Expanding the uses of AHRQ's prevention quality indicators: validity from the clinician perspective

Early impacts of the Pennsylvania Rural Health Model on potentially avoidable utilization

The Pennsylvania Rural Health Model (PARHM) is a novel alternative payment model for rural hospitals that aims to test whether hospital-based global budgets, coupled with delivery transformation plans, improve the quality of health care and health outcomes in rural communities. Eighteen hospitals joined PARHM in 3 cohorts between 2019 and 2021. This study assessed PARHM's impact on changes in potentially avoidable utilization (PAU)-a measure of admission rates policymakers explicitly targeted for improvement in PARHM. Using a difference-in-differences analysis and all-payer hospital discharge data for Pennsylvania hospitals from 2016 through 2022, we found no significant overall reduction in community-level PAU rates up to 4 years post-PARHM implementation, relative to changes in rural Pennsylvania communities whose hospitals did not join PARHM. However, heterogeneous treatment effects were observed across cohorts that joined PARHM in different years, and between critical access vs prospective payment system hospitals. These findings offer insight into how alternative payment models in rural health care settings may have heterogeneous impacts based on contextual factors and highlight the importance of accounting for these factors in proposed expansions of alternative payment models for rural health systems.

Risk stratification models for predicting preventable hospitalization in commercially insured late middle-aged adults with depression

BACKGROUND

A significant number of late middle-aged adults with depression have a high illness burden resulting from chronic conditions which put them at high risk of hospitalization. Many late middle-aged adults are covered by commercial health insurance, but such insurance claims have not been used to identify the risk of hospitalization in individuals with depression. In the present study, we developed and validated a non-proprietary model to identify late middle-aged adults with depression at risk for hospitalization, using machine learning methods.

METHODS

This retrospective cohort study involved 71,682 commercially insured older adults aged 55-64 years diagnosed with depression. National health insurance claims were used to capture demographics, health care utilization, and health status during the base year. Health status was captured using 70 chronic health conditions, and 46 mental health conditions. The outcomes were 1- and 2-year preventable hospitalization. For each of our two outcomes, we evaluated seven modelling approaches: four prediction models utilized logistic regression with different combinations of predictors to evaluate the relative contribution of each group of variables, and three prediction models utilized machine learning approaches - logistic regression with LASSO penalty, random forests (RF), and gradient boosting machine (GBM).

RESULTS

Our predictive model for 1-year hospitalization achieved an AUC of 0.803, with a sensitivity of 72% and a specificity of 76% under the optimum threshold of 0.463, and our predictive model for 2-year hospitalization achieved an AUC of 0.793, with a sensitivity of 76% and a specificity of 71% under the optimum threshold of 0.452. For predicting both 1-year and 2-year risk of preventable hospitalization, our best performing models utilized the machine learning approach of logistic regression with LASSO penalty which outperformed more black-box machine learning models like RF and GBM.

CONCLUSIONS

Our study demonstrates the feasibility of identifying depressed middle-aged adults at higher risk of future hospitalization due to burden of chronic illnesses using basic demographic information and diagnosis codes recorded in health insurance claims. Identifying this population may assist health care planners in developing effective screening strategies and management approaches and in efficient allocation of public healthcare resources as this population transitions to publicly funded healthcare programs, e.g., Medicare in the US.

The Costs Of Disparities In Preventable Heart Failure Hospitalizations In The US South, 2015-17

Black Americans in the US South have high rates of preventable heart failure hospitalizations, which reflects systemic inequities that also produce economic costs. We measured the direct medical costs of disparities in preventable heart failure admissions (that is, excess admissions) among Medicare beneficiaries living in six states in the US South (Kentucky, Arkansas, Florida, Georgia, Mississippi, and North Carolina). We used 2015-17 data from the Healthcare Cost and Utilization Project and constructed negative binomial models with state-level fixed effects to calculate adjusted admission rates with heart failure as the principal diagnosis. We calculated the number of these admissions that would have been avoided if Black, Hispanic, Asian/Pacific Islander, and American Indian/Alaska Native Medicare beneficiaries had the same admission rates as White beneficiaries. We found 28,213 excess admissions (48 percent excess) with $60,845,855 annual costs among Black beneficiaries, 3,499 (14 percent excess) with $8,179,381 annual costs among Hispanic beneficiaries, and 550 (51 percent excess) with $1,093,472 in annual costs among American Indian/Alaska Native beneficiaries. Failure to address heart failure treatment inequities in the community has a high opportunity cost.

Quantifying care delivery team influences on the hospitalization outcomes of patients with multimorbidity: Implications for clinical informatics

The primary objective was to quantify the influences of care delivery teams on the outcomes of patients with multimorbidity. Electronic medical record data on 68,883 patient care encounters (i.e., 54,664 patients) were extracted from the Arkansas Clinical Data Repository. Social network analysis assessed the minimum care team size associated with improved care outcomes (i.e., hospitalizations, days between hospitalizations, and cost) of patients with multimorbidity. Binomial logistic regression further assessed the influence of the presence of seven specific clinical roles. When compared to patients without multimorbidity, patients with multimorbidity had a higher mean age (i.e., 47.49 v. 40.61), a higher mean dollar amount of cost per encounter (i.e., $3,068 v. $2,449), a higher number of hospitalizations (i.e., 25 v. 4), and a higher number of clinicians engaged in their care (i.e., 139,391 v. 7,514). Greater network density in care teams (i.e., any combination of two or more Physicians, Residents, Nurse Practitioners, Registered Nurses, or Care Managers) was associated with a 46-98% decreased odds of having a high number of hospitalizations. Greater network density (i.e., any combination of two or more Residents or Registered Nurses) was associated with 11-13% increased odds of having a high cost encounter. Greater network density was not significantly associated with having a high number of days between hospitalizations. Analyzing the social networks of care teams may fuel computational tools that better monitor and visualize real-time hospitalization risk and care cost that are germane to care delivery.

Algorithms Identifying Low Acuity Emergency Department Visits: A Review and Validation Study

OBJECTIVE

To characterize and validate the landscape of algorithms that use International Classification of Disease (ICD) codes to identify low acuity emergency department (ED) visits.

DATA SOURCES

Publicly available ED data from the National Hospital Ambulatory Medical Care Survey (NHAMCS).

STUDY DESIGN

We systematically searched for studies that specify algorithms consisting of ICD codes that identify preventable or low acuity ED visits. We classified ED visits in NHAMCS according to these algorithms and compared agreement using the Jaccard index. We then evaluated the performance of each algorithm using positive predictive value (PPV) and sensitivity, with the reference group specified using low acuity composite (LAC) criteria consisting of both triage and clinical components. In sensitivity analyses, we repeated our primary analysis using only triage or only clinical criteria for reference.

DATA COLLECTION

We used 2011-2017 NHAMCS data, totaling 163,576 observations before survey weighting and after dropping observations missing a primary diagnosis. We translated ICD-9 codes (years 2011-2015) to ICD-10 using a standard crosswalk.

PRINCIPAL FINDINGS

We identified 15 papers with an original list of ICD codes used to identify preventable or low acuity ED presentations. These papers were published between 1992 and 2020, cited an average of 310 (SD 360) times, and included 968 (SD 1175) codes. Pairwise Jaccard similarity indices (0 = no overlap, 1 = perfect congruence) ranged from 0.01 to 0.82, with mean 0.20 (SD 0.13). When validated against the LAC reference group, the algorithms had an average PPV of 0.308 (95% CI [0.253, 0.364]) and sensitivity of 0.183 (95% CI [0.111, 0.256]). Overall, 2.1% of visits identified as low acuity by the algorithms died prehospital or in the ED, or needed surgery, critical care, or cardiac catheterization.

CONCLUSIONS

Existing algorithms that identify low acuity ED visits lack congruence and are imperfect predictors of visit acuity.

Potentially preventable hospital readmissions after patients' first stroke in Taiwan

Readmission is an important indicator of the quality of care. The purpose of this study was to explore the probabilities and predictors of 30-day and 1-year potentially preventable hospital readmission (PPR) after a patient's first stroke. We used claims data from the National Health Insurance (NHI) from 2010 to 2018. Multinomial logistic regression was used to assess the predictors of 30-day and 1-year PPR. A total of 41,921 discharged stroke patients was identified. We found that hospital readmission rates were 15.48% within 30-days and 47.25% within 1-year. The PPR and non-PPR were 9.84% (4123) and 5.65% (2367) within 30-days, and 30.65% (12,849) and 16.60% (6959) within 1-year, respectively. The factors of older patients, type of stroke, shorter length of stay, higher Charlson Comorbidity Index (CCI), higher stroke severity index (SSI), regional hospital, public and private hospital, and hospital in the lower urbanized area were associated significantly with the 30-day PPR. In addition, the factors of male, hospitalization year, and monthly income were associated significantly with 1-year PPR. The ORs of long-term PPR showed a decreasing trend since implementing the national health insurance post-acute care (PAC) program in 2014 and a dramatic drop in 2018 after the government expanded the long-term care plan-LTC 2.0 in 2017. The results showed that better discharge planning, implementing post-acute care programs and long-term care plan-LTC 2.0 may benefit the care of stroke patients and help reduce long-term readmission in Taiwan.

Impact of underlying malignancy on emergency department utilization and outcomes

PURPOSE

Cancer patients frequently utilize the emergency department (ED) for a variety of diagnoses both related to and unrelated to their cancer, yet ED outcomes for cancer patients are not well documented. This study sought to define risks and identify predictors for inpatient admission and hospital mortality among cancer patients presenting to the ED.

PATIENTS AND METHODS

We utilized the National Emergency Department Sample to identify patients with and without a diagnosis of cancer presenting to the ED between January 2016 and December 2018. We used multivariable mixed-effects logistic regression models to assess the influence of cancer on outcomes of hospital admission after the ED visit and hospital mortality for the whole patient cohort and individual presenting diagnoses.

RESULTS

There were 340 million weighted ED visits, of which 8.3 million (2.3%) were associated with a cancer diagnosis. Compared to non-cancer patients, patients with cancer had an increased risk of inpatient admission (64.7% vs. 14.8%; p < 0.0001) and hospital mortality (4.6% vs. 0.5%; p < 0.0001). For each of the top 15 presenting diagnoses, cancer patients had increased risks of hospitalization (odds ratio [OR] range 2.0-13.2) or death (OR range 2.1-14.4). Although our dataset does not contain reliable estimation of stage, cancer site was the most robust individual predictor associated with the risk of hospitalization or death compared to other clinical or system-related factors.

CONCLUSIONS

Cancer patients in the ED have high risks for hospital admission and death when compared to patients without cancer. Cancer patients represent a distinct population and may benefit from cancer-specific risk stratification or focused interventions to improve outcomes.

Exploration of Preventable Hospitalizations for Colorectal Cancer with the National Cancer Control Program in Taiwan

Background: Causing more than 40,000 deaths each year, cancer is one of the leading causes of mortality and preventable hospitalizations (PH) in Taiwan. To reduce the incidence and severity of cancer, the National Cancer Control Program (NCCP) includes screening for various types of cancer. A cohort study was conducted to explore the long-term trends in PH/person-years following NCCP intervention from 1997 to 2013. Methods: Trend analysis was carried out for long-term hospitalization. The Poisson regression model was used to compare PH/person-years before (1997–2004) and after intervention (2005–2013), and to explore the impact of policy intervention. Results: The policy response reduced 26% for the risk of hospitalization; in terms of comorbidity, each additional point increased the risk of hospitalization by 2.15 times. The risk of hospitalization doubled for each 10-year increase but was not statistically significant. Trend analysis validates changes in the number of hospitalizations/person-years in 2005. Conclusions: PH is adopted as an indicator for monitoring primary care quality, providing governments with a useful reference for which to gauge the adequacy, accessibility, and quality of health care. Differences in PH rates between rural and urban areas can also be used as a reference for achieving equitable distribution of medical resources.

Local Health and Social Services Spending to Reduce Preventable Hospitalizations

Upstream spending on social determinants of health can lead to improved downstream population health outcomes but intermediate steps between these end points are unclear. The purpose of this study was to determine the longitudinal impacts of government spending on hospital visits for potentially preventable conditions. The authors used secondary data sets from 2007-2014 to measure county-level Prevention Quality Indicator (PQI) rates, local government health and social services spending, hospital-provided community health services, and other sociodemographics. Mixed effects models regressed county PQI rates on deviation from mean local government spending from 4 years previously to account for lag between spending and outcomes. Thirty-two states reported PQI data; complete data were available for 1660 counties. Controlling for baseline spending levels, a 1-time $10 per capita increase in social services spending was associated with 1.9 fewer preventable hospitalizations (per 100,000) within 4 years (P < 0.001); $10 increases in public health or education were associated with 1.8 and 2.2 fewer preventable hospitalizations (per 100,000), respectively (P < 0.001). The association between change in spending and change in PQI was larger for acute than for chronic conditions. Additional health and social services spending by local governments can prevent hospitalizations for conditions for which quality outpatient care can potentially prevent the need for hospitalization or for which early intervention can prevent complications or progression of disease. Upstream spending can affect health care utilization and may offer a way to improve health outcomes or reshape the health care cost curve.

Potentially Preventable Medical Hospitalizations and Emergency Department Visits by the Behavioral Health Population

This study investigated geographic variation in potentially preventable medical outcomes that might be used to monitor access to high-quality medical care in the behavioral health population. Analyzing public and non-public data sources from California on adults admitted between 2009 and 2011 to all non-federal licensed medical inpatient (N = 6,603,146) or emergency department units (N = 21,011,958) revealed that 33.6% of nearly 1 million potentially preventable hospitalizations and 9.8% of 1.5 million potentially preventable emergency department visits were made by people with mental or substance use disorder diagnoses. Across California counties or county groups (N = 36), a higher preventable hospitalization rate in the behavioral health population was associated with higher poverty, higher primary care safety net utilization, and fewer mental health providers. Although further validation is required, rates of potentially preventable encounters, particularly hospitalizations, may be useful measures of access to high-quality care in the behavioral health population.

Family Caregiver Factors Associated With Emergency Department Utilization Among Community-Living Older Adults With Disabilities

Background: Older adults with disability are frequent users of the emergency department (ED) and often rely on family caregiver support. We identify whether and which caregiver characteristics are associated with older adults' ED use. Methods: We use Cox proportional hazards regression to model the likelihood of all-cause ED use (defined as 1 or more visits within 12 months of survey) as a function of caregiver characteristics after adjusting for older adult sociodemographic and health characteristics. We draw from linked older adult and caregiver surveys and administrative claims, creating a sample of 2521 community-living older adults with mobility/self-care disability receiving care from a family or unpaid caregiver. Results: About half (52.5%) of older adults receiving mobility or self-care help incurred 1 or more ED visits within 12 months of interview. Adjusting for year of data collection, sociodemographic characteristics, and health status, these older adults were at greater risk of all-cause ED use if their primary caregiver provided greater than 40 hours of care per week (hazard ratio [HR] 1.22, 95% CI 1.04-1.43; P = .02), helped with health care tasks (HR 1.26; 95% CI 1.08-1.46; P < .01), or experienced physical strain (HR 1.18; 95% CI 1.03-1.36; P = .02). Conclusion: Caregiver strain, helping with health care tasks, and greater hours of help per week are associated with heightened risk of ED use among older adults receiving mobility or self-care help. Study findings suggest the potential benefit of caregiver assessment and support.

Patient-Sharing Networks of Physicians and Health Care Utilization and Spending Among Medicare Beneficiaries

IMPORTANCE

Physicians are embedded in informal networks in which they share patients, information, and behaviors.

OBJECTIVE

We examined the association between physician network properties and health care spending, utilization, and quality of care among Medicare beneficiaries.

DESIGN, SETTING, AND PARTICIPANTS

In this cross-sectional study, we applied methods from social network analysis to Medicare administrative data from 2006 to 2010 for an average of 3 761 223 Medicare beneficiaries per year seen by 40 241 physicians practicing in 51 hospital referral regions (HRRs) to identify networks of physicians linked by shared patients. We improved on prior methods by restricting links to physicians who shared patients for distinct episodes of care, thereby excluding potentially spurious linkages between physicians treating common patients but for unrelated reasons. We also identified naturally occurring communities of more tightly linked physicians in each region. We examined the relationship between network properties measured in the prior year and outcomes in the subsequent year using regression models.

MAIN OUTCOMES AND MEASURES

Spending on total medical services, hospital, physician, and other services, use of services, and quality of care.

RESULTS

The mean patient age across the 5 years of study was 72.3 years and 58.5% of the participants were women. The mean age across communities of included physicians was 49 years and approximately 78% were men. Mean total annual spending per patient was $10 051. Total spending was higher for patients of physicians with more connections to other physicians ($1009 for a 1-standard deviation increase, P < .001) and more shared care outside of their community ($172, P < .001). Spending on inpatient care was slightly lower for patients of physicians whose communities had higher proportions of primary care physicians (-$38, P < .001). Patients cared for by physicians linked to more physicians also had more hospital admissions and days (0.02 and 0.18, respectively; both P < .001 for a 1-standard deviation increase in the number of connected physicians), more emergency visits (0.02, P < .001), more visits to specialists (0.37, P < .001), and more primary care visits (0.11, P < .001). Patients whose physicians' networks had more primary care physicians had more primary care visits (0.44, P < .001) and fewer specialist and emergency visits (-0.33 [P < .001] and -0.008 [P = .008], respectively). The various measures of quality were inconsistently related to the network measures.

CONCLUSIONS AND RELEVANCE

Characteristics of physicians' networks and the position of physicians in the network were associated with overall spending and utilization of services for Medicare beneficiaries.

Development and Validation of the Agency for Healthcare Research and Quality Measures of Potentially Preventable Emergency Department (ED) Visits: The ED Prevention Quality Indicators for General Health Conditions

OBJECTIVE

To develop and validate rates of potentially preventable emergency department (ED) visits as indicators of community health.

DATA SOURCES

Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project 2008-2010 State Inpatient Databases and State Emergency Department Databases.

STUDY DESIGN

Empirical analyses and structured panel reviews.

METHODS

Panels of 14-17 clinicians and end users evaluated a set of ED Prevention Quality Indicators (PQIs) using a Modified Delphi process. Empirical analyses included assessing variation in ED PQI rates across counties and sensitivity of those rates to county-level poverty, uninsurance, and density of primary care physicians (PCPs).

PRINCIPAL FINDINGS

ED PQI rates varied widely across U.S. communities. Indicator rates were significantly associated with county-level poverty, median income, Medicaid insurance, and levels of uninsurance. A few indicators were significantly associated with PCP density, with higher rates in areas with greater density. A clinical and an end-user panel separately rated the indicators as having strong face validity for most uses evaluated.

CONCLUSIONS

The ED PQIs have undergone initial validation as indicators of community health with potential for use in public reporting, population health improvement, and research.

Changes in Hospital Inpatient Utilization Following Health Care Reform

OBJECTIVE

To estimate the effects of 2014 Medicaid expansions on inpatient outcomes.

DATA SOURCES

Health Care Cost and Utilization Project State Inpatient Databases, 2011-2014; population and unemployment estimates.

STUDY DESIGN

Retrospective study estimating effects of Medicaid expansions using difference-in-differences regression. Outcomes included total admissions, referral-sensitive surgical and preventable admissions, length of stay, cost, and patient illness severity.

FINDINGS

In 2014 quarter four, compared with nonexpansion states, Medicaid admissions increased (28.5 percent, p = .006), and uninsured and private admissions decreased (-55.1 percent, p = .001, and -6.6 percent, p = .052), whereas all-payer admissions showed little change. Uninsured expansion effects were negative for preventable admissions (-24.4 percent, p = .068), length of stay (-9.3 percent, p = .039), total cost (-9.2 percent, p = .128), and illness severity (-4.5 percent, p = .397). Significant positive expansion effects were found for Medicaid referral-sensitive surgeries (11.8 percent, p = .021) and patient illness severity (2.3 percent, p = .015). Private and all-payer expansion effects for outcomes other than admission volume were small and mainly nonsignificant (p > .05).

CONCLUSION

Medicaid expansions did not change all-payer admission volumes, but they were associated with increased Medicaid and decreased uninsured volumes. Results suggest those previously uninsured with greater needs for inpatient services were most likely to gain coverage. Compositional changes in uninsured and Medicaid admissions may be due to selection.

Impact of Disease Prevalence Adjustment on Hospitalization Rates for Chronic Ambulatory Care-Sensitive Conditions in Germany

OBJECTIVES

To explore effects of disease prevalence adjustment on ambulatory care-sensitive hospitalization (ACSH) rates used for quality comparisons.

DATA SOURCES/STUDY SETTING

County-level hospital administrative data on adults discharged from German hospitals in 2011 and prevalence estimates based on administrative ambulatory diagnosis data were used.

STUDY DESIGN

A retrospective cross-sectional study using in- and outpatient secondary data was performed.

DATA COLLECTION

Hospitalization data for hypertension, diabetes, heart failure, chronic obstructive pulmonary disease, and asthma were obtained from the German Diagnosis Related Groups (DRG) database. Prevalence estimates were obtained from the German Central Research Institute of Ambulatory Health Care.

PRINCIPAL FINDINGS

Crude hospitalization rates varied substantially across counties (coefficients of variation [CV] 28-37 percent across conditions); this variation was reduced by prevalence adjustment (CV 21-28 percent). Prevalence explained 40-50 percent of the observed variation (r = 0.65-0.70) in ACSH rates for all conditions except asthma (r = 0.07). Between 30 percent and 38 percent of areas moved into or outside condition-specific control limits with prevalence adjustment.

CONCLUSIONS

Unadjusted ACSH rates should be used with caution for high-stakes public reporting as differences in prevalence may have a marked impact. Prevalence adjustment should be considered in models analyzing ACSH.

Trends and area variations in Potentially Preventable Admissions for COPD in Spain (2002-2013): a significant decline and convergence between areas

BACKGROUND

Potentially Preventable Hospitalizations (PPH) are hospital admissions for conditions which are preventable with timely and appropriate outpatient care being Chronic Obstructive Pulmonary Disease (COPD) admissions one of the most relevant PPH. We estimate the population age-sex standardized relative risk of admission for COPD-PPH by year and area of residence in the Spanish National Health System (sNHS) during the period 2002-2013.

METHODS

The study was conducted in the 203 Hospital Service Areas of the sNHS, using the 2002 to 2013 hospital admissions for a COPD-PPH condition of patients aged 20 and over. We use conventional small area variation statistics and a Bayesian hierarchical approach to model the different risk structures of dependence in both space and time.

RESULTS

COPD-PPH admissions declined from 24.5 to 15.5 per 10,000 persons-year (Men: from 40.6 to 25.1; Women: from 9.1 to 6.4). The relative risk declined from 1.19 (19 % above 2002-2013 average) in 2002 to 0.77 (30 % below average) in 2013. Both the starting point and the slope were different for the different regions. Variation among admission rates between extreme areas dropped from 6.7 times higher in 2002 to 4.6 times higher in 2013.

CONCLUSIONS

COPD-PPH conditions in Spain have undergone a strong decline and a reduction in geographical variation in the last 12 years, suggesting a general improvement in health policies and health care over time. Variability among areas still remains, with a substantial room for improvement.

Primary care team communication networks, team climate, quality of care, and medical costs for patients with diabetes: A cross-sectional study

BACKGROUND

Primary care teams play an important role in providing the best quality of care to patients with diabetes. Little evidence is available on how team communication networks and team climate contribute to high quality diabetes care.

OBJECTIVE

To determine whether primary care team communication and team climate are associated with health outcomes, health care utilization, and associated costs for patients with diabetes.

METHODS

A cross-sectional survey of primary care team members collected information on frequency of communication with other care team members about patient care and on team climate. Patient outcomes (glycemic, cholesterol, and blood pressure control, urgent care visits, emergency department visits, hospital visit days, medical costs) in the past 12 months for team diabetes patient panels were extracted from the electronic health record. The data were analyzed using nested (clinic/team/patient) generalized linear mixed modeling.

PARTICIPANTS

155 health professionals at 6 U.S. primary care clinics participated from May through December 2013.

RESULTS

Primary care teams with a greater number of daily face-to-face communication ties among team members were associated with 52% (rate ratio=0.48, 95% CI: 0.22, 0.94) fewer hospital days and US$1220 (95% CI: -US$2416, -US$24) lower health-care costs per team diabetes patient in the past 12 months. In contrast, for each additional registered nurse (RN) who reported frequent daily face-to-face communication about patient care with the primary care practitioner (PCP), team diabetes patients had less-controlled HbA1c (Odds ratio=0.83, 95% CI: 0.66, 0.99), increased hospital days (RR=1.57, 95% CI: 1.10, 2.03), and higher healthcare costs (β=US$877, 95% CI: US$42, US$1713). Shared team vision, a measure of team climate, significantly mediated the relationship between team communication and patient outcomes.

CONCLUSIONS

Primary care teams which relied on frequent daily face-to-face communication among more team members, and had a single RN communicating patient care information to the PCP, had greater shared team vision, better patient outcomes, and lower medical costs for their diabetes patient panels.

Preventable Admissions on a General Medicine Service: Prevalence, Causes and Comparison with AHRQ Prevention Quality Indicators-A Cross-Sectional Analysis

BACKGROUND

Rates of preventable admissions will soon be publicly reported and used in calculating performance-based payments. The current method of assessing preventable admissions, the Agency of Healthcare Research and Quality (AHRQ) Preventable Quality Indicators (PQI) rate, is drawn from claims data and was originally designed to assess population-level access to care.

OBJECTIVE

To identify the prevalence and causes of preventable admissions by attending physician review and to compare its performance with the PQI tool in identifying preventable admissions.

DESIGN

Cross-sectional survey.

SETTING

General medicine service at an academic medical center.

PARTICIPANTS

Consecutive inpatient admissions from December 1-15, 2013.

MAIN MEASURES

Survey of inpatient attending physicians regarding the preventability of the admissions, primary contributing factors and feasibility of prevention. For the same patients, the PQI tool was applied to determine the claims-derived preventable admission rate.

KEY RESULTS

Physicians rated all 322 admissions and classified 122 (38 %) as preventable, of which 31 (25 %) were readmissions. Readmissions were more likely to be rated preventable than other admissions (49 % vs. 35 %, p = 0.04). Application of the AHRQ PQI methodology identified 75 (23 %) preventable admissions. Thirty-one admissions (10 %) were classified as preventable by both methods, and the majority of admissions considered preventable by the AHRQ PQI method (44/78) were not considered preventable by physician assessment (K = 0.04). Of the preventable admissions, physicians assigned patient factors in 54 (44 %), clinician factors in 36 (30 %) and system factors in 32 (26 %).

CONCLUSIONS

A large proportion of admissions to a general medicine service appeared preventable, but AHRQ's PQI tool was unable to identify these admissions. Before initiation of the PQI rate for use in pay-for-performance programs, further study is warranted.

Estimating burden and disease costs of exposure to endocrine-disrupting chemicals in the European union

CONTEXT

Rapidly increasing evidence has documented that endocrine-disrupting chemicals (EDCs) contribute substantially to disease and disability.

OBJECTIVE

The objective was to quantify a range of health and economic costs that can be reasonably attributed to EDC exposures in the European Union (EU).

DESIGN

A Steering Committee of scientists adapted the Intergovernmental Panel on Climate Change weight-of-evidence characterization for probability of causation based upon levels of available epidemiological and toxicological evidence for one or more chemicals contributing to disease by an endocrine disruptor mechanism. To evaluate the epidemiological evidence, the Steering Committee adapted the World Health Organization Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group criteria, whereas the Steering Committee adapted definitions recently promulgated by the Danish Environmental Protection Agency for evaluating laboratory and animal evidence of endocrine disruption. Expert panels used the Delphi method to make decisions on the strength of the data.

RESULTS

Expert panels achieved consensus at least for probable (>20%) EDC causation for IQ loss and associated intellectual disability, autism, attention-deficit hyperactivity disorder, childhood obesity, adult obesity, adult diabetes, cryptorchidism, male infertility, and mortality associated with reduced testosterone. Accounting for probability of causation and using the midpoint of each range for probability of causation, Monte Carlo simulations produced a median cost of €157 billion (or $209 billion, corresponding to 1.23% of EU gross domestic product) annually across 1000 simulations. Notably, using the lowest end of the probability range for each relationship in the Monte Carlo simulations produced a median range of €109 billion that differed modestly from base case probability inputs.

CONCLUSIONS

EDC exposures in the EU are likely to contribute substantially to disease and dysfunction across the life course with costs in the hundreds of billions of Euros per year. These estimates represent only those EDCs with the highest probability of causation; a broader analysis would have produced greater estimates of burden of disease and costs.

Higher practice intensity is associated with higher quality of care but more avoidable admissions for medicare beneficiaries

BACKGROUND

The relationship between practice intensity and the quality and outcomes of care has not been studied.

OBJECTIVE

To examine the relationship between primary care physicians' costliness both for defined episodes of care and for defined patients and the quality and outcomes of care delivered to Medicare beneficiaries.

STUDY DESIGN

Cross sectional analysis of physician survey data linked to Medicare claims. Physician costliness measures were calculated by comparing the episode specific and overall costs of care for their patients with the care delivered by other physicians.

PARTICIPANTS

We studied physicians participating in the 2004-2005 Community Tracking Study Physician Survey linked with administrative claims from the Medicare program for the years 2004-2006.

MAIN MEASURES

Proportion of eligible beneficiaries receiving each of seven preventive services and rates of preventable admissions for acute and chronic conditions.

KEY RESULTS

The 2,211 primary care physician respondents included 937 internists and 1,274 family or general physicians who were linked to more than 250,000 Medicare enrollees. Patients treated by more costly physicians (whether measured by the overall costliness index or the episode-level index) were more likely to receive recommended preventive services, but were also more likely to experience preventable admissions. For instance, physicians in the lowest quartile of costliness performed appropriate monitoring for hemoglobin A1C for diabetics 72.8% of the time, as compared with 81.9% for physicians in the highest quartile of costliness (p < 0.01). In contrast, patients treated by the physicians in the lowest quartile of episode costliness were admitted at a rate of 1.8/100 for both acute and chronic Prevention Quality Indicators (PQIs), as compared with 2.9/100 for both acute and chronic PQIs for those treated by physicians in the highest quartile of costliness (p < 0.001).

CONCLUSIONS

Physician practice patterns are associated with the quality of preventive services delivered to Medicare patients. Ongoing efforts to influence physician practice patterns may have differential effects on different aspects of quality.

Joint spatial modeling to identify shared patterns among chronic related potentially preventable hospitalizations

BACKGROUND

Rates of Potentially Preventable Hospitalizations (PPH) are used to evaluate access of territorially delimited populations to high quality ambulatory care. A common geographic pattern of several PPH would reflect the performance of healthcare providers. This study is aimed at modeling jointly the geographical variation in six chronic PPH conditions in one Spanish Autonomous Community for describing common and discrepant patterns, and to assess the relative weight of the common pattern on each condition.

METHODS

Data on the 39,970 PPH hospital admissions for diabetes short term complications, chronic obstructive pulmonary disease (COPD), congestive heart failure, dehydration, angina admission and adult asthma, between 2007 and 2009 were extracted from the Hospital Discharge Administrative Databases and assigned to one of the 240 Basic Health Zones. Rates and Standardized Hospitalization Ratios per geographic unit were estimated. The spatial analysis was carried out jointly for PPH conditions using Shared Component Models (SCM).

RESULTS

The component shared by the six PPH conditions explained about the 36% of the variability of each PPH condition, ranging from the 25.9 for dehydration to 58.7 for COPD. The geographical pattern found in the latent common component identifies territorial clusters with particularly high risk. The specific risk pattern that each isolated PPH does not share with the common pattern for all six conditions show many non-significant areas for most PPH, but with some exceptions.

CONCLUSIONS

The geographical distribution of the risk of the PPH conditions is captured in a 36% by a unique latent pattern. The SCM modeling may be useful to evaluate healthcare system performance.

Comparing potentially avoidable hospitalization rates related to ambulatory care sensitive conditions in Switzerland: the need to refine the definition of health conditions and to adjust for population health status

Background

Regional rates of hospitalization for ambulatory care sensitive conditions (ACSC) are used to compare the availability and quality of ambulatory care but the risk adjustment for population health status is often minimal. The objectives of the study was to examine the impact of more extensive risk adjustment on regional comparisons and to investigate the relationship between various area-level factors and the properly adjusted rates.

Methods

Our study is an observational study based on routine data of 2 million anonymous insured in 26 Swiss cantons followed over one or two years. A binomial negative regression was modeled with increasingly detailed information on health status (age and gender only, inpatient diagnoses, outpatient conditions inferred from dispensed drugs and frequency of physician visits). Hospitalizations for ACSC were identified from principal diagnoses detecting 19 conditions, with an updated list of ICD-10 diagnostic codes. Co-morbidities and surgical procedures were used as exclusion criteria to improve the specificity of the detection of potentially avoidable hospitalizations. The impact of the adjustment approaches was measured by changes in the standardized ratios calculated with and without other data besides age and gender.

Results

25% of cases identified by inpatient main diagnoses were removed by applying exclusion criteria. Cantonal ACSC hospitalizations rates varied from to 1.4 to 8.9 per 1,000 insured, per year. Morbidity inferred from diagnoses and drugs dramatically increased the predictive performance, the greatest effect found for conditions linked to an ACSC. More visits were associated with fewer PAH although very high users were at greater risk and subjects who had not consulted at negligible risk. By maximizing health status adjustment, two thirds of the cantons changed their adjusted ratio by more than 10 percent. Cantonal variations remained substantial but unexplained by supply or demand.

Conclusion

Additional adjustment for health status is required when using ACSC to monitor ambulatory care. Drug-inferred morbidities are a promising approach.

Preventable readmissions within 30 days of ischemic stroke among Medicare beneficiaries

BACKGROUND AND PURPOSE

The Centers for Medicare and Medicaid Services proposes to use 30-day hospital readmissions after ischemic stroke as part of the Hospital Inpatient Quality Reporting Program for payment determination beginning in 2016. The proportion of poststroke readmissions that is potentially preventable is unknown.

METHODS

Thirty-day readmissions for all Medicare fee-for-service beneficiaries aged≥65 years discharged alive with a primary diagnosis of ischemic stroke (International Classification of Diseases, Ninth Revision, Clinical Modification 433, 434, 436) between December 2005 and November 2006 were analyzed. Preventable readmissions were identified based on 14 Prevention Quality Indicators developed for use with administrative data by the US Agency for Healthcare Research and Quality. National, hospital-level, and regional preventable readmission rates were estimated. Random-effects logistic regression was also used to determine patient-level factors associated with preventable readmissions.

RESULTS

Among 307 887 ischemic stroke discharges, 44 379 (14.4%) were readmitted within 30 days; 5322 (1.7% of all discharges) were the result of a preventable cause (eg, pneumonia), and 39 057 (12.7%) were for other reasons (eg, cancer). In multivariate analysis, older age and cardiovascular-related comorbid conditions were strong predictors of preventable readmissions. Preventable readmission rates were highest in the Southeast, Mid-Atlantic, and US territories and lowest in the Mountain and Pacific regions.

CONCLUSIONS

On the basis of Agency for Healthcare Research and Quality Prevention Quality Indicators, we found that a small proportion of readmissions after ischemic stroke were classified as preventable. Although other causes of readmissions not reflected in the Agency for Healthcare Research and Quality measures could also be avoidable, hospital-level programs intended to reduce all-cause readmissions and costs should target high-risk patients.

Potentially avoidable hospitalizations among Medicare beneficiaries with Alzheimer's disease and related disorders

BACKGROUND

Individuals with Alzheimer's disease and related disorders (ADRD) have more frequent hospitalizations than individuals without ADRD, and some of these admissions may be preventable with proactive outpatient care.

METHODS

This study was a cross-sectional analysis of Medicare claims data from 195,024 fee-for-service ADRD beneficiaries aged ≥65 years and an equal number of matched non-ADRD controls drawn from the 5% random sample of Medicare beneficiaries in 2007-2008. We analyzed the proportion of patients with potentially avoidable hospitalizations (PAHs, as defined by the Medicare Ambulatory Care Indicators for the Elderly) and used logistic regression to examine patient characteristics associated with PAHs. We used paired t tests to compare Medicare expenditures by ADRD status, stratified by whether there were PAHs related to a particular condition.

RESULTS

Compared with matched non-ADRD subjects, Medicare beneficiaries with ADRD were significantly more likely to have PAHs for diabetes short-term complications (OR = 1.43; 95% CI 1.31-1.57), diabetes long-term complications (OR = 1.08; 95% CI = 1.02-1.14), and hypertension (OR = 1.22; 95% CI 1.08-1.38), but less likely to have PAHs for chronic obstructive pulmonary disease (COPD)/asthma (OR = 0.85; 95% CI 0.82-0.87) and heart failure (OR = 0.89; 95% CI 0.86-0.92). Risks of PAHs increased significantly with comorbidity burden. Among beneficiaries with a PAH, total Medicare expenditures were significantly higher for those subjects who also had ADRD.

CONCLUSION

Medicare beneficiaries with ADRD were at a higher risk of PAHs for certain uncontrolled comorbidities and incurred higher Medicare expenditures compared with matched controls without dementia. ADRD appears to make the management of some comorbidities more difficult and expensive. Ideally, ADRD programs should involve care management targeting high-risk patients with multiple chronic conditions.