The height of the box represents the interquartile range (IQR), the horizontal line in the box interior represents the median, the whiskers represent the 1.5 IQR of the 25th quartile or the 1.5 IQR of the 75th quartile, and the circles, plus signs, Xs, triangles, squares, and diamonds represent outliers. Each point represents an individual hospital. AMI indicates acute myocardial infarction; HF, heart failure.
AMI indicates acute myocardial infarction; HF, heart failure; and the numbers in parentheses represent the range.
aAdjusted for hospital characteristics.
bUnadjusted for hospital characteristics.
eAppendix 1. CMS Risk-Standardized Payment Method for Profiling Hospitals
eAppendix 2. Patient and Hospital Characteristics
eAppendix 3. CMS Risk-Standardized Outcome Method for Profiling Hospitals
eTable 1. Sources of Expenditures Included in the CMS 30-Day All-Cause Risk-Standardized Data
eTable 2. List of the 21 Adverse Event Measures in the Medicare Patient Safety Monitoring System
eTable 3. Estimates From the Regression Analysis
eFigure 1. Distributions of Medicare 30-Day Episode-of-Care Expenditures by AMI, HF, and Pneumonia
eFigure 2. Relationship of Risk-Standardized Medicare 30-Day Episode-of-Care Expenditures by AMI, HF, and Pneumonia
eFigure 3. Relationship of Risk-Standardized Rate of Adverse Events Among AMI, HF, and Pneumonia
eFigure 4. Relationship Between Hospital-Specific Risk-Standardized Medicare 30-Day Episode-of-Care Expenditures and Adverse Events by Condition
eFigure 5. Association Between Hospital-Specific Performance on Patient Safety and Hospital-Specific Performance on 30-Day Episode-of-Care Expenditures by AMI, HF, and Pneumonia (Hospitals With at Least 25 Adverse Events for Which Patients Were at Risk for Each Condition)
eFigure 6. Hospital Characteristics Associated With High-Value Care
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Wang Y, Eldridge N, Metersky ML, et al. Association Between Medicare Expenditures and Adverse Events for Patients With Acute Myocardial Infarction, Heart Failure, or Pneumonia in the United States. JAMA Netw Open. 2020;3(4):e202142. doi:10.1001/jamanetworkopen.2020.2142
What is the association between 30-day episode-of-care expenditures and in-hospital adverse events?
This cross-sectional study of 44 807 patients, which linked the 2011 to 2016 hospital-specific risk-standardized 30-day episode-of-care expenditure data from the Centers for Medicare & Medicaid Services and medical record–abstracted in-hospital adverse event data from the Medicare Patient Safety Monitoring System, found that hospitals with high adverse event rates were more likely to have high 30-day episode-of-care Medicare expenditures for patients discharged with acute myocardial infarction, heart failure, or pneumonia.
This study suggests that hospitals with higher adverse event rates are more likely to have higher costs for acute myocardial infarction, heart failure, or pneumonia.
Studies have shown that adverse events are associated with increasing inpatient care expenditures, but contemporary data on the association between expenditures and adverse events beyond inpatient care are limited.
To evaluate whether hospital-specific adverse event rates are associated with hospital-specific risk-standardized 30-day episode-of-care Medicare expenditures for fee-for-service patients discharged with acute myocardial infarction (AMI), heart failure (HF), or pneumonia.
Design, Setting, and Participants
This cross-sectional study used the 2011 to 2016 hospital-specific risk-standardized 30-day episode-of-care expenditure data from the Centers for Medicare & Medicaid Services and medical record–abstracted in-hospital adverse event data from the Medicare Patient Safety Monitoring System. The setting was acute care hospitals treating at least 25 Medicare fee-for-service patients for AMI, HF, or pneumonia in the United States. Participants were Medicare fee-for-service patients 65 years or older hospitalized for AMI, HF, or pneumonia included in the Medicare Patient Safety Monitoring System in 2011 to 2016. The dates of analysis were July 16, 2017, to May 21, 2018.
Main Outcomes and Measures
Hospitals’ risk-standardized 30-day episode-of-care expenditures and the rate of occurrence of adverse events for which patients were at risk.
The final study sample from 2194 unique hospitals included 44 807 patients (26.1% AMI, 35.6% HF, and 38.3% pneumonia) with a mean (SD) age of 79.4 (8.6) years, and 52.0% were women. The patients represented 84 766 exposures for AMI, 96 917 exposures for HF, and 109 641 exposures for pneumonia. Patient characteristics varied by condition but not by expenditure category. The mean (SD) risk-standardized expenditures were $22 985 ($1579) for AMI, $16 020 ($1416) for HF, and $16 355 ($1995) for pneumonia per hospitalization. The mean risk-standardized rates of occurrence of adverse events for which patients were at risk were 3.5% (95% CI, 3.4%-3.6%) for AMI, 2.5% (95% CI, 2.5%-2.5%) for HF, and 3.0% (95% CI, 2.9%-3.0%) for pneumonia. An increase by 1 percentage point in the rate of occurrence of adverse events was associated with an increase in risk-standardized expenditures of $103 (95% CI, $57-$150) for AMI, $100 (95% CI, $29-$172) for HF, and $152 (95% CI, $73-$232) for pneumonia per discharge.
Conclusions and Relevance
Hospitals with high adverse event rates were more likely to have high 30-day episode-of-care Medicare expenditures for patients discharged with AMI, HF, or pneumonia.
The US health care system is moving toward high-value care, with the goal of producing the best health outcomes at the lowest cost.1,2 Reducing both expenditures and hospital-acquired adverse events are 2 important aspects of this goal3,4 because health care expenditures are projected to increase faster than the US gross domestic product over the 2015 to 2025 period.5 Studies6-16 show that adverse events are associated with prolonged length of hospital stay, high mortality, unplanned readmissions, and deteriorating health status and quality of life of patients, all of which are associated with increased expenditures. However, few empirical studies have linked adverse events and expenditures across a large number of institutions.
A conceptual association between adverse events and expenditures could be that patients who have in-hospital adverse events may require additional expenditures to treat these adverse events. Such additional expenditures may also occur after discharge. Nevertheless, restricted by available data, previous studies were limited by the use of only a small number of measures17,18 and were largely focused on inpatient cost.9,11,19-27 Information is needed to examine the association between hospital performance on adverse events and hospital performance on episode-of-care expenditures within a standard period after admission in a contemporary and national cohort.
Accordingly, we sought to investigate the association at the hospital level between in-hospital adverse events and 30-day episode-of-care Medicare expenditures for Medicare fee-for-service patients with acute myocardial infarction (AMI), heart failure (HF), or pneumonia, 3 common conditions among older adults. The study used 2 unique national data sets, the hospital-specific Medicare 30-day episode-of-care expenditure data from the Centers for Medicare & Medicaid Services (CMS) and the adverse event data from the Medicare Patient Safety Monitoring System (MPSMS) to conduct this analysis. The 30-day Medicare episode-of-care expenditure data include all-source Medicare payments directly associated with care for individual services. It was the first database of its kind to be made available, and the MPSMS data represent the nation’s largest randomly selected hospital medical record–abstracted adverse event database. The setting was acute care hospitals treating at least 25 Medicare fee-for-service patients for AMI, HF, or pneumonia in the United States. Participants were Medicare fee-for-service patients 65 years or older hospitalized for AMI, HF, or pneumonia included in the MPSMS in 2011 to 2016. The dates of analysis were July 16, 2017, to May 21, 2018. In addition, we identified the best-performing hospitals in both expenditures and adverse events to represent high-value health care hospitals and assessed their characteristics.1,28
The institutional review board at Solutions IRB29 deemed that the requirement for informed consent could be waived for this cross-sectional study. The institutional review board at Solutions IRB reviewed the study protocol and granted a waiver of informed consent for the use of the deidentified database. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for observational studies.30
The Medicare expenditure data for AMI, HF, and pneumonia are available at the individual hospital level from the Hospital Compare website.31 The data include hospital-specific risk-standardized Medicare expenditures for episodes of care, starting with inpatient admission to a short-term acute care facility and extending 30 days after admission for Medicare fee-for-service patients (eAppendix 1 and eTable 1 in the Supplement). The geographic differences and policy adjustments in payment rates were removed. The CMS pooled expenditure data from a 3-year period to ensure that each hospital had sufficient discharges (ie, cases). Reporting periods used were from July 1, 2011, through June 30, 2014, from July 1, 2012, through June 30, 2015, and from July 1, 2013, through June 30, 2016. To include the maximum number of hospitals, we combined three 3-year-period data sets into a single multiple-year data set from July 1, 2011, to June 30, 2016. If a hospital was in multiple periods, we averaged its expenditure weighted by its average number of discharges in each period.
The MPSMS data, described elsewhere,8,13,15,32-38 are available at the individual patient level. The data include patient demographic, clinical, and comorbidity information and 21 in-hospital adverse event measures (eTable 2 in the Supplement) jointly developed by federal agencies and private health care organizations.39,40 Approximately 34 000 records were selected randomly from 1400 hospitals in 2011, 27 200 records from 1110 hospitals in 2012, 17 900 records from 730 hospitals in 2013, 25 300 records from 836 hospitals in 2014, 29 300 records from 1626 hospitals in 2015, and 29 800 records from 1190 hospitals in 2016. Hospitals were randomly selected and contributed approximately equal numbers of randomly selected medical records to the MPSMS. Medical record abstraction was conducted at the CMS Clinical Data Abstraction Center. Based on 80-monthly reabstractions, the agreement between abstraction and reabstraction ranged from 94% to 99% for data elements used to identify adverse events. To align the CMS and MPSMS data, we restricted the final cohort to Medicare patients discharged with AMI, HF, or pneumonia from a short-term acute care hospital in the United States from July 1, 2011, through June 30, 2016.
Patient characteristics for the MPSMS data were obtained from medical records, and hospital characteristics were obtained from the American Hospital Association’s 2015 Annual Survey Database (eAppendix 2 in the Supplement). An Elixhauser Comorbidity Index score was calculated for each patient in the MPSMS sample. The score ranged from 0 to 29, with a score of 0 indicating no major comorbidities and a a score of 29 indicating the highest number of comorbidities. We then aggregated the score at the hospital level to represent hospital-specific patient complexity. An additional variable included was a fully electronic health record (yes or no) as assessed by the MPSMS data to reflect a hospital’s adoption of such a system.35
The primary outcome was hospital-specific risk-standardized 30-day episode-of-care Medicare expenditures, which combine Medicare payments directly associated with care for patients during their initial hospitalization and Medicare payments directly associated with continued care after discharge but within 30 days after admission from the initial hospitalization (eTable 1 in the Supplement). The CMS measures the initial hospitalization expenditures from the date of admission and post–acute care expenditures from the date of discharge for patients who were discharged alive. We used the hospital-specific risk-standardized rate of occurrence of adverse events as a proxy to measure the hospital performance on adverse events. Specifically, using the CMS risk-standardized method for profiling hospitals (eAppendix 3 in the Supplement), we fitted a mixed model with a Poisson link function to model the number of adverse events as a function of patients’ age, sex, and comorbidities. The number of exposures for which patients were at risk was the offset in the model. Using this model, a hospital-specific risk-standardized rate of occurrence of adverse events was estimated for each hospital. We then linked the risk-standardized adverse event measurement with the CMS hospital-specific risk-standardized expenditure data at the hospital level.
Our second outcome was high-value hospitals, defined as hospitals with both risk-standardized expenditures and the risk-standardized rate of occurrence of adverse events in the lowest quartile (<25th percentile). Because the classification of high-value care varies by condition, the range of this outcome is from 0 to 3, corresponding to none, 1, 2, and all 3 conditions in high-value care.
Each hospital was classified into 1 of the following 3 mutually exclusive categories based on its risk-standardized 30-day expenditures: (1) low if the expenditures were in the lowest quartile (<25th percentile), (2) high if the expenditures were in the highest quartile (>75th percentile), and (3) average if otherwise.41,42 We then performed a descriptive analysis to show patient and hospital characteristics and adverse event measurement across the 3 categories.
To evaluate the association between expenditures and adverse events at the hospital level, we fitted a linear regression model to link hospital-specific risk-standardized expenditures to the hospital-specific risk-standardized rate of occurrence of adverse events, with and without adjustment for hospital characteristics, including the hospital-specific Elixhauser Comorbidity Index score. The model was fitted for AMI, HF, and pneumonia separately.
To address potential uncertainty in the estimates of expenditures and adverse events, we conducted bootstrapping analyses. Because the expenditure data were only available at the hospital level, parametric bootstrapping was used to generate 2000 random data points based on the hospital-specific point and interval estimates in the expenditure data. The inverse of variance of these bootstrapped data points was used to weight by their precision in the regression analyses described above. Because the MPSMS data were at the individual patient level, we used nonparametric bootstrapping with replacement to generate 2000 random data sets using the method developed for the CMS outcome measurements.43 For each sub–data set and each hospital, we then calculated the risk-standardized rate of occurrence of adverse events described previously and fitted the above regressions to obtain a distribution and 95% CI for the estimate of the association between a hospital’s expenditures and the adverse event measure. To align with the CMS method for outcome measurements that restricts the analysis to hospitals with at least 25 discharges in the expenditure data, we conducted additional analyses by restricting the sample to hospitals with at least 25 adverse events for which patients were at risk over the study period.
Finally, we fitted a negative binomial regression model to assess hospital characteristics associated with high-value hospitals weighted by the hospital-specific number of exposures for which patients were at risk. Analyses were conducted using SAS, version 9.4, 64-bit (SAS Institute Inc).
The final study sample based on linked CMS and MPSMS data across 2194 unique hospitals included 44 807 patients (26.1% with AMI, 35.6% with HF, and 38.3% with pneumonia), with a mean (SD) age of 79.4 (8.6) years, and 52.0% were women. The patients represented 84 766 exposures for AMI, 96 917 exposures for HF, and 109 641 exposures for pneumonia. Patient characteristics varied by condition but not by expenditure category. The mean (SD) ages were 78.2 (8.7) years for AMI, 80.2 (8.5) years for HF, and 79.1 (8.6) years for pneumonia, and women accounted for 47.0%, 54.7%, and 52.3% for each condition, respectively (Table 1). Hospitals that had high proportions of patients with coronary artery disease, kidney disease, and diabetes and hospitals that performed coronary artery bypass graft surgery had higher hospital-specific risk-standardized expenditures for all 3 conditions (Table 1).
The mean (SD) risk-standardized expenditures were $22 985 ($1579) for AMI, $16 020 ($1416) for HF, and $16 355 ($1995) for pneumonia per hospitalization (eFigure 1 in the Supplement). Hospitals with high expenditures for 1 condition were also likely to have high expenditures for other conditions (eFigure 2 in the Supplement). The hospital-specific median numbers of adverse events were 40 (interquartile range [IQR], 19-66) for AMI, 39 (IQR, 19-65) for HF, and 47 (IQR, 20-75) for pneumonia. Each patient had a mean of 7.1 (range, 3-19) exposures for AMI, 6.1 (range, 3-17) exposures for HF, and 6.4 (range, 3-17) exposures for pneumonia.
The mean risk-standardized rates of occurrence of adverse events for which patients were at risk were 3.5% (95% CI, 3.4%-3.6%) for AMI, 2.5% (95% CI, 2.5%-2.5%) for HF, and 3.0% (95% CI, 2.9%-3.0%) for pneumonia and varied by expenditure group (Figure 1). Hospitals with a high number of adverse events in 1 condition were likely to have a high number of adverse events in other conditions except for AMI vs pneumonia (eFigure 3 in the Supplement).
The risk-standardized rate of occurrence of adverse events was associated with the risk-standardized expenditures for all 3 conditions (eFigure 4 in the Supplement), with or without adjustment for hospital characteristics (Figure 2). An increase by 1 percentage point in the rate of occurrence of adverse events was associated with an increase in risk-standardized expenditures of $103 (95% CI, $57-$150) for AMI, $100 (95% CI, $29-$172) for HF, and $152 (95% CI, $73-$232) for pneumonia per discharge for the specified condition (Figure 2 and eTable 3 in the Supplement).
The additional analyses, which restricted the sample to hospitals with at least 25 adverse events, showed an even stronger association for AMI and HF. An increase by 1 percentage point in the rate of occurrence of adverse events was associated with an increase in risk-standardized expenditures of $114 (95% CI, $63-$166) for AMI and $116 (95% CI, $39-$193) for HF per discharge. This association was reduced for pneumonia ($132; 95% CI, $49-$216) (eFigure 5 in the Supplement).
The numbers of hospitals classified as providing high-value care were 73 of 1647 (4.4%) for AMI, 189 of 2029 (9.3%) for HF, and 71 of 2060 (3.4%) for pneumonia, and they treated 5.6% of patients with AMI, 6.3% of patients with HF, and 5.9% of patients with pneumonia. Together, they represented 291 of 2194 unique hospitals (13.3%), of which 2 (0.7%) delivered high-value care for all 3 conditions, 38 (13.1%) for 2 conditions, and 251 (86.3%) for 1 condition. High-value hospital characteristics varied by condition (Table 2). Hospitals with case management, safety-net hospitals, and hospitals with a fully electronic health record were more likely to be classified as delivering high-value care (eFigure 6 in the Supplement).
This study used the hospital-specific risk-standardized rate of occurrence of adverse events as a proxy measurement of the hospital performance on adverse events. We found that the hospital performance on adverse events was associated with hospital-specific risk-standardized 30-day episode-of-care expenditures for patients with AMI, HF, or pneumonia. This finding suggests that investment in reducing adverse events may provide substantial savings in Medicare cost. Although the rationale to reduce adverse events goes far beyond economics, we believe that empirical data from across the country demonstrated the alignment between adverse events and cost.
There are several possible explanations for our findings. Patients who developed in-hospital adverse events probably required more care or were at increased risk of mortality7 and were more likely to be readmitted,15 at least for AMI. The Office of Inspector General found that two-thirds of Medicare hospital costs associated with adverse events were the result of additional hospital stays necessitated within the same calendar month as the index hospitalization because of harm from the adverse event.16 Complications resulting from in-hospital adverse events may also cause additional adverse events after discharge, rendering these patients more likely to receive post–acute care services in skilled nursing facilities, home health care, and outpatient visits, as well as unplanned readmissions; consequently, such patients have higher 30-day episode-of-care risk-standardized expenditures compared with patients who do not develop an adverse event during their hospitalization.16,44,45 It is also possible that these patients were provided post–acute care services with higher rates of ambulatory care and follow-up with a condition-specific specialist after discharge, which are associated with additional expenditures.46
The present study based on medical record–abstracted adverse event information was a large population-based investigation to assess the association between hospital performance on adverse events and 30-day expenditures for an episode of care for AMI, HF, or pneumonia in a contemporary cohort of Medicare beneficiaries in the United States. The use of risk-standardized 30-day payment data allowed us to capture costs not only during an index hospitalization but also immediately after discharge, a period in which substantial variation in Medicare expenditures exists predominantly because of differential use of post–acute care services.26 Previous studies16,23,47-51 were restricted to in-hospital cost, but this study extends the cost from in-hospital to a 30-day standard period. For example, Zhan et al48 found that Medicare paid an extra $300 million in 2002 for 5 types of adverse events (pressure ulcer, iatrogenic pneumothorax, postoperative hematoma or hemorrhage, postoperative pulmonary embolism or deep vein thrombosis, and postoperative sepsis). Spector et al49 found that the occurrence of a hospital-acquired pressure ulcer was associated with an estimated $792 million in additional hospital costs that were incurred nationwide. Tsai et al50 found that patients who had major surgery at high-quality hospitals cost Medicare less than patients who had major surgery at low-quality institutions. Shamliyan and Kane51 found that hospitalizations associated with drug poisoning comprised 0.8% of all Medicare hospitalizations, with an annual hospital cost of $4 billion in 2008; in-hospital adverse drug events occurred during 5.3% of all Medicare hospitalizations. However, none of these studies captured expenditures for both inpatient and post–acute care services for AMI, HF, or pneumonia.
Reductions in adverse events often require investment in additional resources, which could increase a hospital’s overall budget and operating costs in the short term. However, from a long-term perspective, such an investment may reduce both Medicare expenditures and hospital costs, in addition to the primary objective of delivering safer care. The Office of Inspector General found that 84% of adverse events did not add to the Medicare payment for an inpatient stay.16 The reason is because these claims did not include diagnosis or procedure codes associated with the adverse events. Even if the claims included codes associated with the events, the codes often had no association with payments because the claims included other costly diagnoses or procedure codes that elevated the reimbursement to equivalent or higher amounts. Nevertheless, hospitals often must absorb the cost for these events. Researchers in Canada found savings of $9.1 million after implementing an infection prevention and control system that cost $6.7 million.52 Pettker et al53 reviewed liability claims at a single tertiary care teaching hospital for two 5-year periods (1998-2002 and 2003-2007) before and after implementing a safety program. They found that both liability claims (30 vs 14) and expenditures ($50.7 million vs $2.9 million) declined with the program.
This study has limitations. We focused on adverse events that occurred during the index hospitalization and not after discharge; therefore, some events may have been missed. However, Forster et al54,55 showed that adverse events frequently occur during the index hospitalization and adverse events that occur after hospital discharge are typically drug related. Restricted by the MPSMS data, we were unable to assess whether some of the measured adverse events have stronger associations with Medicare expenditures than others. It is possible that a proportion of the adverse events detected in the MPSMS may not be preventable, although each of the 21 in-hospital adverse event measures is characterized as being frequently preventable with the delivery of high-quality care. The study may also have underestimated the association between expenditures and adverse events because it is possible that some of the 21 adverse events may require care beyond a 30-day period. Limited by available expenditure data, we were unable to assess the expenditures from direct treatment of adverse events, and it is plausible that some expenditures may be associated with unmeasured confounding factors and that these expenditures may not be attributable to differences in adverse events. In addition, poor hospital performance on adverse events could be a marker of other systemic contributors and mechanisms, such as lower staffing ratios associated with care inefficiency and longer length of stay. Although the scope of this study constrained our ability to address these limitations in depth, future studies are warranted to elucidate them. Nevertheless, this study distinguishes itself by the breadth and standardization of events measured and its national scope.
This study suggests that hospitals with poor performance on adverse events are likely to have high 30-day expenditures for AMI, HF, and pneumonia. These findings strengthen the evidence that adverse events may reflect the quality of hospital care and their reduction may be used as a mechanism for decreasing Medicare expenditures.
Accepted for Publication: February 9, 2020.
Published: April 7, 2020. doi:10.1001/jamanetworkopen.2020.2142
Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Wang Y et al. JAMA Network Open.
Corresponding Author: Yun Wang, PhD, Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Ave, SPH-2 Room 437F, Boston, MA 02115 (email@example.com).
Author Contributions: Dr Wang had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: Wang, Eldridge, Eckenrode, Galusha, Hunt, Bernheim, Krumholz.
Acquisition, analysis, or interpretation of data: Wang, Eldridge, Metersky, Sonnenfeld, Rodrick, Fine, Eckenrode, Galusha, Tasimi, Normand.
Drafting of the manuscript: Wang, Eckenrode, Galusha.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Wang, Galusha, Normand.
Obtained funding: Eldridge, Hunt.
Administrative, technical, or material support: Eldridge, Metersky, Sonnenfeld, Rodrick, Fine, Eckenrode, Galusha, Tasimi, Hunt.
Supervision: Wang, Eldridge.
Conflict of Interest Disclosures: Dr Metersky reported working on various quality improvement and patient safety projects with Qualidigm, the Centers for Medicare & Medicaid Services (CMS), and the Agency for Healthcare Research and Quality (AHRQ) (his employer received remuneration for all). Dr Normand reported having a US patent pending (201810345624.5). Drs Bernheim, Normand, and Krumholz reported working under contract with the CMS to develop and maintain performance measures. Dr Krumholz reported working under contract with the CMS to support quality measurement programs; receiving a research grant through Yale University from Medtronic and the US Food and Drug Administration to develop methods for postmarket surveillance of medical devices; receiving a research grant from Johnson & Johnson through Yale University to support clinical trial data sharing; receiving a research agreement through Yale University from the Shenzhen Center for Health Information for work to advance intelligent disease prevention and health promotion; collaborating with the National Center for Cardiovascular Diseases in Beijing, China; receiving payment from the Arnold & Porter Law Firm for work related to the Sanofi clopidogrel litigation, from the Martin Baughman Law Firm for work related to the Cook Celect inferior vena cava filter litigation, and from the Siegfried and Jensen Law Firm for work related to Vioxx litigation; being chair of a cardiac scientific advisory board for UnitedHealth; being a member of the IBM Watson Health life sciences board; being a member of the advisory board for Element Science, the advisory board for Facebook, and the physician advisory board for Aetna; and being cofounder of Hugo Health, a personal health information platform, and cofounder of Refactor Health, an enterprise health care artificial intelligence–augmented data management company. No other disclosures were reported.
Funding/Support: This work was supported by contract HHSA290201200003C from the AHRQ (Qualidigm was the contractor). Dr Normand is partially supported by grant R01 GM111339 from the National Institutes of Health.
Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Disclaimer: The content of the publication does not necessarily reflect the views or policies of the US Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US government. The authors assume full responsibility for the accuracy and completeness of the ideas presented.
Additional Contributions: We thank all of the previous and current Medicare Patient Safety Monitoring System team members for their contributions to this work, with a special thanks to the abstractors and other team members at the CMS Clinical Data Abstraction Center. They were not compensated for their contributions.