measure of psychiatric inpatient care

Incorporating statistical uncertainty in the use of physician cost profiles

<p>Abstract</p> <p>Background</p> <p>Physician cost profiles (also called efficiency or economic profiles) compare the costs of care provided by a physician to his or her peers. These profiles are increasingly being used as the basis for policy applications such as tiered physician networks. Tiers (low, average, high cost) are currently defined by health plans based on percentile cut-offs which do not account for statistical uncertainty. In this paper we compare the percentile cut-off method to another method, using statistical testing, for identifying high-cost or low-cost physicians.</p> <p>Methods</p> <p>We created a claims dataset of 2004-2005 data from four Massachusetts health plans. We employed commercial software to create episodes of care and assigned responsibility for each episode to the physician with the highest proportion of professional costs. A physicians' cost profile was the ratio of the sum of observed costs divided by the sum of expected costs across all assigned episodes. We discuss a new method of measuring standard errors of physician cost profiles which can be used in statistical testing. We then assigned each physician to one of three cost categories (low, average, or high cost) using two methods, percentile cut-offs and a t-test (p-value ≤ 0.05), and assessed the level of disagreement between the two methods.</p> <p>Results</p> <p>Across the 8689 physicians in our sample, 29.5% of physicians were assigned a different cost category when comparing the percentile cut-off method and the t-test. This level of disagreement varied across specialties (17.4% gastroenterology to 45.8% vascular surgery).</p> <p>Conclusions</p> <p>Health plans and other payers should incorporate statistical uncertainty when they use physician cost-profiles to categorize physicians into low or high-cost tiers.</p

Validation of the treatment identification strategy of the HEDIS addiction quality measures: concordance with medical record review

<p>Abstract</p> <p>Background</p> <p>Strategies to accurately identify the occurrence of specific health care events in administrative data is central to many quality improvement and research efforts. Many health care quality measures have treatment identification strategies based on diagnosis and procedure codes - an approach that is inexpensive and feasible but usually of unknown validity. In this study, we examined if the diagnosis/procedure code combinations used in the 2006 HEDIS Initiation and Engagement quality measures to identify instances of addiction treatment have high concordance with documentation of addiction treatment in clinical progress notes.</p> <p>Methods</p> <p>Four type of records were randomly sampled from VHA electronic medical data: (a) Outpatient records from a substance use disorder (SUD) specialty clinic with a HEDIS-qualified substance use disorder (SUD) diagnosis/CPT code combination (n = 700), (b) Outpatient records from a non-SUD setting with a HEDIS-qualified SUD diagnosis/CPT code combination (n = 592), (c) Specialty SUD Inpatient/residential records that included a SUD diagnosis (n = 700), and (d) Non-SUD specialty Inpatient/residential records that included a SUD diagnosis (n = 700). Clinical progress notes for the sampled records were extracted and two raters classified each as documenting or not documenting addiction treatment. Rates of concordance between the HEDIS addiction treatment identification strategy and the raters' judgments were calculated for each record type.</p> <p>Results</p> <p>Within SUD outpatient clinics and SUD inpatient specialty units, 92% and 98% of sampled records had chart evidence of addiction treatment. Of outpatient encounters with a qualifying diagnosis/procedure code combination outside of SUD clinics, 63% had chart evidence of addiction treatment. Within non-SUD specialty inpatient units, only 46% of sampled records had chart evidence of addiction treatment.</p> <p>Conclusions</p> <p>For records generated in SUD specialty settings, the HEDIS strategy of identifying SUD treatment with diagnosis and procedure codes has a high concordance with chart review. The concordance rate outside of SUD specialty settings is much lower and highly variable between facilities. Therefore, some patients may be counted as meeting the 2006 HEDIS Initiation and Engagement criteria without having received the specified amount (or any) addiction treatment.</p

Impact of state mandatory insurance coverage on the use of diabetes preventive care

<p>Abstract</p> <p>Background</p> <p>46 U.S. states and the District of Columbia have passed laws and regulations mandating that health insurance plans cover diabetes treatment and preventive care. Previous research on state mandates suggested that these policies had little impact, since many health plans already covered the benefits. Here, we analyze the contents of and model the effect of state mandates. We examined how state mandates impacted the likelihood of using three types of diabetes preventive care: annual eye exams, annual foot exams, and performing daily self-monitoring of blood glucose (SMBG).</p> <p>Methods</p> <p>We collected information on diabetes benefits specified in state mandates and time the mandates were enacted. To assess impact, we used data that the Behavioral Risk Factor Surveillance System gathered between 1996 and 2000. 4,797 individuals with self-reported diabetes and covered by private insurance were included; 3,195 of these resided in the 16 states that passed state mandates between 1997 and 1999; 1,602 resided in the 8 states or the District of Columbia without state mandates by 2000. Multivariate logistic regression models (with state fixed effect, controlling for patient demographic characteristics and socio-economic status, state characteristics, and time trend) were used to model the association between passing state mandates and the usage of the forms of diabetes preventive care, both individually and collectively.</p> <p>Results</p> <p>All 16 states that passed mandates between 1997 and 1999 required coverage of diabetic monitors and strips, while 15 states required coverage of diabetes self management education. Only 1 state required coverage of periodic eye and foot exams. State mandates were positively associated with a 6.3 (P = 0.04) and a 5.8 (P = 0.03) percentage point increase in the probability of privately insured diabetic patient's performing SMBG and simultaneous receiving all three preventive care, respectively; state mandates were not significantly associated with receiving annual diabetic eye (0.05 percentage points decrease, P = 0.92) or foot exams (2.3 percentage points increase, P = 0.45).</p> <p>Conclusions</p> <p>Effects of state mandates varied by preventive care type, with state mandates being associated with a small increase in SMBG. We found no evidence that state mandates were effective in increasing receipt of annual eye or foot exams. The small or non-significant effects might be attributed to small numbers of insured people not having the benefits prior to the mandates' passage. If state mandates' purpose is to provide improved benefits to many persons, policy makers should consider determining the number of people who might benefit prior to passing the mandate.</p

The validity of using ICD-9 codes and pharmacy records to identify patients with chronic obstructive pulmonary disease

Background: Administrative data is often used to identify patients with chronic obstructive pulmonary disease (COPD), yet the validity of this approach is unclear. We sought to develop a predictive model utilizing administrative data to accurately identify patients with COPD. Methods: Sequential logistic regression models were constructed using 9573 patients with postbronchodilator spirometry at two Veterans Affairs medical centers (2003-2007). COPD was defined as: 1) FEV1/FVC <0.70, and 2) FEV1/FVC < lower limits of normal. Model inputs included age, outpatient or inpatient COPD-related ICD-9 codes, and the number of metered does inhalers (MDI) prescribed over the one year prior to and one year post spirometry. Model performance was assessed using standard criteria. Results: 4564 of 9573 patients (47.7%) had an FEV1/FVC < 0.70. The presence of ≥1 outpatient COPD visit had a sensitivity of 76% and specificity of 67%; the AUC was 0.75 (95% CI 0.74-0.76). Adding the use of albuterol MDI increased the AUC of this model to 0.76 (95% CI 0.75-0.77) while the addition of ipratropium bromide MDI increased the AUC to 0.77 (95% CI 0.76-0.78). The best performing model included: ≥6 albuterol MDI, ≥3 ipratropium MDI, ≥1 outpatient ICD-9 code, ≥1 inpatient ICD-9 code, and age, achieving an AUC of 0.79 (95% CI 0.78-0.80). Conclusion: Commonly used definitions of COPD in observational studies misclassify the majority of patients as having COPD. Using multiple diagnostic codes in combination with pharmacy data improves the ability to accurately identify patients with COPD.Department of Veterans Affairs, Health Services Research and Development (DHA), American Lung Association (CI- 51755-N) awarded to DHA, the American Thoracic Society Fellow Career Development AwardPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/84155/1/Cooke - ICD9 validity in COPD.pd

Competencies for food graduate careers: developing a language tool

Unlike many other graduate career pathways in the UK, the food industry does not have a cohesive competency framework to support employers, students and degree providers. Food sciences-based technical graduates are a significant proportion of the industry’s graduate intake; this study aims to provide such a framework. Initial work involving a sample of representative stakeholders has created a list of typical attributes and associated definitions that may be desirable in food sciences graduates. Material was gathered by semi-structured qualitative interviews and analysed by thematic analysis followed by a modified Delphi technique. The resulting framework is tailored to needs and terminology prevalent in food industry employment. The process employed could be utilised for building other vocational graduate competency frameworks. Further plans include using the framework to ascertain the important elements for typical graduate entry roles, better informing students about desirable qualities and supporting future competency-based curriculum review

An analysis of inter-professional collaboration in osteoporosis screening at a primary care level using the D'Amour model

Objectives This study describes the perspective of patients, nurses, pharmacists, doctors and policy makers to identify the level of collaboration and the areas for improvement to achieve inter-professional collaboration between doctors, nurses, pharmacists and policy makers in a primary care clinic. Methods Patients (n = 20), Nurses (n = 10), pharmacists (n = 11), doctors (n = 10) and policy makers (n = 5) from a primary care were individually interviewed using a semi-structured topic guide. Purposive sampling was used. Interviews were transcribed verbatim and analysed using thematic analysis informed by constant comparison. Results Patients, doctors, nurses, pharmacists and policy makers were eager for pharmacists to be more proactive in creating health awareness and conducting osteoporosis screening at the primary care clinic via inter-professional collaboration. These findings were further examined using the D'Amour's structural model of collaboration which encompasses four main themes: shared goals and visions, internalization, formalization and governance. This model supports our data which highlights a lack of understanding of the pharmacists' role among the doctors, nurses, policy makers and pharmacists themselves. There is also a lack of governance and formalization, that fosters consensus, leadership, protocol and information exchange. Nonetheless, the stakeholders trust that pharmacists have sufficient knowledge to contribute to the screening of osteoporosis. Our primary care clinic can be described as developing towards an inter-professional collaboration in managing osteoporosis but is still in its early stages. Conclusions Inter-professional collaboration in osteoporosis management at the primary care level is beginning to be practised. Efforts extending to awareness and acceptance towards the pharmacists' role will be crucial for a successful change