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Camille Robichaux,1 Jordan Aron,2 Chris H Wendt,1,3 Jesse D Berman,4 Austin Rau,4 Ann Bangerter,5 R Adams Dudley,1,3,5 Arianne K Baldomero1,3,5 1Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Minnesota, Minneapolis, MN, USA; 2Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, MN, USA; 3Pulmonary, Allergy, Critical Care, and Sleep Medicine, Minneapolis VA Health Care System, Minneapolis, MN, USA; 4Division of Environmental Health Sciences, University of Minnesota School of Public Health, Minneapolis, MN, USA; 5Center for Care Delivery and Outcomes Research, Minneapolis VA Health Care System, Minneapolis, MN, USACorrespondence: Camille Robichaux, Division of Pulmonary, Allergy, Critical Care, and Sleep, University of Minnesota, 420 Delaware Street SE, MMC 276, Minneapolis, MN, 55455, USA, Tel +1 612 624-0999, Email robic020@umn.eduBackground: Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality worldwide. Identifying both individual and community risk factors associated with higher mortality is essential to improve outcomes. Few population-based studies of mortality in COPD include both individual characteristics and community risk factors.Objective: We used geocoded, patient-level data to describe the associations between individual demographics, neighborhood socioeconomic status, and all-cause mortality.Methods: We performed a nationally representative retrospective cohort analysis of all patients enrolled in the Veteran Health Administration with at least one ICD-9 or ICD-10 code for COPD in 2016– 2019. We obtained demographic characteristics, comorbidities, and geocoded residential address. Area Deprivation Index and rurality were classified using individual geocoded residential addresses. We used logistic regression models to assess the association between these characteristics and age-adjusted all-cause mortality.Results: Of 1,106,163 COPD patients, 33.4% were deceased as of January 2021. In age-adjusted models, having more comorbidities, Black/African American race (OR 1.09 [95% CI: 1.08– 1.11]), and higher neighborhood disadvantage (OR 1.30 [95% CI: 1.28– 1.32]) were associated with all-cause mortality. Female sex (OR 0.67 [95% CI: 0.65– 0.69]), Asian race (OR 0.64, [95% CI: 0.59– 0.70]), and living in a more rural area were associated with lower odds of all-cause mortality. After adjusting for age, comorbidities, neighborhood socioeconomic status, and rurality, the association with Black/African American race reversed.Conclusion: All-cause mortality in COPD patients is disproportionately higher in patients living in poorer neighborhoods and urban areas, suggesting the impact of social determinants of health on COPD outcomes. Black race was associated with higher age-adjusted all-cause mortality, but this association was abrogated after adjusting for gender, socioeconomic status, comorbidities, and urbanicity. Future studies should focus on exploring mechanisms by which disparities arise and developing interventions to address these.Keywords: chronic obstructive pulmonary disease, social determinants, race, geography, socioeconomic status

Neal Yuan,1 Christy Boscardin,2 Nadra E Lisha,2 R Adams Dudley,3,4 Grace A Lin2 1Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; 2Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, CA, USA; 3Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Minnesota, Minneapolis, MN, USA; 4School of Public Health, University of Minnesota, Minneapolis, MN, USACorrespondence: Neal YuanSmidt Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd., Davis 1015, Los Angeles, CA 90048, USAEmail Neal.Yuan@cshs.orgBackground: In stable coronary artery disease (CAD), shared decision-making (SDM) is encouraged when deciding whether to pursue percutaneous coronary intervention (PCI) given similar cardiovascular outcomes between PCI and medical therapy. However, it remains unclear whether improving patient-provider communication and patient knowledge, the main tenets of SDM, changes patient preferences or the treatment chosen. We explored the relationships between patient-provider communication, patient knowledge, patient preferences, and the treatment received.Methods: We surveyed stable CAD patients referred for elective cardiac catheterization at seven hospitals from 6/2016 to 9/2018. Surveys assessed patient-provider communication, medical knowledge, and preferences for treatment and decision-making. We verified treatments received by chart review. We used linear and logistic regression to examine relationships between patient-provider communication and knowledge, knowledge and preference, and preference and treatment received.Results: Eighty-seven patients completed the survey. More discussion of the benefits and risks of both medical therapy and PCI associated with higher patient knowledge scores (β=0.28, p< 0.01). Patient knowledge level was not associated with preference for PCI (OR=0.78, 95% CI 0.57– 1.03, p=0.09). Black patients had more than four times the odds of preferring medical therapy to PCI (OR=4.49, 1.22– 18.45, p=0.03). Patients preferring medical therapy were not significantly less likely to receive PCI (OR=0.67, 0.16– 2.52, p=0.57).Conclusions: While communicating the risks of PCI may improve patient knowledge, this knowledge may not affect patient treatment preferences. Rather, other factors such as race may be significantly more influential on a patient’s treatment preferences. Furthermore, patient preferences are still not well reflected in the treatment received. Improving shared decision-making in stable CAD therefore may require not only increasing patient education but also better understanding and including a patient’s background and pre-existing beliefs.Keywords: stable coronary artery disease, stable angina, shared decision-making, percutaneous coronary intervention, stent, optimal medical therapy

© 2015 Wolters Kluwer Health, Inc. All rights reserved. BACKGROUND:: Hospital report cards and financial incentives linked to performance require clinical data that are reliable, appropriate, timely, and cost-effective to process. Pay-for-performance plans are transitioning to automated electronic health record (EHR) data as an efficient method to generate data needed for these programs. OBJECTIVE:: To determine how well data from automated processing of structured fields in the electronic health record (AP-EHR) reflect data from manual chart review and the impact of these data on performance rewards. RESEARCH DESIGN:: Cross-sectional analysis of performance measures used in a cluster randomized trial assessing the impact of financial incentives on guideline-recommended care for hypertension. SUBJECTS:: A total of 2840 patients with hypertension assigned to participating physicians at 12 Veterans Affairs hospital-based outpatient clinics. Fifty-two physicians and 33 primary care personnel received incentive payments. MEASURES:: Overall, positive and negative agreement indices and Cohen’s kappa were calculated for assessments of guideline-recommended antihypertensive medication use, blood pressure (BP) control, and appropriate response to uncontrolled BP. Pearson’s correlation coefficient was used to assess how similar participants’ calculated earnings were between the data sources. RESULTS:: By manual chart review data, 72.3% of patients were considered to have received guideline-recommended antihypertensive medications compared with 65.0% by AP-EHR review (κ=0.51). Manual review indicated 69.5% of patients had controlled BP compared with 66.8% by AP-EHR review (κ=0.87). Compared with 52.2% of patients per the manual review, 39.8% received an appropriate response by AP-EHR review (κ=0.28). Participants’ incentive payments calculated using the 2 methods were highly correlated (r≥0.98). Using the AP-EHR data to calculate earnings, participants’ payment changes ranged from a decrease of $91.00 (−30.3%) to an increase of $18.20 (+7.4%) for medication use (interquartile range, −14.4% to 0%) and a decrease of $100.10 (−31.4%) to an increase of $36.40 (+15.4%) for BP control or appropriate response to uncontrolled BP (interquartile range, −11.9% to −6.1%). CONCLUSIONS:: Pay-for-performance plans that use only EHR data should carefully consider the measures and the structure of the EHR before data collection and financial incentive disbursement. For this study, we feel that a 10% difference in the total amount of incentive earnings disbursed based on AP-EHR data compared with manual review is acceptable given the time and resources required to abstract data from medical records.

Abstract. Background: In December 2000, Medicare eliminated time limitations in immunosuppressant coverage after kidney transplant for beneficiaries age 65 and those who were disabled. This change did not apply to younger non-disabled beneficiaries who qualified for Medicare only because of their end-stage renal disease (ESRD). We sought to examine access to waitlisting for kidney transplantation in a cohort spanning this policy change. Methods. This was a retrospective cohort analysis of 241,150 Medicare beneficiaries in the United States Renal Data System who initiated chronic dialysis between 1/1/96 and 11/30/03. We fit interrupted time series Cox proportional hazard models to compare access to kidney transplant waitlist within 12months of initiating chronic dialysis by age/disability status, accounting for secular trends. Results: Beneficiaries age

Background: Systemic lupus erythematosus (SLE) affects 1 in 2500 Americans and is associated with significant morbidity and mortality. The recent development of SLE quality measures provides an opportunity to understand gaps in clinical care and to identify modifiable factors associated with variations in quality.Objective: To evaluate performance on SLE quality measures as well as differences in quality of care by demographic, socioeconomic, disease, and health system characteristics.Design and Patients: Cross-sectional analysis of data derived from the Lupus Outcomes Study, a prospective, longitudinal study of 814 individuals. Principal data collection was through annual structured telephone surveys between 2009-2010. Data on 13 SLE quality measures was collected. We used regression models to estimate demographic, socioeconomic, disease, and health system characteristics associated with performance on individual and overall quality measures.Outcome Measures: Performance on each quality measure and overall performance on all measures for which participants were eligible (pass rate).Results: Participants were eligible for a mean of five measures (range 2-12). Performance varied from 29 % (assessment of cardiovascular risk factors) to 90 % (sun avoidance counseling). The overall pass rate was 65 % (95 % CI 64 %, 65 %). In unadjusted analyses, younger age, minority race/ethnicity, poverty, shorter disease duration, fewer physician visits, and lack of health insurance, were associated with lower pass rates. Receiving care in public sector managed care organizations was associated with higher pass rates. After adjustment, younger age, having fewer physician visits and lacking health insurance remained significantly associated with lower performance; receiving care in public sector managed care organizations remained associated with higher performance.Conclusions: We identified a number of gaps in clinical care for SLE. Factors associated with the health care system, including presence and type of health insurance, were the primary determinants of performance on quality measures in SLE. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To examine racial/ethnic and economic variation in cost-related medication underuse among insured adults with diabetes. RESEARCH DESIGN AND METHODS: We surveyed 5,086 participants from the multicenter Translating Research Into Action for Diabetes Study. Respondents reported whether they used less medication because of cost in the past 12 months. We examined unadjusted and adjusted rates of cost-related medication underuse, using hierarchical regression, to determine whether race/ethnicity differences still existed after accounting for economic, health, and other demographic variables. RESULTS: Participants were 48% white, 14% African American, 14% Latino, 15% Asian/Pacific Islander, and 8% other. Overall, 14% reported cost-related medication underuse. Unadjusted rates were highest for Latinos (23%) and African Americans (17%) compared with whites (13%), Asian/Pacific Islanders (11%), and others (15%). In multivariate analyses, race/ethnicity significantly predicted cost-related medication underuse (P = 0.048). However, adjusted rates were only slightly higher for Latinos (14%) than whites (10%) (P = 0.026) and were not significantly different for African Americans (11%), Asian/Pacific Islanders (7%), and others (11%). Income and out-of-pocket drug costs showed the greatest differences in adjusted rates of cost-related medication underuse (15 vs. 5% for participants with income $50,000 and 24 vs. 7% for participants with out-of-pocket costs >$150 per month vs.

Background: Maintaining independence in daily functioning is an important health outcome in older adults. A key measure of functional independence in elders is the ability to do activities of daily living (ADL) without the assistance of another person. However, few prognostic indices have been developed that stratify elders into groups at variable risk for developing ADL dependence.Objective: We sought to develop and validate a prognostic index that distinguishes between elders at different risk of ADL dependence.Research Design, Subjects, and Measures: We studied subjects enrolled in Asset and Health Dynamics Among the Oldest Old (AHEAD), a nationally representative cohort of elders older than the age of 70. We included 5239 subjects (mean age, 77) reporting that they could do each of 5 ADL (bathing, dressing, toileting, transferring, and eating) without the assistance of another person at baseline. Subjects were divided into development (n = 3245) and validation (n = 1994) samples based on region of the United States. Our primary outcome was the need for help (dependence) with at least one ADL at 2 years. We used logistic regression to select among predictor variables encompassing several domains: demographic characteristics, comorbid conditions, functional status, cognitive status, and general health indicators.Results: The 9 independent predictors of 2-year ADL dependence were age older than 80, diabetes, difficulty walking several blocks, difficulty bathing or dressing, need for help with personal finances, difficulty lifting 10 pounds, inability to name the Vice President, history of falling, and low body mass index. We created a risk score by assigning 1 point to each risk factor. In the development sample, rates of 2-year ADL dependence in subjects with 0, 1, 2, 3, 4, and 5 or more risk factors were 1.3%, 2.8%, 3.8%, 10%, 22%, and 33%, respectively (P < 0.001, roc area = 0.79). In the validation sample, the rates were 0.7%, 4.3%, 8.7%, 11%, 18%, and 40% (P < 0.001, roc area = 0.77). The risk score also discriminated between subjects at variable risk for a combined outcome of either ADL decline or death (4.3%, 7.6%, 15%, 21%, 30%, and 47%).Conclusion: Using data available from patient reports, we validated a simple risk index that distinguished between elders at variable risk of ADL dependence. This index may be useful for identifying elders at high risk of poor outcomes or for risk adjustment. [ABSTRACT FROM AUTHOR]

Objective: Access to firearms is associated with increased suicide risk. Our aim was to develop a natural language processing approach to characterizing firearm access in clinical records., Materials and Methods: We used clinical notes from 36 685 Veterans Health Administration (VHA) patients between April 10, 2023 and April 10, 2024. We expanded preexisting firearm term sets using subject matter experts and generated 250-character snippets around each firearm term appearing in notes. Annotators labeled 3000 snippets into three classes. Using these annotated snippets, we compared four nonneural machine learning models (random forest, bagging, gradient boosting, logistic regression with ridge penalization) and two versions of Bidirectional Encoder Representations from Transformers, or BERT (specifically, BioBERT and Bio-ClinicalBERT) for classifying firearm access as "definite access", "definitely no access", or "other"., Results: Firearm terms were identified in 36 685 patient records (41.3%), 33.7% of snippets were categorized as definite access, 9.0% as definitely no access, and 57.2% as "other". Among models classifying firearm access, five of six had acceptable performance, with BioBERT and Bio-ClinicalBERT performing best, with F1s of 0.876 (95% confidence interval, 0.874-0.879) and 0.896 (95% confidence interval, 0.894-0.899), respectively., Discussion and Conclusion: Firearm-related terminology is common in the clinical records of VHA patients. The ability to use text to identify and characterize patients' firearm access could enhance suicide prevention efforts, and five of our six models could be used to identify patients for clinical interventions., (Published by Oxford University Press on behalf of the American Medical Informatics Association 2024.)

Background: Many patients receive guideline-discordant inhaler regimens after chronic obstructive pulmonary disease (COPD) hospitalization. Geography and fragmented care across multiple providers likely influence prescription of guideline-discordant inhaler regimens, but these have not been comprehensively studied. We assessed patient-level differences in guideline-discordant inhaler regimens by rurality, drive time to pulmonary specialty care, and fragmented care., Methods: Retrospective cohort analysis using national Veterans Health Administration (VA) data among patients who received primary care and prescriptions from the VA. Patients hospitalized for COPD exacerbation between 2017 and 2020 were assessed for guideline-discordant inhaler regimens in the subsequent 3 months. Guideline-discordant inhaler regimens were defined as short-acting inhaler/s only, inhaled corticosteroid (ICS) monotherapy, long-acting beta-agonist (LABA) monotherapy, ICS + LABA, long-acting muscarinic antagonist (LAMA) monotherapy, or LAMA + ICS. Rural residence and drive time to the closest pulmonary specialty care were obtained from geocoded addresses. Fragmented care was defined as hospitalization outside the VA. We used multivariable logistic regression models to assess associations between rurality, drive time, fragmentated care, and guideline-discordant inhaler regimens. Models were adjusted for age, sex, race/ethnicity, Charlson Comorbidity Index, Area Deprivation Index, and region., Findings: Of 33,785 patients, 16,398 (48.6%) received guideline-discordant inhaler regimens 3 months after hospitalization. Rural residents had higher odds of guideline-discordant inhalers regimens compared to their urban counterparts (adjusted odds ratio [aOR] 1.18 [95% CI: 1.12-1.23]). The odds of receiving guideline-discordant inhaler regimens increased with longer drive time to pulmonary specialty care (aOR 1.38 [95% CI: 1.30-1.46] for drive time >90 min compared to <30 min). Fragmented care was also associated with higher odds of guideline-discordant inhaler regimens (aOR 1.56 [95% CI: 1.48-1.63])., Interpretation: Rurality, long drive time to care, and fragmented care were associated with greater prescription of guideline-discordant inhaler regimens after COPD hospitalization. These findings highlight the need to understand challenges in delivering evidence-based care., Funding: NIHNCATS grants KL2TR002492 and UL1TR002494., Competing Interests: AKB reported grants from the National Institutes of Health. CHS reported grants from the Health Resources and Services Administration, Federal Office of Rural Health Policy, the National Institutes of Health and the CDC; payment or honoraria from Iowa Primary Care Association, Southern Gerontological Society, and the University of Michigan; and leadership or fiduciary role in the National Rural Health Association Board of Trustees. KMK reported personal fees from Nuvaira and Organicell (Data and Safety Monitoring Boards) outside of this work; consulting fees from Allergan/AbbVie and leadership or fiduciary role in the American Thoracic Society Proposal Review Committee, the American Thoracic Society Clinical Problems, and the Assembly Planning Committee.

Importance: Many patients do not receive recommended services. Drive time to health care services may affect receipt of guideline-recommended care, but this has not been comprehensively studied., Objective: To assess associations between drive time to care and receipt of guideline-recommended screening, diagnosis, and treatment interventions., Design, Setting, and Participants: This cohort study used administrative data from the National Veterans Health Administration (VA) data merged with Medicare data. Eligible participants were patients using VA services between January 2016 and December 2019. Women ages 65 years or older without underlying bone disease were assessed for osteoporosis screening. Patients with new diagnosis of chronic obstructive pulmonary disease (COPD) indicated by at least 2 encounter codes for COPD or at least 1 COPD-related hospitalization were assessed for receipt of diagnostic spirometry. Patients hospitalized for ischemic heart disease were assessed for cardiac rehabilitation treatment., Exposures: Drive time from each patient's residential address to the closest VA facility where the service was available, measured using geocoded addresses., Main Outcomes and Measures: Binary outcome at the patient level for receipt of osteoporosis screening, spirometry, and cardiac rehabilitation. Multivariable logistic regression models were used to assess associations between drive time and receipt of services., Results: Of 110 780 eligible women analyzed, 36 431 (32.9%) had osteoporosis screening (mean [SD] age, 66.7 [5.4] years; 19 422 [17.5%] Black, 63 403 [57.2%] White). Of 281 130 patients with new COPD diagnosis, 145 249 (51.7%) had spirometry (mean [SD] age, 68.2 [11.5] years; 268 999 [95.7%] men; 37 834 [13.5%] Black, 217 608 [77.4%] White). Of 73 146 patients hospitalized for ischemic heart disease, 11 171 (15.3%) had cardiac rehabilitation (mean [SD] age, 70.0 [10.8] years; 71 217 [97.4%] men; 15 213 [20.8%] Black, 52 144 [71.3%] White). The odds of receiving recommended services declined as drive times increased. Compared with patients with a drive time of 30 minutes or less, patients with a drive time of 61 to 90 minutes had lower odds of receiving osteoporosis screening (adjusted odds ratio [aOR], 0.90; 95% CI, 0.86-0.95) and spirometry (aOR, 0.90; 95% CI, 0.88-0.92) while patients with a drive time of 91 to 120 minutes had lower odds of receiving cardiac rehabilitation (aOR, 0.80; 95% CI, 0.74-0.87). Results were similar in analyses restricted to urban patients or patients whose primary care clinic was in a tertiary care center., Conclusions and Relevance: In this retrospective cohort study, longer drive time was associated with less frequent receipt of guideline-recommended services across multiple components of care. To improve quality of care and health outcomes, health systems and clinicians should adopt strategies to mitigate travel burden, even for urban patients.

Rationale: Many patients with suspected chronic obstructive pulmonary disease (COPD) do not undergo spirometry to confirm the diagnosis. Underutilization is often attributed to barriers to accessing spirometry., Objective: Our objective wasto identify factors associated with spirometry underutilization for patients who are less likely to face access barriers related to travel, insurance, and availability of spirometry., Methods: A retrospective analysis was conducted of patients enrolled in the Veterans Health Administration and living in urban areas with a new diagnosis of COPD between 2012 to 2015, reducing out-of-pocket cost and travel barriers, respectively. We included only patients whose primary care clinic was located in an academically affiliated tertiary level facility with spirometry available. We used logistic regression to estimate associations between patient characteristics and receipt of spirometry within 2 years before or after COPD diagnosis., Results: Of 24,300 patients, 59.7% had spirometry. Compared to patients <55 years, patients 75-84 years had an adjusted odds ratio (aOR) of undergoing spirometry of 0.80 (95% confidence interval [CI]:0.72-0.90), while patients ≥85 years had an aOR of 0.47 (95%CI: 0.40-0.54). Compared to patients with a Charlson Comorbidity Index (CCI) ≥3, patients with a CCI of 0 had an aOR of 0.60 (95%CI:0.54-0.67). Patients who had not seen a pulmonary specialist had lower odds of receiving spirometry (aOR 0.38 [95%CI:0.35-0.41])., Conclusion: Spirometry underutilization persists among patients who are less likely to have access barriers related to travel, insurance, and availability of spirometry. Spirometry underutilization is associated with older age, not having received pulmonary care, and having fewer comorbidities. COPD care quality initiatives will need to address these factors., (JCOPDF © 2022.)

Objective: Robust disease and syndromic surveillance tools are underdeveloped in the United States, as evidenced by limitations and heterogeneity in sociodemographic data collection throughout the COVID-19 pandemic. To monitor the COVID-19 pandemic in Minnesota, we developed a federated data network in March 2020 using electronic health record (EHR) data from 8 multispecialty health systems., Materials and Methods: In this serial cross-sectional study, we examined patients of all ages who received a COVID-19 polymerase chain reaction test, had symptoms of a viral illness, or received an influenza test from January 3, 2016, through November 7, 2020. We evaluated COVID-19 testing rates among patients with symptoms of viral illness and percentage positivity among all patients tested, in aggregate and by zip code. We stratified results by patient and area-level characteristics., Results: Cumulative COVID-19 positivity rates were similar for people aged 12-64 years (range, 15.1%-17.6%) but lower for adults aged ≥65 years (range, 9.3%-10.7%). We found notable racial and ethnic disparities in positivity rates early in the pandemic, whereas COVID-19 positivity was similarly elevated across most racial and ethnic groups by the end of 2020. Positivity rates remained substantially higher among Hispanic patients compared with other racial and ethnic groups throughout the study period. We found similar trends across area-level income and rurality, with disparities early in the pandemic converging over time., Practice Implications: We rapidly developed a distributed data network across Minnesota to monitor the COVID-19 pandemic. Our findings highlight the utility of using EHR data to monitor the current pandemic as well as future public health priorities. Building partnerships with public health agencies can help ensure data streams are flexible and tailored to meet the changing needs of decision makers.

Background: Opioids and multimodal analgesia are widely administered to manage postoperative pain. However, little is known on how improvements in inpatient pain control are correlated with high-risk (> 90 daily OME) discharge opioid prescriptions for opioid naïve surgical patients., Methods: We conducted a retrospective observational study of adult opioid-naïve patients undergoing surgery from June 2012 through December 2018 at a large academic medical center. We used multivariate logistic regression to assess whether multimodal analgesic drugs consumed in the 24 h prior to discharge was associated with a reduction in high-risk opioid discharge prescriptions. We identified other risk factors for receiving a high-risk discharge opioid prescription., Results: Among the 32,511 patients, 83% of patients were discharged with an opioid prescription. In 2013, 34.1% of patients with a discharge opioid prescription received a high-risk prescription and this declined to 17.7% by 2018. Use of multimodal analgesic agents during the final 24 h of hospitalization increased each year, with over 80% receiving at least one multimodal analgesic agent by 2018. The median OME consumed in the 24 h prior to discharge peaked in 2013 at 31 and steadily decreased to 19.8 by 2018. There was a significant association between the use of acetaminophen in the 24 h prior to discharge and a high-risk prescription at discharge (p < 0.01). OMEs consumed in the 24 h prior to discharge was a significant predictor of receiving a high-risk discharge prescription, even at low doses. Other factors associated with receipt of a high-risk discharge opioid prescription included male gender, race, history of anxiety disorder, and discharge service., Discussion: Use of multimodal analgesia regimens in hospitalized surgical patients in the 24 h prior to hospital discharge increased between 2012 and 2018. Simultaneously, opioid use prior to hospital discharge decreased. Despite these gains, approximately one in five discharge prescriptions was high-risk (> 90 daily OME). In addition, we found that prescribing of discharge opioids above inpatient opioid requirements remains common in opioid naive surgical patients., Conclusion: Providers should account for pre-discharge opioid consumption and use of multimodal analgesia when considering the total and daily OME's that may be appropriate for an individual surgical patient on the discharge opioid prescription., (© 2021. The Author(s).)

Background: Despite past and ongoing efforts to achieve health equity in the USA, racial and ethnic disparities persist and appear to be exacerbated by COVID-19., Objective: Evaluate neighborhood-level deprivation and English language proficiency effect on disproportionate outcomes seen in racial and ethnic minorities diagnosed with COVID-19., Design: Retrospective cohort study SETTING: Health records of 12 Midwest hospitals and 60 clinics in Minnesota between March 4, 2020, and August 19, 2020 PATIENTS: Polymerase chain reaction-positive COVID-19 patients EXPOSURES: Area Deprivation Index (ADI) and primary language MAIN MEASURES: The primary outcome was COVID-19 severity, using hospitalization within 45 days of diagnosis as a marker of severity. Logistic and competing-risk regression models assessed the effects of neighborhood-level deprivation (using the ADI) and primary language. Within race, effects of ADI and primary language were measured using logistic regression., Results: A total of 5577 individuals infected with SARS-CoV-2 were included; 866 (n = 15.5%) were hospitalized within 45 days of diagnosis. Hospitalized patients were older (60.9 vs. 40.4 years, p < 0.001) and more likely to be male (n = 425 [49.1%] vs. 2049 [43.5%], p = 0.002). Of those requiring hospitalization, 43.9% (n = 381), 19.9% (n = 172), 18.6% (n = 161), and 11.8% (n = 102) were White, Black, Asian, and Hispanic, respectively. Independent of ADI, minority race/ethnicity was associated with COVID-19 severity: Hispanic patients (OR 3.8, 95% CI 2.72-5.30), Asians (OR 2.39, 95% CI 1.74-3.29), and Blacks (OR 1.50, 95% CI 1.15-1.94). ADI was not associated with hospitalization. Non-English-speaking (OR 1.91, 95% CI 1.51-2.43) significantly increased odds of hospital admission across and within minority groups., Conclusions: Minority populations have increased odds of severe COVID-19 independent of neighborhood deprivation, a commonly suspected driver of disparate outcomes. Non-English-speaking accounts for differences across and within minority populations. These results support the ongoing need to determine the mechanisms that contribute to disparities during COVID-19 while also highlighting the underappreciated role primary language plays in COVID-19 severity among minority groups., (© 2021. Society of General Internal Medicine.)

To evaluate whether different approaches in note text preparation (known as preprocessing) can impact machine learning model performance in the case of mortality prediction ICU., Design: Clinical note text was used to build machine learning models for adults admitted to the ICU. Preprocessing strategies studied were none (raw text), cleaning text, stemming, term frequency-inverse document frequency vectorization, and creation of n-grams. Model performance was assessed by the area under the receiver operating characteristic curve. Models were trained and internally validated on University of California San Francisco data using 10-fold cross validation. These models were then externally validated on Beth Israel Deaconess Medical Center data., Setting: ICUs at University of California San Francisco and Beth Israel Deaconess Medical Center., Subjects: Ten thousand patients in the University of California San Francisco training and internal testing dataset and 27,058 patients in the external validation dataset, Beth Israel Deaconess Medical Center., Interventions: None., Measurements and Main Results: Mortality rate at Beth Israel Deaconess Medical Center and University of California San Francisco was 10.9% and 7.4%, respectively. Data are presented as area under the receiver operating characteristic curve (95% CI) for models validated at University of California San Francisco and area under the receiver operating characteristic curve for models validated at Beth Israel Deaconess Medical Center. Models built and trained on University of California San Francisco data for the prediction of inhospital mortality improved from the raw note text model (AUROC, 0.84; CI, 0.80-0.89) to the term frequency-inverse document frequency model (AUROC, 0.89; CI, 0.85-0.94). When applying the models developed at University of California San Francisco to Beth Israel Deaconess Medical Center data, there was a similar increase in model performance from raw note text (area under the receiver operating characteristic curve at Beth Israel Deaconess Medical Center: 0.72) to the term frequency-inverse document frequency model (area under the receiver operating characteristic curve at Beth Israel Deaconess Medical Center: 0.83)., Conclusions: Differences in preprocessing strategies for note text impacted model discrimination. Completing a preprocessing pathway including cleaning, stemming, and term frequency-inverse document frequency vectorization resulted in the preprocessing strategy with the greatest improvement in model performance. Further study is needed, with particular emphasis on how to manage author implicit bias present in note text, before natural language processing algorithms are implemented in the clinical setting., Competing Interests: Dr. Butte is a co-founder and consultant to Personalis and NuMedii; he is a consultant to Samsung, Mango Tree Corporation, and in the recent past, 10x Genomics, Helix, Pathway Genomics, and Verinata (Illumina); he has served on paid advisory panels or boards for Geisinger Health, Regenstrief Institute, Gerson Lehrman Group, AlphaSights, Covance, Novartis, Genentech, Merck, and Roche; he is a shareholder in Personalis and NuMedii; he is a minor shareholder in Apple, Facebook, Alphabet (Google), Microsoft, Amazon, Snap, Snowflake, 10x Genomics, Illumina, Nuna Health, Assay Depot (Scientist.com), Vet24seven, Regeneron, Sanofi, Royalty Pharma, Pfizer, BioNTech, AstraZeneca, Moderna, Biogen, Twist Bioscience, Pacific Biosciences, Editas Medicine, Invitae, and Sutro, and several other nonhealth-related companies and mutual funds; and he has received honoraria and travel reimbursement for invited talks from Johnson and Johnson, Roche, Genentech, Pfizer, Merck, Lilly, Takeda, Varian, Mars, Siemens, Optum, Abbott, Celgene, AstraZeneca, AbbVie, Westat, several investment and venture capital firms, and many academic institutions, medical or disease specific foundations and associations, and health systems. Dr. Butte receives royalty payments through Stanford University, for several patents and other disclosures licensed to NuMedii and Personalis. Dr. Butte’s research has been funded by National Institutes of Health (NIH), Northrop Grumman (as the prime on an NIH contract), Genentech, Johnson and Johnson, Food and Drug Administration, Robert Wood Johnson Foundation, Leon Lowenstein Foundation, Intervalien Foundation, Priscilla Chan and Mark Zuckerberg, the Barbara and Gerson Bakar Foundation, and in the recent past, the March of Dimes, Juvenile Diabetes Research Foundation, California Governor’s Office of Planning and Research, California Institute for Regenerative Medicine, L’Oreal, and Progenity. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Purpose: Heterogeneity has been observed in outcomes of hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of clinical phenotypes may facilitate tailored therapy and improve outcomes. The purpose of this study is to identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes., Methods: This is a retrospective analysis of COVID-19 patients from March 7, 2020 to August 25, 2020 at 14 U.S. hospitals. Ensemble clustering was performed on 33 variables collected within 72 hours of admission. Principal component analysis was performed to visualize variable contributions to clustering. Multinomial regression models were fit to compare patient comorbidities across phenotypes. Multivariable models were fit to estimate associations between phenotype and in-hospital complications and clinical outcomes., Results: The database included 1,022 hospitalized patients with COVID-19. Three clinical phenotypes were identified (I, II, III), with 236 [23.1%] patients in phenotype I, 613 [60%] patients in phenotype II, and 173 [16.9%] patients in phenotype III. Patients with respiratory comorbidities were most commonly phenotype III (p = 0.002), while patients with hematologic, renal, and cardiac (all p<0.001) comorbidities were most commonly phenotype I. Adjusted odds of respiratory, renal, hepatic, metabolic (all p<0.001), and hematological (p = 0.02) complications were highest for phenotype I. Phenotypes I and II were associated with 7.30-fold (HR:7.30, 95% CI:(3.11-17.17), p<0.001) and 2.57-fold (HR:2.57, 95% CI:(1.10-6.00), p = 0.03) increases in hazard of death relative to phenotype III., Conclusion: We identified three clinical COVID-19 phenotypes, reflecting patient populations with different comorbidities, complications, and clinical outcomes. Future research is needed to determine the utility of these phenotypes in clinical practice and trial design., Competing Interests: The authors have declared that no competing interests exist.

Importance: High out-of-pocket drug costs can cause patients to skip treatment and worsen outcomes, and high insurer drug payments could increase premiums. Drug wholesale list prices have doubled in recent years. However, because of manufacturer discounts and rebates, the extent to which increases in wholesale list prices are associated with amounts paid by patients and insurers is poorly characterized., Objective: To determine whether increases in wholesale list prices are associated with increases in amounts paid by patients and insurers for branded medications., Design, Setting, and Participants: Cross-sectional retrospective study analyzing pharmacy claims for patients younger than 65 years in the IBM MarketScan Commercial Database and pricing data from SSR Health, LLC, between January 1, 2010, and December 31, 2016. Pharmacy claims analyzed represent claims of employees and dependents participating in employer health benefit programs belonging to large employers. Rebate data were estimated from sales data from publicly traded companies. Analysis focused on the top 5 patent-protected specialty and 9 traditional brand-name medications with the highest total drug expenditures by commercial insurers nationwide in 2014. Data were analyzed from July 2017 to July 2020., Exposures: Calendar year., Main Outcomes and Measures: Changes in inflation-adjusted amounts paid by patients and insurers for branded medications., Results: In this analysis of 14.4 million pharmacy claims made by 1.8 million patients from 2010-2016, median drug wholesale list price increased by 129% (interquartile range [IQR], 78%-133%), while median insurance payments increased by 64% (IQR, 28%-120%) and out-of-pocket costs increased by 53% (IQR, 42%-82%). The mean percentage of wholesale list price accounted for by discounts increased from 17% in 2010 to 21% in 2016, and the mean percentage of wholesale list price accounted for by rebates increased from 22% in 2010 to 24% in 2016. For specialty medications, median patient out-of-pocket costs increased by 85% (IQR, 73%-88%) from 2010 to 2016 after adjustment for inflation and 42% (IQR, 25%-53%) for nonspecialty medications. During that same period, insurer payments increased by 116% for specialty medications (IQR, 100%-127%) and 28% for nonspecialty medications (IQR, 5%-34%)., Conclusions and Relevance: This study's findings suggest that drug list prices more than doubled over a 7-year study period. Despite rising manufacturer discounts and rebates, these price increases were associated with large increases in patient out-of-pocket costs and insurer payments.

Importance: The 2019 federal Ending the HIV Epidemic initiative requires a vast expansion of access to antiretroviral therapy (ART) and preexposure prophylaxis (PrEP) for HIV treatment and prevention. However, high prices for ART and PrEP can reduce their affordability and use. Medicare covers 1 in 4 persons living with HIV, and the Medicare Part D drug benefit imposes complicated cost-sharing between patients and other stakeholders., Objective: To determine how the Medicare Part D design distributes the cost burden for ART and PrEP between patients, insurance plans, manufacturers, and Medicare., Design and Setting: Nationwide cross-sectional analyses of first quarter 2019 Medicare formulary and pricing files for 3326 Part D plans were performed. These files contain drug benefit data, including prices and cost-sharing requirements., Main Outcomes and Measures: For 18 ART and 2 PrEP regimens, the out-of-pocket costs for patients and the cost borne by plans, manufacturers, and Medicare were projected for 1 year of treatment or prevention under a 2019 standard Medicare Part D insurance plan. Analyses assumed that patients used the ART or PrEP regimen and no other medications., Results: In 2019, ART prices ranged from $24 010 to $46 770 annually (median price, $35 780), with patients projected to pay 9% to 14% of the cost ($3270-$4350), insurance plans 18% to 24% ($5340-$8450), manufacturers 6% to 11% ($2370-$2750), and Medicare 53% to 67% ($12 770-$31 270). The price of PrEP was $20 570 annually, with patients contributing 15% ($2990), insurance plans 22% ($4570), manufacturers 13% ($2750), and Medicare 50% ($10 260). For beneficiaries with low-income subsidies that cover all patient cost-sharing, Medicare would assume 67% to 76% of ART costs and 65% of PrEP costs., Conclusions and Relevance: Medicare Part D mandates universal ART and PrEP coverage, but high prices (>$35 000 annually for ART and>$20 000 annually for PrEP) and the design of Part D can jeopardize affordability for patients and place most of the cost burden on taxpayers. Under a standard Medicare Part D benefit, patients pay $3000 to $4000 out-of-pocket yearly, unless they qualify for low-income subsidies, and half to two-thirds of the cost of ART and PrEP is borne by Medicare rather than insurance plans or manufacturers. To end the HIV epidemic by 2030, it appears that policies must address both high drug prices and revamp Medicare Part D cost-sharing.

Opioids are routinely prescribed to manage acute postoperative pain, but changes in postoperative opioid prescribing associated with the marketing of long-acting opioids such as OxyContin have not been described in the surgical cohort., Methods: Using a large commercial claims data set, we studied postoperative opioid prescribing after selected common surgical procedures between 1994 and 2014. For each procedure and year, we calculated the mean postoperative morphine milligram equivalents (MME) filled on the index prescription and assessed the proportion of patients who filled a high-dose prescription (≥350 MME). We reported changes in postoperative opioid prescribing over time and identified predictors of filling a high-dose postoperative opioid prescription., Results: We identified 1,321,264 adult patients undergoing selected common surgical procedures between 1994 and 2014, of whom 80.3% filled a postoperative opioid prescription. One in five surgery patients filled a high-dose postoperative opioid prescription. Between 1994 and 2014, the mean MME filled increased by 145%, 84%, and 85% for lumbar laminectomy/laminotomy, total knee arthroplasty, and total hip arthroplasty, respectively. The procedures most likely to be associated with a high-dose opioid fill were all orthopaedic procedures (AOR 5.20 to 7.55, P < 0.001 for all). Patients whose postoperative opioid prescription included a long-acting formulation had the highest odds of filling a prescription that exceeded 350 MME (AOR 32.01, 95% CI, 30.23-33.90)., Discussion: After the US introduction of long-acting opioids such as OxyContin, postoperative opioid prescribing in commercially insured patients increased in parallel with broader US opioid-prescribing trends, most notably among patients undergoing orthopaedic surgical procedures. The increase in the mean annual MME filled starting in the late 1990s was driven in part by the higher proportion of long-acting opioid formulations on the index postoperative opioid prescription filled by orthopaedic surgery patients., (Copyright © 2020 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Orthopaedic Surgeons.)

Background As patients with congenital heart disease (CHD) are living longer, understanding the comorbidities they develop as they age is increasingly important. However, there are no published population-based estimates of the comorbidity burden among the US adult patients with CHD. Methods and Results Using the IBM MarketScan commercial claims database from 2010 to 2016, we identified adults aged ≥18 years with CHD and 2 full years of continuous enrollment. These were frequency matched with adults without CHD within categories jointly defined by age, sex, and dates of enrollment in the database. A total of 40 127 patients with CHD met the inclusion criteria (mean [SD] age, 36.8 [14.6] years; and 48.2% were women). Adults with CHD were nearly twice as likely to have any comorbidity than those without CHD ( P <0.001). After adjusting for covariates, patients with CHD had a higher prevalence risk ratio for "previously recognized to be common in CHD" (risk ratio, 9.41; 95% CI, 7.99-11.1), "other cardiovascular" (risk ratio, 1.73; 95% CI, 1.66-1.80), and "noncardiovascular" (risk ratio, 1.47; 95% CI, 1.41-1.52) comorbidities. After adjusting for covariates and considering interaction with age, patients with severe CHD had higher risks of previously recognized to be common in CHD and lower risks of other cardiovascular comorbidities than age-stratified patients with nonsevere CHD. For noncardiovascular comorbidities, the risk was higher among patients with severe than nonsevere CHD before, but not after, the age of 40 years. Conclusions Our data underscore the unique clinical needs of adults with CHD compared with their peers. Clinicians caring for CHD may want to use a multidisciplinary approach, including building close collaborations with internists and specialists, to help provide appropriate care for the highly prevalent noncardiovascular comorbidities.

Objective: Electronic health records (EHRs) were expected to yield numerous benefits. However, early studies found mixed evidence of this. We sought to determine whether widespread adoption of modern EHRs in the US has improved clinical care., Methods: We studied hospitals reporting performance measures from 2008-2015 in the Centers for Medicare and Medicaid Services Hospital Compare database that also reported having an EHR in the American Hospital Association 2015 IT supplement. Using interrupted time-series analysis, we examined the association of EHR implementation, EHR vendor, and Meaningful Use status with 11 process measures and 30-day hospital readmission and mortality rates for heart failure, pneumonia, and acute myocardial infarction., Results: A total of 1246 hospitals contributed 8222 hospital-years. Compared to hospitals without EHRs, hospitals with EHRs had significant improvements over time on 5 of 11 process measures. There were no substantial differences in readmission or mortality rates. Hospitals with CPSI EHR systems performed worse on several process and outcome measures. Otherwise, we found no substantial improvements in process measures or condition-specific outcomes by duration of EHR use, EHR vendor, or a hospital's Meaningful Use Stage 1 or Stage 2 status., Conclusion: In this national study of hospitals with modern EHRs, EHR use was associated with better process of care measure performance but did not improve condition-specific readmission or mortality rates regardless of duration of EHR use, vendor choice, or Meaningful Use status. Further research is required to understand why EHRs have yet to improve standard outcome measures and how to better realize the potential benefits of EHR systems., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Importance: Accurate prediction of outcomes among patients in intensive care units (ICUs) is important for clinical research and monitoring care quality. Most existing prediction models do not take full advantage of the electronic health record, using only the single worst value of laboratory tests and vital signs and largely ignoring information present in free-text notes. Whether capturing more of the available data and applying machine learning and natural language processing (NLP) can improve and automate the prediction of outcomes among patients in the ICU remains unknown., Objectives: To evaluate the change in power for a mortality prediction model among patients in the ICU achieved by incorporating measures of clinical trajectory together with NLP of clinical text and to assess the generalizability of this approach., Design, Setting, and Participants: This retrospective cohort study included 101 196 patients with a first-time admission to the ICU and a length of stay of at least 4 hours. Twenty ICUs at 2 academic medical centers (University of California, San Francisco [UCSF], and Beth Israel Deaconess Medical Center [BIDMC], Boston, Massachusetts) and 1 community hospital (Mills-Peninsula Medical Center [MPMC], Burlingame, California) contributed data from January 1, 2001, through June 1, 2017. Data were analyzed from July 1, 2017, through August 1, 2018., Main Outcomes and Measures: In-hospital mortality and model discrimination as assessed by the area under the receiver operating characteristic curve (AUC) and model calibration as assessed by the modified Hosmer-Lemeshow statistic., Results: Among 101 196 patients included in the analysis, 51.3% (n = 51 899) were male, with a mean (SD) age of 61.3 (17.1) years; their in-hospital mortality rate was 10.4% (n = 10 505). A baseline model using only the highest and lowest observed values for each laboratory test result or vital sign achieved a cross-validated AUC of 0.831 (95% CI, 0.830-0.832). In contrast, that model augmented with measures of clinical trajectory achieved an AUC of 0.899 (95% CI, 0.896-0.902; P < .001 for AUC difference). Further augmenting this model with NLP-derived terms associated with mortality further increased the AUC to 0.922 (95% CI, 0.916-0.924; P < .001). These NLP-derived terms were associated with improved model performance even when applied across sites (AUC difference for UCSF: 0.077 to 0.021; AUC difference for MPMC: 0.071 to 0.051; AUC difference for BIDMC: 0.035 to 0.043; P < .001) when augmenting with NLP at each site., Conclusions and Relevance: Intensive care unit mortality prediction models incorporating measures of clinical trajectory and NLP-derived terms yielded excellent predictive performance and generalized well in this sample of hospitals. The role of these automated algorithms, particularly those using unstructured data from notes and other sources, in clinical research and quality improvement seems to merit additional investigation.

Risk adjustment models for intensive care outcomes have yet to realize the full potential of data unlocked by the increasing adoption of EHRs. In particular, they fail to fully leverage the information present in longitudinal, structured clinical data - including laboratory test results and vital signs - nor can they infer patient state from unstructured clinical narratives without lengthy manual abstraction. A fully electronic ICU risk model fusing these two types of data sources may yield improved accuracy and more personalized risk estimates, and in obviating manual abstraction, could also be used for real-time decision-making. As a first step towards fully "electronic" ICU models based on fused data, we present results of generalized additive modeling applied to a sample of over 36,000 ICU patients. Our approach outperforms those based on the SAPS and OASIS systems (A UC: 0.908 vs. 0.794 and 0.874), and appears to yield more granular and easily visualized risk estimates.

Hepatitis B virus (HBV) reactivation in the setting of rituximab use is a potentially fatal but preventable safety event. The rate of HBV screening and proportion of patients at risk who receive antiviral prophylaxis in patients initiating rituximab is unknown.We analyzed electronic health record (EHR) data from 2 health systems, a university center and a safety net health system, including diagnosis grouper codes, problem lists, medications, laboratory results, procedures codes, clinical encounter notes, and scanned documents. We identified all patients who received rituximab between 6/1/2012 and 1/1/2016. We calculated the proportion of rituximab users with inadequate screening for HBV according to the Centers for Disease Control guidelines for detecting latent HBV infection before their first rituximab infusion during the study period. We also assessed the proportion of patients with positive hepatitis B screening tests who were prescribed antiviral prophylaxis. Finally, we characterized safety failures and adverse events.We included 926 patients from the university and 132 patients from the safety net health system. Sixty-one percent of patients from the university had adequate screening for HBV compared with 90% from the safety net. Among patients at risk for reactivation based on results of HBV testing, 66% and 92% received antiviral prophylaxis at the university and safety net, respectively.We found wide variations in hepatitis B screening practices among patients receiving rituximab, resulting in unnecessary risks to patients. Interventions should be developed to improve patient safety procedures in this high-risk patient population.

Background: Providers wish to help patients with prescription costs but often lack drug cost information. We examined whether giving providers formulary and drug cost information was associated with changes in their diabetes patients' drug costs and use. We conducted a longitudinal non-randomized evaluation of the web-based Prescribing Guide ( www.PrescribingGuide.com ), a free resource available to Hawaii's providers since 2006, which summarizes the formularies and copayments of six health plans for drugs to treat 16 common health conditions. All adult primary care physicians in Hawaii were offered the Prescribing Guide, and providers who enrolled received a link to the website and regular hardcopy updates., Methods: We analyzed prescription claims from a large health plan in Hawaii for 5,883 members with diabetes from 2007 (baseline) to 2009 (follow-up). Patients were linked to 299 "main prescribing" providers, who on average, accounted for >88 % of patients' prescriptions and drug costs. We compared changes in drug costs and use for "study" patients whose main provider enrolled to receive the Prescribing Guide, versus "control" patients whose main provider did not enroll to receive the Prescribing Guide., Results: In multivariate analyses controlling for provider specialty and clustering of patients by providers, both patient groups experienced similar increases in number of prescriptions (+3.2 vs. +2.7 increase, p = 0.24), and days supply of medications (+141 vs. +129 increase, p = 0.40) averaged across all drugs. Total and out-of-pocket drug costs also increased for both control and study patients. However, control patients showed higher increases in yearly total drug costs of $208 per patient (+$792 vs. +$584 increase, p = 0.02) and in 30-day supply costs (+$9.40 vs. +$6.08 increase, p = 0.03). Both groups experienced similar changes in yearly out-of-pocket costs (+$41 vs + $31 increase, p = 0.36) and per 30-day supply (-$0.23 vs. -$0.19 decrease, p = 0.996)., Conclusion: Giving formulary and drug cost information to providers was associated with lower increases in total drug costs but not with lower out-of-pocket costs or greater medication use. Insurers and health information technology businesses should continue to increase providers' access to formulary and drug cost information at the point of care.

Objectives: Narrative review of the impact of pay-for-performance (P4P) and public reporting (PR) on health care outcomes, including spillover effects and impact on disparities., Principal Findings: The impact of P4P and PR is dependent on the underlying payment system (fee-for-service, salary, capitation) into which these schemes are introduced. Both have the potential to improve care, but they can also have substantial unintended consequences. Evidence from the behavioral economics literature suggests that individual physicians will vary in how they respond to incentives. We also discuss issues to be considered when including patient-reported outcome measures (PROMs) or patient-reported experience measures into P4P and PR schemes., Conclusion: We provide guidance to payers and policy makers on the design of P4P and PR programs so as to maximize their benefits and minimize their unintended consequences. These include involving clinicians in the design of the program, taking into account the payment system into which new incentives are introduced, designing the structure of reward programs to maximize the likelihood of intended outcomes and minimize the likelihood of unintended consequences, designing schemes that minimize the risk of increasing disparities, providing stability of incentives over some years, and including outcomes that are relevant to patients' priorities. In addition, because of the limitations of PR and P4P as effective interventions in their own right, it is important that they are combined with other policies and interventions intended to improve quality to maximize their likely impact., (© Health Research and Educational Trust.)

Background and Objectives: Anemia guidelines for CKD recommend withholding intravenous iron in the setting of active infection, although no data specifically support this recommendation. This study aimed to examine the association between intravenous iron and clinical outcomes among hemodialysis patients hospitalized for infection., Design, Setting, Participants, & Measurements: This was a retrospective observational cohort study using data from the US Renal Data System of 22,820 adult Medicare beneficiaries on in-center hemodialysis who had received intravenous iron in the 14 days preceding their first hospitalization for bacterial infection in 2010. In multivariable analyses, the association between receipt of intravenous iron at any point from the day of hospital admission to discharge and all-cause 30-day mortality, mortality in 2010, length of hospital stay, and readmission for infection or death within 30 days of discharge was evaluated., Results: There were 2463 patients (10.8%) who received intravenous iron at any point from the day of admission to discharge. Receipt of intravenous iron was not associated with age, dialysis vintage, or comorbidities. There were 2618 deaths within 30 days of admission and 6921 deaths in 2010 (median follow-up 173 days; 25th and 75th percentiles, 78-271 days). The median length of stay was 7 days (25th and 75th percentiles, 5-12 days). Receipt of intravenous iron was not associated with higher 30-day mortality (odds ratio, 0.86; 95% confidence interval [95% CI], 0.74 to 1.00), higher mortality in 2010 (hazard ratio, 0.92; 95% CI, 0.85 to 1.00), longer mean length of stay (10.1 days [95% CI, 9.7 to 10.5] versus 10.5 days [95% CI, 10.3 to 10.7]; P=0.05), or readmission for infection or death within 30 days of discharge (odds ratio, 1.08; 95% CI, 0.96 to 1.22) compared with no receipt of intravenous iron., Conclusions: This analysis does not support withholding intravenous iron upon admission for bacterial infection in hemodialysis patients, although clinical trials are required to make definitive recommendations., (Copyright © 2015 by the American Society of Nephrology.)

Objective: To recommend methods for assessing quality of care via patient-reported outcome-based performance measures (PRO-PMs) of symptoms, functional status, and quality of life., Methods: A Technical Expert Panel was assembled by the American Medical Association-convened Physician Consortium for Performance Improvement. An environmental scan and structured literature review were conducted to identify quality programs that integrate PRO-PMs. Key methodological considerations in the design, implementation, and analysis of these PRO-PM data were systematically identified. Recommended methods for addressing each identified consideration were developed on the basis of published patient-reported outcome (PRO) standards and refined through public comment. Literature review focused on programs using PROs to assess performance and on PRO guidance documents., Results: Thirteen PRO programs and 10 guidance documents were identified. Nine best practices were developed, including the following: provide a rationale for measuring the outcome and for using a PRO-PM; describe the context of use; select a measure that is meaningful to patients with adequate psychometric properties; provide evidence of the measure's sensitivity to differences in care; address missing data and risk adjustment; and provide a framework for implementation, interpretation, dissemination, and continuous refinement., Conclusion: Methods for integrating PROs into performance measurement are available., (Copyright © 2015 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.)