COVID-19 Admission Rates and Changes in Care Quality in US Hospitals.

Importance: Unprecedented increases in hospital occupancy rates during COVID-19 surges in 2020 caused concern over hospital care quality for patients without COVID-19. Objective: To examine changes in hospital nonsurgical care quality for patients without COVID-19 during periods of high and low COVID-19 admissions. Design, Setting, and Participants: This cross-sectional study used data from the 2019 and 2020 Agency for Healthcare Research and Quality's Healthcare Cost and Utilization Project State Inpatient Databases. Data were obtained for all nonfederal, acute care hospitals in 36 states with admissions in 2019 and 2020, and patients without a diagnosis of COVID-19 or pneumonia who were at risk for selected quality indicators were included. The data analysis was performed between January 1, 2023, and March 15, 2024. Exposure: Each hospital and week in 2020 was categorized based on the number of COVID-19 admissions per 100 beds: less than 1.0, 1.0 to 4.9, 5.0 to 9.9, 10.0 to 14.9, and 15.0 or greater. Main Outcomes and Measures: The main outcomes were rates of adverse outcomes for selected quality indicators, including pressure ulcers and in-hospital mortality for acute myocardial infarction, heart failure, acute stroke, gastrointestinal hemorrhage, hip fracture, and percutaneous coronary intervention. Changes in 2020 compared with 2019 were calculated for each level of the weekly COVID-19 admission rate, adjusting for case-mix and hospital-month fixed effects. Changes during weeks with high COVID-19 admissions (≥15 per 100 beds) were compared with changes during weeks with low COVID-19 admissions (<1 per 100 beds). Results: The analysis included 19 111 629 discharges (50.3% female; mean [SD] age, 63.0 [18.0] years) from 3283 hospitals in 36 states. In weeks 18 to 48 of 2020, 35 851 hospital-weeks (36.7%) had low COVID-19 admission rates, and 8094 (8.3%) had high rates. Quality indicators for patients without COVID-19 significantly worsened in 2020 during weeks with high vs low COVID-19 admissions. Pressure ulcer rates increased by 0.09 per 1000 admissions (95% CI, 0.01-0.17 per 1000 admissions; relative change, 24.3%), heart failure mortality increased by 0.40 per 100 admissions (95% CI, 0.18-0.63 per 100 admissions; relative change, 21.1%), hip fracture mortality increased by 0.40 per 100 admissions (95% CI, 0.04-0.77 per 100 admissions; relative change, 29.4%), and a weighted mean of mortality for the selected indicators increased by 0.30 per 100 admissions (95% CI, 0.14-0.45 per 100 admissions; relative change, 10.6%). Conclusions and Relevance: In this cross-sectional study, COVID-19 surges were associated with declines in hospital quality, highlighting the importance of identifying and implementing strategies to maintain care quality during periods of high hospital use.

COVID-19 Admission Rates and Changes in US Hospital Inpatient and Intensive Care Unit Occupancy.

Importance: The COVID-19 pandemic had unprecedented effects on hospital occupancy, with consequences for hospital operations and patient care. Previous studies of occupancy during COVID-19 have been limited to small samples of hospitals. Objective: To measure the association between COVID-19 admission rates and hospital occupancy in different US areas and at different time periods during 2020. Design, Setting, and Participants: This cross-sectional study used data from the Healthcare Cost and Utilization Project State Inpatient Databases (2019-2020) for patients in nonfederal acute care hospitals in 45 US states, including the District of Columbia. Data analysis was performed between September 1, 2022, and April 30, 2023. Exposures: Each hospital and week in 2020 was categorized based on the number of COVID-19 admissions per 100 beds (<1 [low], 1-4.9, 5-9.9, 10-14.9, or ≥15 [high]). Main Outcomes and Measures: The main outcomes were inpatient and intensive care unit (ICU) occupancy. We used regression analysis to estimate the average change in occupancy for each hospital-week in 2020 relative to the same hospital week in 2019. Results: This study included 3960 hospitals and 54 355 916 admissions. Of the admissions in the 40 states used for race and ethnicity analyses, 15.7% were for Black patients, 12.9% were for Hispanic patients, 62.5% were for White patients, and 7.2% were for patients of other race or ethnicity; 1.7% of patients were missing these data. Weekly COVID-19 admission rates in 2020 were less than 4 per 100 beds for 63.9% of hospital-weeks and at least 10 in only 15.9% of hospital-weeks. Inpatient occupancy decreased by 12.7% (95% CI, 12.1% to 13.4%) during weeks with low COVID-19 admission rates and increased by 7.9% (95% CI, 6.8% to 9.0%) during weeks with high COVID-19 admission rates. Intensive care unit occupancy rates increased by 67.8% (95% CI, 60.5% to 75.3%) during weeks with high COVID-19 admissions. Increases in ICU occupancy were greatest when weighted to reflect the experience of Hispanic patients. Changes in occupancy were most pronounced early in the pandemic. During weeks with high COVID-19 admissions, occupancy decreased for many service lines, with occupancy by surgical patients declining by 43.1% (95% CI, 38.6% to 47.2%) early in the pandemic. Conclusions and Relevance: In this cross-sectional study of US hospital discharges in 45 states in 2020, hospital occupancy decreased during weeks with low COVID-19 admissions and increased during weeks with high COVID-19 admissions, with the largest changes occurring early in the pandemic. These findings suggest that surges in COVID-19 strained ICUs and were associated with large decreases in the number of surgical patients. These occupancy fluctuations may have affected quality of care and hospital finances.

Trends in Severe Maternal Morbidity in the US Across the Transition to ICD-10-CM/PCS From 2012-2019.

Importance: Surveillance of severe maternal morbidity (SMM) is critical for monitoring maternal health and evaluating clinical quality improvement efforts. Objective: To evaluate national and state trends in SMM rates from 2012 to 2019 and potential disruptions associated with the transition to International Classification of Diseases, 10th Revision, Clinical Modification and Procedure Coding System (ICD-10-CM/PCS) in October 2015. Design, Setting, and Participants: This repeated cross-sectional analysis examined delivery hospitalizations from 2012 through 2019 in the Healthcare Cost and Utilization Project's National Inpatient Sample and State Inpatient Databases, an all-payer compendium of hospital discharge records from community, nonrehabilitation hospitals. Trends were evaluated using segmented linear binomial regression models that allowed for discontinuities across the ICD-10-CM/PCS transition. Analyses were completed from April 2021 through March 2022. Exposures: Time, ICD-10-CM/PCS coding system, and state. Main Outcomes and Measures: SMM rates, excluding blood transfusion, per 10 000 delivery hospitalizations, overall and by indicator. Results: From 2012 to 2019, there were 5 964 315 delivery hospitalizations in the national sample representing a weighted total of 29.8 million deliveries with a mean (SD) maternal age of 28.6 (5.9) years. SMM rates increased from 69.5 per 10 000 in 2012 to 79.7 per 10 000 in 2019 (rate difference [RD], 10.2; 95% CI, 5.8 to 14.6) without a significant change across the ICD-10-CM/PCS transition (RD, -3.2; 95% CI, -6.9 to 0.6). Of 20 SMM indicators, rates for 10 indicators significantly increased while 3 significantly decreased; 5 of these changes were associated with ICD-10-CM/PCS transition. Acute kidney failure had the largest increase, from 6.4 to 15.3 per 10 000 delivery hospitalizations (RD, 8.9; 95% CI, 7.5 to 10.3) with no change associated with ICD transition (RD, -0.1; 95% CI, -1.2 to 1.1). Disseminated intravascular coagulation had the largest decrease from 31.3 to 21.2 per 10 000 (RD, 10.2; 95% CI, -12.8 to -7.5), with a significant drop associated with ICD transition (RD, -7.9; 95% CI, -10.2 to -5.6). State SMM rates significantly decreased for 1 state and significantly increased for 21 states from 2012 to 2019 and associations with ICD transition varied. Conclusions and Relevance: In this cross-sectional study, overall US SMM rates increased from 2012 to 2019, which was not associated with the ICD-10-CM/PCS transition. However, data for certain indicators and states may not be comparable across coding systems; efforts are needed to understand SMM increases and state variation.

New evidence on geographic disparities in United States hospital capacity.

OBJECTIVE: To characterize the quantity and quality of hospital capacity across the United States. DATA SOURCES: We combine a 2017 near-census of US hospital inpatient discharges from the Healthcare Cost and Utilization Project (HCUP) with American Hospital Association Survey, Hospital Compare, and American Community Survey data. STUDY DESIGN: This study produces local hospital capacity quantity and care quality measures by allocating capacity to zip codes using market shares and population totals. Disparities in these measures are examined by race and ethnicity, income, age, and urbanicity. DATA COLLECTION/EXTRACTION METHODS: All data are derived from pre-existing sources. All hospitals and zip codes in states, including the District of Columbia, contributing complete data to HCUP in 2017 are included. PRINCIPAL FINDINGS: Non-Hispanic Black individuals living in zip codes supplied, on average, 0.11 more beds per 1000 population (SE = 0.01) than places where non-Hispanic White individuals live. However, the hospitals supplying this capacity have 0.36 fewer staff per bed (SE = 0.03) and perform worse on many care quality measures. Zip codes in the most urban parts of America have the least hospital capacity (2.11 beds per 1000 persons; SEM = 0.01) from across the rural-urban continuum. While more rural areas have markedly higher capacity levels, urban areas have advantages in staff and capital per bed. We do not find systematic differences in care quality between rural and urban areas. CONCLUSIONS: This study highlights the importance of lower hospital care quality and resource intensity in driving racial and ethnic, as well as income, disparities in hospital care-related outcomes. This study also contributes an alternative approach for measuring local hospital capacity that accounts for cross-hospital service area flows. Adjusting for these flows is necessary to avoid underestimating the supply of capacity in rural areas and overestimating it in places where non-Hispanic Black individuals tend to live.

Incorporating harms into the weighting of the revised Agency for Healthcare Research and Quality Patient Safety for Selected Indicators Composite (Patient Safety Indicator 90).

OBJECTIVE: To reweight the Agency for Healthcare Research and Quality Patient Safety for Selected Indicators Composite (Patient Safety Indicator [PSI] 90) from weights based solely on the frequency of component PSIs to those that incorporate excess harm reflecting patients' preferences for outcome-related health states. DATA SOURCES: National administrative and claims data involving hospitalizations in nonfederal, nonrehabilitation, acute care hospitals. STUDY DESIGN: We estimated the average excess aggregate harm associated with the occurrence of each component PSI using a cohort sample for each indicator based on denominator-eligible records. We used propensity scores to account for potential confounding in the risk models for each PSI and weighted observations to estimate the "average treatment effect in the treated" for those with the PSI event. We fit separate regression models for each harm outcome. Final PSI weights reflected both the disutilities and the frequencies of the harms. DATA COLLECTION/EXTRACTION METHODS: We estimated PSI frequencies from the 2012 Healthcare Cost and Utilization Project State Inpatient Databases with present on admission data and excess harms using 2012-2013 Centers for Medicare & Medicaid Services Medicare Fee-for-Service data. PRINCIPAL FINDINGS: Including harms in the weighting scheme changed individual component weights from the original frequency-based weighting. In the reweighted composite, PSIs 11 ("Postoperative Respiratory Failure"), 13 ("Postoperative Sepsis"), and 12 ("Perioperative Pulmonary Embolism or Deep Vein Thrombosis") contributed the greatest harm, with weights of 29.7%, 21.1%, and 20.4%, respectively. Regarding reliability, the overall average hospital signal-to-noise ratio for the reweighted PSI 90 was 0.7015. Regarding discrimination, among hospitals with greater than median volume, 34% had significantly better PSI 90 performance, and 41% had significantly worse performance than benchmark rates (based on percentiles). CONCLUSIONS: Reformulation of PSI 90 with harm-based weights is feasible and results in satisfactory reliability and discrimination, with a more clinically meaningful distribution of component weights.

Neonatal Abstinence Syndrome and Maternal Opioid-Related Diagnoses: Analysis of ICD-10-CM Transition, 2013-2017.

BACKGROUND AND OBJECTIVES: Hospital discharge records remain a common data source for tracking the opioid crisis among pregnant women and infants. The International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) transition from the International Classification of Diseases, Ninth Revision, Clinical Modification may have affected surveillance. Our aim was to evaluate this transition on rates of neonatal abstinence syndrome (NAS), maternal opioid use disorder (OUD), and opioid-related diagnoses (OUD with ICD-10-CM codes for long-term use of opioid analgesics and unspecified opioid use). METHODS: Data from the 2013-2017 Healthcare Cost and Utilization Project's National Inpatient Sample were used to conduct, interrupted time series analysis and log-binomial segmented regression to assess whether quarterly rates differed across the transition. RESULTS: From 2013 to 2017, an estimated 18.8 million birth and delivery hospitalizations were represented. The ICD-10-CM transition was not associated with NAS rates (rate ratio [RR]: 0.99; 95% confidence interval [CI]: 0.90-1.08; P = .79) but was associated with 11% lower OUD rates (RR: 0.89; 95% CI: 0.80-0.98; P = .02) and a decrease in the quarterly trend (RR: 0.98; 95% CI: 0.96-1.00; P = .04). The transition was not associated with maternal OUD plus long-term use rates (RR: 0.98; 95% CI: 0.89-1.09; P = .76) but was associated with a 20% overall increase in opioid-related diagnosis rates including long-term and unspecified use (RR: 1.20; 95% CI: 1.09-1.32; P < .001). CONCLUSIONS: The ICD-10-CM transition did not appear to affect NAS. However, coding of maternal OUD alone may not capture the same population across the transition, which confounds the interpretation of trend data spanning this time period.

Neonatal Abstinence Syndrome and Maternal Opioid-Related Diagnoses in the US, 2010-2017.

Importance: Substantial increases in both neonatal abstinence syndrome (NAS) and maternal opioid use disorder have been observed through 2014. Objective: To examine national and state variation in NAS and maternal opioid-related diagnoses (MOD) rates in 2017 and to describe national and state changes since 2010 in the US, which included expanded MOD codes (opioid use disorder plus long-term and unspecified use) implemented in International Classification of Disease, 10th Revision, Clinical Modification. Design, Setting, and Participants: Repeated cross-sectional analysis of the 2010 to 2017 Healthcare Cost and Utilization Project's National Inpatient Sample and State Inpatient Databases, an all-payer compendium of hospital discharge records from community nonrehabilitation hospitals in 47 states and the District of Columbia. Exposures: State and year. Main Outcomes and Measures: NAS rate per 1000 birth hospitalizations and MOD rate per 1000 delivery hospitalizations. Results: In 2017, there were 751 037 birth hospitalizations and 748 239 delivery hospitalizations in the national sample; 5375 newborns had NAS and 6065 women had MOD documented in the discharge record. Mean gestational age was 38.4 weeks and mean maternal age was 28.8 years. From 2010 to 2017, the estimated NAS rate significantly increased by 3.3 per 1000 birth hospitalizations (95% CI, 2.5-4.1), from 4.0 (95% CI, 3.3-4.7) to 7.3 (95% CI, 6.8-7.7). The estimated MOD rate significantly increased by 4.6 per 1000 delivery hospitalizations (95% CI, 3.9-5.4), from 3.5 (95% CI, 3.0-4.1) to 8.2 (95% CI, 7.7-8.7). Larger increases for MOD vs NAS rates occurred with new International Classification of Disease, 10th Revision, Clinical Modification codes in 2016. From a census of 47 state databases in 2017, NAS rates ranged from 1.3 per 1000 birth hospitalizations in Nebraska to 53.5 per 1000 birth hospitalizations in West Virginia, with Maine (31.4), Vermont (29.4), Delaware (24.2), and Kentucky (23.9) also exceeding 20 per 1000 birth hospitalizations, while MOD rates ranged from 1.7 per 1000 delivery hospitalizations in Nebraska to 47.3 per 1000 delivery hospitalizations in Vermont, with West Virginia (40.1), Maine (37.8), Delaware (24.3), and Kentucky (23.4) also exceeding 20 per 1000 delivery hospitalizations. From 2010 to 2017, NAS and MOD rates increased significantly for all states except Nebraska and Vermont, which only had MOD increases. Conclusions and Relevance: In the US from 2010 to 2017, estimated rates of NAS and MOD significantly increased nationally and for the majority of states, with notable state-level variation.

Effects of Hurricanes on Emergency Department Utilization: An Analysis Across 7 US Storms.

OBJECTIVE: Emergency departments (EDs) are critical sources of care after natural disasters such as hurricanes. Understanding the impact on ED utilization by subpopulation and proximity to the hurricane's path can inform emergency preparedness planning. This study examines changes in ED utilization for residents of 344 counties after the occurrence of 7 US hurricanes between 2005 and 2016. METHODS: This retrospective observational study used ED data from the Healthcare Cost and Utilization Project State Inpatient Databases and State Emergency Department Databases. ED utilization rates for weeks during and after hurricanes were compared with pre-hurricane rates, stratified by the proximity of the patient county to the hurricane path, age, and disease category. RESULTS: The overall population rate of weekly ED visits changed little post-hurricane, but rates by disease categories and age demonstrated varying results. Utilization rates for respiratory disorders exhibited the largest post-hurricane increase, particularly 2-3 weeks following the hurricane. The change in population rates by disease categories and age tended to be larger for people residing in counties closer to the hurricane path. CONCLUSIONS: Changes in ED utilization following hurricanes depend on disease categories, age, and proximity to the hurricane path. Emergency managers could incorporate these factors into their planning processes.

Readmissions Following Inpatient Treatment for Opioid-Related Conditions.

BACKGROUND: Previous research suggests that relatively few hospitalized patients with opioid-related conditions receive substance use treatment during their inpatient stay. Without treatment, these individuals may be more likely to have subsequent hospitalizations for continued opioid use disorder. OBJECTIVE: To evaluate the relationship between receipt of inpatient drug detoxification and/or rehabilitation services and subsequent opioid-related readmission. METHODS: This study used combined hospital inpatient discharge and emergency department visit data from the Agency for Healthcare Research and Quality's Healthcare Cost and Utilization Project. Our sample consisted of 329,037 patients from seven states with an opioid-related index hospitalization occurring between March 2010 and September 2013. Multivariate analysis was conducted to examine the relationship between opioid-related readmission and the receipt of inpatient drug detoxification and/or rehabilitation during the index visit. RESULTS: A relatively small percentage (19.4%) of patients with identified opioid-related conditions received treatment for drug use during their hospital inpatient stay. Patients who received drug rehabilitation, but not drug detoxification, during an opioid-related index hospitalization had lower odds of an opioid-related readmission within 90 days of discharge (odds ratio = 0.60, 95% confidence interval = 0.54-0.67) compared with patients with no inpatient drug detoxification or rehabilitation. Conclusions/Importance: A low percentage of patients receive inpatient services for drug use during an index stay involving an opioid-related diagnosis. Our findings indicate that receipt of drug rehabilitation services in acute care hospitals is associated with a lower 90-day readmission rate. Further research is needed to understand factors associated with the receipt of inpatient services and readmissions.

Vital Signs: State-Level Variation in Nonfatal and Fatal Cardiovascular Events Targeted for Prevention by Million Hearts 2022.

INTRODUCTION: Despite its preventability, cardiovascular disease remains a leading cause of morbidity, mortality, and health care costs in the United States. This study describes the burden, in 2016, of nonfatal and fatal cardiovascular events targeted for prevention by Million Hearts 2022, a national initiative working to prevent one million cardiovascular events during 2017-2021. METHODS: Emergency department (ED) visits and hospitalizations were identified using Healthcare Cost and Utilization Project databases, and deaths were identified using National Vital Statistics System data. Age-standardized Million Hearts-preventable event rates and hospitalization costs among adults aged ≥18 years in 2016 are described nationally and across states, as data permit. Expected 2017-2021 event totals and hospitalization costs were estimated assuming 2016 values remain unchanged. RESULTS: Nationally, in 2016, 2.2 million hospitalizations (850.9 per 100,000 population) resulting in $32.7 billion in costs, and 415,480 deaths (157.4 per 100,000) occurred. Hospitalization and mortality rates were highest among men (989.6 and 172.3 per 100,000, respectively) and non-Hispanic blacks (211.6 per 100,000, mortality only) and increased with age. However, 805,000 hospitalizations and 75,245 deaths occurred among adults aged 18-64 years. State-level variation occurred in rates of ED visits (from 56.4 [Connecticut] to 274.8 per 100,000 [Kentucky]), hospitalizations (484.0 [Wyoming] to 1670.3 per 100,000 [DC]), and mortality (111.2 [Vermont] to 267.3 per 100,000 [Mississippi]). Approximately 16.3 million events and $173.7 billion in hospitalization costs could occur during 2017-2021 without preventive intervention. CONCLUSIONS AND IMPLICATIONS FOR PUBLIC HEALTH PRACTICE: Million Hearts-preventable events place a considerable health and economic burden on the United States. With coordinated efforts, many of these events could be prevented in every state to achieve the initiative's goal.

Annual Report on Health Care for Children and Youth in the United States: Focus on 30-Day Unplanned Inpatient Readmissions, 2009 to 2014.

OBJECTIVE: To describe trends in unplanned 30-day all-condition hospital readmissions for children aged 1 to 17 years between 2009 and 2014. METHODS: Analysis was conducted with the 2009-14 Nationwide Readmissions Database from the Agency for Healthcare Research and Quality's Healthcare Cost and Utilization Project. Annual hospital readmission rates, resource use, and the most common reasons for readmission were calculated for the 2009-14 period. RESULTS: The rate of readmission for children aged 1 to 17 years was essentially stable between 2009 and 2014 (5.5% in 2009 and 5.9% in 2014). In 2009, the most common reason (principal diagnosis) for readmission was sickle cell anemia, whereas in 2014 the most common reason was epilepsy. Pneumonia fell from the second to the sixth most common reason for readmission over this period (from 3832 to 2418 stays). Other respiratory infections were among the top 10 principal readmission diagnoses in 2009, but not in 2014. Septicemia was among the 10 most common reasons for readmission in 2014, but not in 2009. Although the average cost of index (ie, initial) stays with a subsequent readmission were similar in 2009 and 2014, the average cost of index stays without a readmission and cost of readmission stays increased by approximately 23%. In both 2009 and 2014, the average cost of the index stays with a subsequent readmission was 73% to 89% higher than that of the index stays of children who were not readmitted within 30 days. The average cost of index stays preceding a readmission was 33% to 45% higher than average costs for readmitted stays. In 2014, the aggregate cost of index stays plus readmissions was $1.58 billion, with 42.9% of the costs attributable to readmissions. Regarding the average costs and lengths of stay for the 10 most common readmission diagnoses, in 2009 the average cost per stay for complications of devices, implants, or grafts was nearly 5 times greater than that of asthma ($21,200 vs $4500, respectively). In 2014, average cost per stay ranged from $5500 for asthma to $39,500 for septicemia. In 2009, the average length of stay (LOS) for complications of devices, implants, or grafts was more than 3 three times higher than that for asthma (7.8 days vs 2.5 days, respectively), and in 2014, the average LOS for septicemia was nearly 4 times higher than that for asthma (10.4 days vs. 2.6 days). CONCLUSIONS: This study provides a baseline assessment for examining trends in 30-day unplanned pediatric readmissions, an important quality metric as the provisions of the Children's Health Insurance Program Reauthorization Act and the Affordable Care Act are changed and implemented in the future. More than 50,000 pediatric hospital stays in 2014 occurred within 30 days of a previous hospitalization, with an average cost of $13,800. This report is timely, as the health care system works to become more patient-centered and public and private payers grapple with how to pay for quality care for children. The report provides baseline information that can be used to further explore ways to reduce unplanned readmissions.

Conversion of the Agency for Healthcare Research and Quality's Quality Indicators from ICD-9-CM to ICD-10-CM/PCS: The Process, Results, and Implications for Users.

OBJECTIVE: To convert the Agency for Healthcare Research and Quality's (AHRQ) Quality Indicators (QIs) from International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) specifications to ICD, 10th Revision, Clinical Modification and Procedure Classification System (ICD-10-CM/PCS) specifications. DATA SOURCES: ICD-9-CM and ICD-10-CM/PCS classifications, General Equivalence Maps (GEMs). STUDY DESIGN: We convened 77 clinicians and coders to evaluate ICD-10-CM/PCS codes mapped from ICD-9-CM using automated GEMs. We reviewed codes to develop "legacy" specifications resembling those in ICD-9-CM and "enhanced" specifications addressing enhanced capabilities of ICD-10-CM/PCS. DATA COLLECTION/EXTRACTION METHODS: We tabulated the numbers of mapped codes, added nonmapped codes, and deleted mapped codes to achieve the specifications. PRINCIPAL FINDINGS: Of 212 clinical concepts (sets of codes) that comprise the QI specifications, we either added nonmapped codes to or deleted mapped codes from 115 (54 percent). The legacy and enhanced specifications differed for 46 sets (22 percent), affecting 67 of the 101 QIs (66 percent). Occasionally, concepts that defied conversion required reformulation of indicators. CONCLUSIONS: Converting the AHRQ QIs to ICD-10-CM/PCS required a detailed, thorough process beyond automated mapping of codes. Differences between the legacy and enhanced versions of the QIs are frequently minor but sometimes substantive.

Returns to Emergency Department, Observation, or Inpatient Care Within 30 Days After Hospitalization in 4 States, 2009 and 2010 Versus 2013 and 2014.

BACKGROUND: Nationally, readmissions have declined for acute myocardial infarction (AMI) and heart failure (HF) and risen slightly for pneumonia, but less is known about returns to the hospital for observation stays and emergency department (ED) visits. OBJECTIVE: To describe trends in rates of 30-day, all-cause, unplanned returns to the hospital, including returns for observation stays and ED visits. DESIGN: By using Healthcare Cost and Utilization Project data, we compared 210,007 index hospitalizations in 2009 and 2010 with 212,833 matched hospitalizations in 2013 and 2014. SETTING: Two hundred and one hospitals in Georgia, Nebraska, South Carolina, and Tennessee. PATIENTS: Adults with private insurance, Medicaid, or no insurance and seniors with Medicare who were hospitalized for AMI, HF, and pneumonia. MEASUREMENTS: Thirty-day hospital return rates for inpatient, observation, and ED visits. RESULTS: Return rates remained stable among adults with private insurance (15.1% vs 15.3%; P = 0.45) and declined modestly among seniors with Medicare (25.3% vs 25.0%; P = 0.04). Increases in observation and ED visits coincided with declines in readmissions (8.9% vs 8.2% for private insurance and 18.3% vs 16.9% for Medicare, both P ≤ 0.001). Return rates rose among patients with Medicaid (31.0% vs 32.1%; P = 0.04) and the uninsured (18.8% vs 20.1%; P = 0.004). Readmissions remained stable (18.7% for Medicaid and 9.5% for uninsured patients, both P > 0.75) while observation and ED visits increased. CONCLUSIONS: Total returns to the hospital are stable or rising, likely because of growth in observation and ED visits. Hospitalists' efforts to improve the quality and value of hospital care should consider observation and ED care.

Trends in Opioid-related Inpatient Stays Shifted After the US Transitioned to ICD-10-CM Diagnosis Coding in 2015.

BACKGROUND: Trend analyses of opioid-related inpatient stays depend on the availability of comparable data over time. In October 2015, the US transitioned diagnosis coding from International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) to ICD-10-CM, increasing from ∼14,000 to 68,000 codes. This study examines how trend analyses of inpatient stays involving opioid diagnoses were affected by the transition to ICD-10-CM. SUBJECTS: Data are from Healthcare Cost and Utilization Project State Inpatient Databases for 14 states in 2015-2016, representing 26% of acute care inpatient discharges in the US. STUDY DESIGN: We examined changes in the number of opioid-related stays before, during, and after the transition to ICD-10-CM using quarterly ICD-9-CM data from 2015 and quarterly ICD-10-CM data from the fourth quarter of 2015 and the first 3 quarters of 2016. RESULTS: Overall, stays involving any opioid-related diagnosis increased by 14.1% during the ICD transition-which was preceded by a much lower 5.0% average quarterly increase before the transition and followed by a 3.5% average increase after the transition. In stratified analysis, stays involving adverse effects of opioids in therapeutic use showed the largest increase (63.2%) during the transition, whereas stays involving abuse and poisoning diagnoses decreased by 21.1% and 12.4%, respectively. CONCLUSIONS: The sharp increase in opioid-related stays overall during the transition to ICD-10-CM may indicate that the new classification system is capturing stays that were missed by ICD-9-CM data. Estimates of stays involving other diagnoses may also be affected, and analysts should assess potential discontinuities in trends across the ICD transition.

The Shifting Landscape in Utilization of Inpatient, Observation, and Emergency Department Services Across Payers.

Recent policies by public and private payers have increased incentives to reduce hospital admissions. Using data from four states from the Agency for Healthcare Research and Quality Healthcare Cost and Utilization Project, this study compared the payer-specific population-based rates of adults using inpatient, observation, and emergency department (ED) services for 10 common medical conditions in 2009 and in 2013. Patients had an expected primary payer of private insurance, Medicare, Medicaid, or no insurance. Across all four payer populations, inpatient admissions declined, and care shifted toward treat-and-release observation stays and ED visits. The percentage of hospitalizations that began with an observation stay increased. Implications for quality of care and costs to patients warrant further examination. Journal of Hospital Medicine 2017;12:443-446.

Development and Validation of an Agency for Healthcare Research and Quality Indicator for Mortality After Congenital Heart Surgery Harmonized With Risk Adjustment for Congenital Heart Surgery (RACHS-1) Methodology.

BACKGROUND: The National Quality Forum previously approved a quality indicator for mortality after congenital heart surgery developed by the Agency for Healthcare Research and Quality (AHRQ). Several parameters of the validated Risk Adjustment for Congenital Heart Surgery (RACHS-1) method were included, but others differed. As part of the National Quality Forum endorsement maintenance process, developers were asked to harmonize the 2 methodologies. METHODS AND RESULTS: Parameters that were identical between the 2 methods were retained. AHRQ's Healthcare Cost and Utilization Project State Inpatient Databases (SID) 2008 were used to select optimal parameters where differences existed, with a goal to maximize model performance and face validity. Inclusion criteria were not changed and included all discharges for patients <18 years with International Classification of Diseases, Ninth Revision, Clinical Modification procedure codes for congenital heart surgery or nonspecific heart surgery combined with congenital heart disease diagnosis codes. The final model includes procedure risk group, age (0-28 days, 29-90 days, 91-364 days, 1-17 years), low birth weight (500-2499 g), other congenital anomalies (Clinical Classifications Software 217, except for 758.xx), multiple procedures, and transfer-in status. Among 17 945 eligible cases in the SID 2008, the c statistic for model performance was 0.82. In the SID 2013 validation data set, the c statistic was 0.82. Risk-adjusted mortality rates by center ranged from 0.9% to 4.1% (5th-95th percentile). CONCLUSIONS: Congenital heart surgery programs can now obtain national benchmarking reports by applying AHRQ Quality Indicator software to hospital administrative data, based on the harmonized RACHS-1 method, with high discrimination and face validity.

Marginal Hospital Cost of Surgery-related Hospital-acquired Pressure Ulcers.

BACKGROUND: Patients who develop hospital-acquired pressure ulcers (HAPUs) are more likely to die, have longer hospital stays, and are at greater risk of infections. Patients undergoing surgery are prone to developing pressure ulcers (PUs). OBJECTIVE: To estimate the hospital marginal cost of a HAPU for adults patients who were hospitalized for major surgeries, adjusted for patient characteristics, comorbidities, procedures, and hospital characteristics. RESEARCH DESIGN AND SUBJECTS: Data are from the Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases and the Medicare Patient Safety Monitoring System for 2011 and 2012. PU information was obtained using retrospective structured record review from trained MPMS data abstractors. Costs are derived using HCUP hospital-specific cost-to-charge ratios. Marginal cost estimates were made using Extended Estimating Equations. We estimated the marginal cost at the 25th, 50th, and 75th percentiles of the cost distribution using Simultaneous Quantile Regression. RESULTS: We find that 3.5% of major surgical patients developed HAPUs and that the HAPUs added ∼$8200 to the cost of a surgical stay after adjusting for comorbidities, patient characteristics, procedures, and hospital characteristics. This is an ∼44% addition to the cost of a major surgical stay but less than half of the unadjusted cost difference. In addition, we find that for high-cost stays (75th percentile) HAPUs added ∼$12,100, whereas for low-cost stays (25th percentile) HAPUs added ∼$3900. CONCLUSIONS: This paper suggests that HAPUs add ∼44% to the cost of major surgical hospital stays, but the amount varies depending on the total cost of the visit.

Differences in severity at admission for heart failure between rural and urban patients: the value of adding laboratory results to administrative data.

BACKGROUND: Rural/urban variations in admissions for heart failure may be influenced by severity at hospital presentation and local practice patterns. Laboratory data reflect clinical severity and guide hospital admission decisions and treatment for heart failure, a costly chronic illness and a leading cause of hospitalization among the elderly. Our main objective was to examine the role of laboratory test results in measuring disease severity at the time of admission for inpatients who reside in rural and urban areas. METHODS: We retrospectively analyzed discharge data on 13,998 hospital discharges for heart failure from three states, Hawai'i, Minnesota, and Virginia. Hospital discharge records from 2008 to 2012 were derived from the State Inpatient Databases of the Healthcare Cost and Utilization Project, and were merged with results of laboratory tests performed on the admission day or up to two days before admission. Regression models evaluated the relationship between clinical severity at admission and patient urban/rural residence. Models were estimated with and without use of laboratory data. RESULTS: Patients residing in rural areas were more likely to have missing laboratory data on admission and less likely to have abnormal or severely abnormal tests. Rural patients were also less likely to be admitted with high levels of severity as measured by the All Patient Refined Diagnosis Related Groups (APR-DRG) severity subclass, derivable from discharge data. Adding laboratory data to discharge data improved model fit. Also, in models without laboratory data, the association between urban compared to rural residence and APR-DRG severity subclass was significant for major and extreme levels of severity (OR 1.22, 95% CI 1.03-1.43 and 1.55, 95% CI 1.26-1.92, respectively). After adding laboratory data, this association became non-significant for major severity and was attenuated for extreme severity (OR 1.12, 95% CI 0.94-1.32 and 1.43, 95% CI 1.15-1.78, respectively). CONCLUSION: Heart failure patients from rural areas are hospitalized at lower severity levels than their urban counterparts. Laboratory test data provide insight on clinical severity and practice patterns beyond what is available in administrative discharge data.