Case Mix Index Research Articles

Commercial Inpatient Hospital Price Growth Driven by System Affiliation and Non-Profit Status Hospitals

Abstract As policymakers continue to grapple with rising health care costs and prices, understanding trends and variations in inpatient prices among hospital characteristics is an important benchmark to allow policymakers to craft targeted policies. In this study, we provide descriptive trends on variation of inpatient prices paid by commercial health plans stratified by hospital characteristics using data from Health Care Cost Institute’s employer-sponsored insured claims data. Our analyses found evidence of considerable variation among inpatient price levels and growth among system affiliation and profitability. Prices among system-affiliated hospitals grew from $14,281.74 in 2012 to $20,731.95 in 2021, corresponding to a 45.2% increase during this period. On the other hand, prices among independent hospitals grew slower from $13,460.50 in 2012 to $18,196.90 in 2021, corresponding to a 35.2% increase. We do not observe a similar trend in growth rates among case mix index by hospital characteristics, implying that differential inpatient price growth is not driven by changes in case mix by hospital characteristics. Heterogeneity in hospital prices and price growth by type of hospital suggests that public and private policymakers aiming to rein in health spending should consider policies that address this variation.

Operational efficiency effect on nursing workload of tertiary hospital wards in China: A prospective observational study.

To investigate the effect of ward operational efficiency on nursing workload and identify the factors that influence nursing workload. It remains unclear how and to what extent ward operational efficiency can influence nursing workload. A prospective observational study was conducted from July 1, 2022 to June 30, 2023, in one tertiary general hospital in China. Purposive and convenience sampling was used, and 266 470 patients from 66 wards and 52 nurses from 13 wards were recruited. The relationships between operational efficiency and nursing workload and the predictors of nursing workload were analyzed. The STROBE guidelines were followed. The operational characteristics vary by the type of wards. Nursing workloads were positively correlated with case mix index (CMI), rate of level 4 surgery, the number of patients transferred in and out, the number of deaths, total bed days, and the number of emergency admissions and critical illnesses (γs: 0.35-0.56, p<0.05). And the CMI, rate of level 4 surgery, average bed occupancy rate, number of critically ill patients, and total bed days were the predictors of nursing workload (R2=57.3%, p<0.05). This study is the first to discuss the relationship between operational efficiency and nursing workload on the ward level and offers valuable insights into the nursing workload. The operational efficiency of wards affects the nursing workload and needs to be considered both in the measurement of nursing activities and in the sizing of the nursing staff. The study findings provide a full understanding of the relationship between ward operation and nurse staffing, which is helpful for nursing managers to formulate scientific nurse staffing policies.

Length of stay and cost of care differences between postoperative patients who board in PACU and those that proceed directly to inpatient bed

BackgroundBottlenecks in PACU throughput are frequently encountered challenges that OR managers must face. This study seeks to examine the impact of extended PACU stay (e.g. boarding) on both total hospital length of stay and overall cost of care. MethodsA total of 4,740 patients were studied having same-day admit surgeries for seven procedure types including: arthroplasty total knee, arthroplasty total hip, fusion spine transforaminal interbody lumbar, revision arthroplasty total knee, revision arthroplasty total hip, posterior cervical fusion, and anterior cervical fusion. 4,471 were identified as non-PACU boarders and 269 as PACU boarders (>6 h in PACU). Included in the analysis were demographics, date of admission, surgical procedure, PACU and hospital length of stay (HLOS), hospital direct costs, case mix index (CMI), and ASA status. ResultsThe median (IQR) PACU times in minutes were 57.00 (80) and 488.00 (453.50) minutes for PACU non-boarders and boarders (p < 0.001). HLOS was significantly (p < 0.001) more elevated in PACU Boarders compared to PACU Non-boarders: median (IQR) 2.00 (2.00) and 2.00 (3.00), (mean HLOS 3.16±2.83 vs 2.60±2.71 days). Median direct costs were ≈14.36% higher (p = 0.008) for the PACU boarders compared to Non-boarders. Direct costs were also not significantly different when using a 4-hour criteria for PACU boarders (14.39% higher median direct costs, p = 0.004). ConclusionsPACU boarding (>4 h) is associated with a statistically significant increased length of stay as well as direct costs compared with non-boarders across a variety of elective orthopedic and spine procedures. Thus, when frequent PACU boarding occurs, OR managers should consider the potential impact to patient care and hospital margins.

Comparison of Externally Transferred and Self-Recruited Patients with Hip and Knee Revision Arthroplasty at a Certified Maximum-Care Arthroplasty Center (ACmax).

According to the guidelines of the EndoCert initiative, certified maximum-care arthroplasty centers (ACmax) are obliged to admit patients from certified arthroplasty centers (AC) if these patients need to be transferred to the more specialized ACmax due to difficult replacement and revision procedures as well as after complications in primary care that are beyond the expertise of the smaller centers. This study investigated whether the cohort of transferred patients differed from the patients directly recruited at the ACmax for factors such as severity of diagnosis, comorbidities or outcome. The aim was to determine whether transferred patients increased the resource requirements for the ACmax. A total of 136 patients were included in the retrospective study and analyzed in terms of case severity, length of hospital stays (LOS), Diagnosis-Related Group charges, readmission rate and concomitant diseases. All patients were followed for up to 12 months after the initial hospital stay. There were significant differences between the groups of transferred and self-recruited patients. For example, transferred patients had a higher Patient Clinical Complexity Level (PCCL). Similarly, the increased Case Mix Index (CMI) of transferred patients indicated more intensive care during the inpatient stay. The higher values for the comorbidity indices also supported these results. This had an impact on the LOS and overall costs, too. The differences between the groups were also reflected by adverse events during the one-year follow-up. The higher percentage of patients with septic revisions, whose treatment is especially demanding, among transferred patients aggravated the differences even further. Thus, transferred patients were associated with increased resource requirements for the ACmax. While it serves patients' safety to transfer them to an ACmax with specialized expertise and greater structural quality, the care of transferred patients ties up considerable resources at the ACmax that might only be insufficiently reimbursed by the generalized tariffs.

Visualizing Hospital Management Data in R Shiny-A Case Study.

There is a demand to make hospital management information beyond basic key performance indicators (KPIs) accessible for clinicians. We developed an interactive application (IAPP) in R Shiny to visualize such information. We provided the IAPP source code online. As a use case, we recorded basic KPIs (numbers of patients (NPs), reimbursed valuation ratios (RVRs), mean length of stay (LOS)), main diagnoses (MDGNs), main procedures (MPRCs), and catchment area (CA) by district from April 2022 to March 2024 at the index department in central Germany, where a neurotrauma and spinal surgery service was resumed on 1 April 2022. Case mix indexes (CMIs) were calculated. We retrieved information about online-reported patient satisfaction (ORPS) from an online physician rating platform between January 2022 and March 2024. Information on longitudes and latitudes of the index department and neighbouring hospitals was collected. We calculated car travelling isochrones (CTIs) of the hospitals as a proxy variable for accessibility. Chi-square and Fisher's exact served as statistical tests. During the observation period, the monthly NPs increased from 26 to 43, the RVR showed a 3.96-fold increase, the CMI showed a 2.41-fold increase, and the LOS reached a steady state in the 2nd year after service resumption. CA (p = 0.03), MDGNs, and MPRCs diversified. ORPS trended towards better overall evaluation after service resumption (p = 0.09). CTI mapping identified a unique market position of the index department. The IAPP makes extended hospital management data accessible to clinicians, can inform other stakeholders in healthcare, and can be tailored to local conditions.

CDI 2.0: Optimising CMI and risk-adjustment value through data analytics and provider education

Accurate clinical documentation is vital for any healthcare facility. Documentation in the record plays a critical role in reimbursement, case mix index, risk adjusted quality outcomes, length of stay days, etc. The need for Clinical documentation improvement (CDI) specialists is high. In this article, we discuss the success we have had by transitioning from a traditional approach to capture severity reporting (CDI 1.0), which relies heavily on the concurrent review of medical records by the CDI team, to a patient population driven and provider focused approach (CDI 2.0).

Managing the patient portfolio using mathematical programming: decision support guidelines using a real-world use case at a university hospital

AbstractMany hospitals in Germany are facing escalating economic pressures. After several years of stagnation, the number of inpatient hospital treatments dropped by $$\:13\%$$ in 2020 compared to the previous year. This negative tendency can also be seen in operating theaters (OTs). Strategic management of the case mix in hospital OTs now necessitates a solid data foundation. The case mix and the case mix index have become central economic indicators in contemporary hospital operations. In this work, we develop a mathematical model for case mix optimization at Augsburg University Hospital in Germany, which is based on an extensive data analysis with descriptive methods. The optimization model is subject to rigorous testing and evaluation through an extensive series of scenario analyses. The primary objective is to calculate a revenue-maximizing patient mix while respecting the available scarce personnel resources in the OT and intensive care unit. This research marks a pioneering effort in delineating the practical integration of case mix planning into a hospital’s routine operations using mathematical optimization. The analyses reveal a strong correlation between an upsurge in revenue and an increased number of cases. Furthermore, the results demonstrate that strategic planning of the patient mix has the potential to enhance revenue with existing resources. Even though the optimal patient mix may not be directly implementable in practice, the findings yield valuable insights for managerial decision-making. A critical examination of these results also fosters a nuanced discourse on the utilization of optimization models as decision support tools within hospital management.

Hospital–Skilled Nursing Facility Integration and Participation in a Bundled Payment Initiative

Reduced institutional postacute care has been associated with savings in alternative payment models. However, organizations may avoid voluntary participation if participation could threaten their own revenues. To characterize the association between hospital-skilled nursing facility (SNF) integration and participation in Medicare's Bundled Payments for Care Improvement Advanced (BPCI-A) program. This is a cross-sectional analysis of hospital participation in BPCI-A beginning with its launch in 2018. Each SNF-integrated hospital was matched with 2 nonintegrated hospitals for each of 4 episode-specific analyses. Fifteen hospital-level variables were used for matching: beds, case mix index, days, area SNF beds, metropolitan location, ownership, region, system membership, and teaching status. Hospitals were also matched on episode-specific volume, target price, and the interaction of target price and case mix. Episode-specific logistic models were estimated regressing hospital participation on integration and the previously listed variables. The marginal effect of integration on participation was then calculated. Analysis took place from August 2022 to May 2024. Hospital-SNF integration, as defined by common ownership and referral patterns and identified using cost reports, Medicare claims, and Provider Enrollment, Chain, and Ownership System records. Additional sources included records of target prices and participation, the Area Health Resources File, and the Compendium of US Health Systems. Participation in BPCI-A. In total, 1524 hospitals met criteria for inclusion in the hip and femur (HFP) analysis, 1825 were included in the major joint replacement of the lower extremity (MJRLE) analysis, 2018 were included in the sepsis analysis, and 1564, were included in the stroke-specific analysis. Across episodes, 191 HFP-eligible hospitals (12.5% of HFP-eligible hospitals), 302 MJRLE-eligible hospitals (16.5%), 327 sepsis-eligible hospitals (16.2%), and 185 sepsis-eligible hospitals (11.8%) were SNF integrated. In total, 79 hospitals (5.2%) participated in the HFP episode, 128 (7.0%) participated in the MJRLE episode, 204 (10.1%) participated in the sepsis episode, and 141 (9.0%) participated in the stroke episode. Integration was associated with a 4.7-percentage point decrease (95% CI, 2.4 to 6.9 percentage points) in participation in the MJRLE episode. There was no association between integration and participation for HFP (0.5-percentage point increase in participation moving from nonintegrated to integrated; 95% CI, -2.9 to 3.8 percentage points), sepsis (1.0-percentage point increase; 95% CI, -2.2 to 4.2 percentage points), and stroke (0.3-percentage point decrease; 95% CI, -3.1 to 3.8 percentage points). In this cross-sectional study, there was an uneven association between hospital-SNF integration and participation in Medicare's BPCI-A program. Other factors may be more consistent determinants of selection into voluntary payment reform.

Changing patterns of routine laboratory testing over time at children's hospitals.

Research into low-value routine testing at children's hospitals has not consistently evaluated changing patterns of testing over time. To identify changes in routine laboratory testing rates at children's hospitals over ten years and the association with patient outcomes. We performed a multi-center, retrospective cohort study of children aged 0-18 hospitalized with common, lower-severity diagnoses at 28 children's hospitals in the Pediatric Health Information Systems database. We calculated average annual testing rates for complete blood counts, electrolytes, and inflammatory markers between 2010 and 2019 for each hospital. A >2% average testing rate change per year was defined as clinically meaningful and used to separate hospitals into groups: increasing, decreasing, and unchanged testing rates. Groups were compared for differences in length of stay, cost, and 30-day readmission or ED revisit, adjusted for demographics and case mix index. Our study included 576,572 encounters for common, low-severity diagnoses. Individual hospital testing rates in each year of the study varied from 0.3 to 1.4 tests per patient day. The average yearly change in hospital-specific testing rates ranged from -6% to +7%. Four hospitals remained in the lowest quartile of testing and two in the highest quartile throughout all 10 years of the study. We grouped hospitals with increasing (8), decreasing (n = 5), and unchanged (n = 15) testing rates. No difference was found across subgroups in costs, length of stay, 30-day ED revisit, or readmission rates. Comparing resource utilization trends over time provides important insights into achievable rates of testing reduction.

The Legacy of the COVID-19 Pandemic: Impact on Infant and Maternal and Health from an Appalachian Academic Medical Center.

The COVID-19 pandemic period from 2020 to 2022 had a significant impact on maternal infant health with mothers impacted more than their infants. We questioned whether there have been any lingering effects from the pandemic. We examined intermediate and long-term pandemic effects on maternal and neonatal outcomes before, during, and after the COVID-19 pandemic period. We reviewed mother-infant pairs from the following three epochs: (1) the pre-COVID-19 period, (2) the COVID-19 pandemic period, and (3) the post-pandemic period. The Case Mix Index (CMI) for the neonates from all three epochs were detailed. Post-pandemic, we noted a rising trend of LGA infants (10%) and an increase in SGA infants (13%). For women in 2023, we noted an increase in hypertension, preeclampsia, diabetes, and a higher BMI than in the pre-pandemic period. There have also been more congenital anomalies (9%), and neonatal CMI increased in the post-pandemic period. Well after the pandemic period, maternal-infant health continues to be affected. For women, the increase in hypertension and diabetes during pregnancy is concerning. For infants, being LGA or SGA may have long-term consequences. The post-pandemic increase in infants with congenital anomalies compared to the pre-pandemic era is an area that needs ongoing review.

Patient Harm Events and Associated Cost Outcomes Reported to a Patient Safety Organization.

The aims of the study were to describe inpatient harm events detected via an automatic electronic trigger system (ETS) and to consider their financial consequences. Over a 27-month period, inpatient harm events were identified and documented in 1 healthcare system with 37 acute care facilities. Patients who experienced harm (all harm or preventable harm only) were compared with similar patients who did not. Clinical, financial, and demographic data were used to identify labor-adjusted direct variable costs (DVC) and potential differences in length of stay (LOS) associated with all-harm, preventable-harm, and nonharmed cohorts. Age-adjusted Charlson Comorbidity Index, case mix index, diagnosis-related groups, major diagnostic category, sex, age, location, diagnosis, adverse event category and subcategory, preventability, and harm severity were used to compare cohorts. Total harm events reported via the ETS and the health system's voluntary event reporting system were compared. Nearly 93,000 encounters for all-harm (n = 25,665) and nonharmed cohorts (n = 67,217) were compared by random sampling of diagnosis-related group-matched all-harm and nonharmed groups to ensure similar clinical conditions, as measured by Charlson Comorbidity Index and case mix index. Sampling (2 groups, n = 100 and n = 200) showed that increased LOS was associated with harm; yet other clinical comparators were similar across groups. the preventable-harm subcohort had longer LOS (10.7 versus 5.9 days) and higher DVC ($13,442 versus $8024) than the nonharmed cohort. Identification of harm events was nearly 6-fold higher with the ETS than with the voluntary event reporting system. Patients with preventable harm had increased LOS that was associated with higher DVC per preventable-harm encounter in a large US healthcare system.

Iterative Health Equity Analyses of Central Line-Associated Bloodstream Infection (CLABSI) Events at a Pediatric Hospital

Background: Per the Centers for Disease Control and Prevention, health equity stipulates all have a fair, just opportunity to attain their highest level of health. Limited evidence exists for disparities in health equity and healthcare-associated infections (HAI), with no evidence on language or primary insurance payor. While reviewing quality metrics, a disparity signal in central line-associated bloodstream infections (CLABSIs) prompted a multidisciplinary deep dive, with iterative analyses to understand potential inequities to identify improvement opportunities. Methods: CLABSI data was stratified and analyzed for evidence of disparity by race/ethnicity, primary insurance payor, and preferred spoken language utilizing an internal methodology. Subsequent analyses included a root cause analysis (RCA), case mix index (CMI) analysis, analysis of CLABSI Kamishibai card (K-card) rounding to monitor maintenance bundle reliability, and comparison of distribution of central venous catheter (CVC) line days to K-card audits [Figure 1]. Chi-square tests were used to test for significant differences for categorical variables in RCA and K-card analyses. ANOVA was used to compare CMI between demographic groups. Multiple logistic regression was used to compare K-card compliance rates by demographic groups. Results: When stratifying CLABSI rate by primary payor, pairwise comparisons indicated patients with a public payor had a statistically higher rate of CLABSI compared to private (p=0.02) [Figure 2A]. RCA analysis revealed when compared to patients with private payors, those with public had significantly higher rates of overdue needless connector changes (p = 0.03) and increased number of daily CVC entries (p = 0.05), while patients speaking another language (p = 0.02) were significantly more likely to have CVC contamination events. CMI analyses on CLABSI cases did not show patient acuity to vary significantly between demographics. Bivariate analysis of K-card data revealed minor differences in reliability with 7 Core Maintenance Bundle Elements by demographics; adjusting for all demographics and accounting for unit, pairwise comparisons indicated public payors had significantly higher compliance than international [Figure 2B]. We found no major differences in demographic distribution of CVC line days compared to K-card audits, suggesting we representatively audit maintenance bundle process measures. Conclusions: Our review of health equity in CLABSI events ultimately led to subsequent questions requiring analysis of other data sets. Utilizing an exploratory approach and assembling a multidisciplinary team to identify potential drivers of identified disparities adds value to health equity analyses. This is the first description of HAI data beyond race and ethnicity and can assist other institutions in their process of evaluating healthcare disparities and HAIs.

Resilience and adaptation: a mixed-methods exploration of COVID-19’s influence on neonatal residency education in China

BackgroundThis study aimed to assess the impact of the pandemic of the coronavirus disease 2019 (COVID-19) on neonatology residency training in a tertiary children’s hospital in Chongqing, located in southwest China. Specifically, the study encompassed the effects on residents’ education, lived experiences, well-being, and the quality of neonatal care delivered. As higher educational institutions adapt to the post-COVID-19 era after the pandemic disruption, it is imperative that educational designers/academics learn from their experiences and challenges in curriculum design and delivery, ensuring quality and relevance in education.MethodsThis study employed a mixed-methods approach to investigate the influence of the COVID-19 pandemic on neonatology residency training at a tertiary children’s hospital in Chongqing. The first phase surveyed residents’ perceptions and experiences of their clinical education and well-being during the crisis. The second phase compared the quality of neonatal care between the pre-pandemic and pandemic periods.ResultsThe survey of 123 neonatology residents examines the effects of COVID-19 on their learning, training, and mental health. The survey showed that most residents adapted well to the situation. Still, some faced challenges in their clinical education and experiences, such as reduced clinical exposure and opportunities to see rare diseases and conditions. A retrospective analysis of clinical data revealed that 7,151 neonates were admitted to the neonatology department during the study period. There was a 27.6% decrease in neonatal admissions during COVID-19, with more premature births and transfers. Residents conducted fewer clinical procedures but managed more complex cases. During COVID, hospital stays and costs were higher, but antibiotic use was lower. Although the case-mix index (CMI) score increased during the pandemic (1.25 vs. 1.18, p < 0.05), there was no significant difference in the rates of readmission within 7 days or poor prognosis.ConclusionsDespite reduced clinical exposure, the quality of neonatal care was maintained through innovative training methods that enhanced comprehensive residency programs. The study suggested that neonatology residency education remained effective and resilient during the crisis. Exceptional health professional education is vital to train qualified physicians and enhance healthcare systems for future challenges.

Comparing Hospital Length of Stay Between Persons With LEP and English-Speaking Patients in a Large Rural Academic Medical Center.

Patients with limited English proficiency that are hospitalized without regular access to professional medical interpreters have a longer length of stay (LOS).1 The authors studied the difference in LOS between English-speaking patients and patients with limited English proficiency in New Hampshire's only academic trauma medical center. The authors also examined race, ethnicity, and distance of residence from hospital. Retrospective data were queried from EPIC, the electronic medical record system used by the authors. Queried data focused on inpatient hospitalizations between January 1, 2019, and December 31, 2021. Patient primary language was grouped into English, Spanish, and other non-English language. Spanish-speaking patients live on average 39.6 miles farther from a hospital than English-speaking patients and have a 0.34 lower case mix index. After English, Spanish is the second-most frequently spoken language. Regression analyses found language to be a significant factor in LOS, LOS variance, and case mix index. A 2.34-day longer LOS for Spanish-speaking patients demonstrates an important health care disparity warranting further attention.

Impact of staffing an inpatient oncology consult service with dedicated hospital-based oncologists.

e13538 Background: No standard care model for hospitalized patients with cancer currently exists. The majority of oncology care is provided in the outpatient setting. Consequently, for many oncologists, inpatient care is a secondary responsibility. To address this issue, several U.S. institutions have changed their inpatient oncology models to include dedicated inpatient oncology-trained attendings (hospital-based oncologists). In this study, we investigate outcomes following such a change on an inpatient solid oncology consult service. Methods: This is a single-institution retrospective study of the University of California, San Francisco (UCSF)’s inpatient solid oncology consult service, which switched from a rotating outpatient oncologist attending model to a dedicated inpatient oncologist model in 2018. Since data prior to the switch were not available, we compared all inpatients who received an oncology consult (n=3974) with a comparison group (CG) of all inpatients with a solid tumor diagnosis who did not receive an oncology consult (n=3200) between fiscal years 2018 and 2023. We analyzed trends in case mix index (case complexity), in-hospital mortality index (observed/expected), 30-day unplanned readmission rate, length of stay index, and direct cost index using the Pearson correlation coefficient. In 2022, we conducted an anonymous retrospective pre/post-switch survey of hospitalists who had worked on the hospital medicine service both before and after the switch and compared mean satisfaction with oncology consultation (5-point scale) using Student’s t test. Results: Following the switch, the number of weeks per year covered by dedicated inpatient oncologists increased from seven (2018) to 39 (2023) out of 52. From 2018 to 2023, we found a significant increase in annual consults (495 to 919, r=0.95, p&lt;0.01) and case mix index (2.30 to 3.30, r=0.90, p=0.01; CG 1.60 to 1.70, r=0.60, p=0.20), and a significant decrease in mortality index (1.59 to 0.70, r=-0.98, p&lt;0.01; CG 0.90 to 0.48, r=-0.66, p=0.15). There were non-significant decreases in 30-day unplanned readmission rate (35.8% to 30.5%, r=-0.77, p=0.07), length of stay index (1.45 to 1.32, r=-0.75, p=0.08), and direct cost index (2.2 to 1.8, r=-0.61, p=0.20), similar to the CG. Mean (standard deviation) satisfaction increased from 3.27 (1.22) pre-switch to 4.53 (0.92) post-switch ( p=0.02, response rate 15/51, 29%). Conclusions: Switching to a dedicated inpatient oncologist model on a consult service was associated with decreased in-hospital mortality and increased satisfaction with oncology consultation despite growing case volume and complexity, while readmission rate, length of stay, and cost remained unchanged. This study adds to growing literature supporting dedicated inpatient oncologist attending models of care and is in line with recent literature from other specialties supporting similar inpatient care models.

Impacts of DRG-Based Prepayment Reform on the Cost and Quality of Patients with Neurologic Disorders: Evidence from a Quasi-Experimental Analysis in Beijing, China.

As one of the pioneering pilot cities in China's extensive Diagnosis Related Groups (DRG) -based prepayment reform, Beijing is leading a comprehensive overhaul of the prepayment system, encompassing hospitals of varying affiliations and tiers. This systematic transformation is rooted in extensive patient group data, with the commencement of actual payments on March 15, 2022. This study aims to evaluate the effectiveness of DRG payment reform by examining how it affects the cost, volume, and utilization of care for patients with neurological disorders. Utilizing the exogenous shock resulting from the implementation of the DRG-based prepayment system, we adopted the Difference-in-Differences (DID) approach to discern changes in outcome variables among DRG payment cases, in comparison to control cases, both before and following the enactment of the DRG policy. The analytical dataset was derived from patients diagnosed with neurological disorders across all hospitals in Beijing that underwent the DRG-based prepayment reform. Strict data inclusion and exclusion criteria, including reasonableness tests, were applied, defining the pre-reform timeframe as March 15th through October 31st, 2021, and the post-reform timeframe as the corresponding period in 2022. The extensive dataset encompassed 53 hospitals and encompassed hundreds of thousands of cases. The implementation of DRG-based prepayment resulted in a substantial 12.6% decrease in total costs per case and a reduction of 0.96 days in length of stay. Additionally, the reform was correlated with significant reductions in overall in-hospital mortality and readmission rates. Surprisingly, the study unearthed unintended consequences, including a significant reduction in the proportion of inpatient cases classified as surgical patients and the Case Mix Index (CMI), indicating potential strategic adjustments by providers in response to the introduction of DRG payments. The DRG payment reform demonstrates substantial effects in restraining cost escalation and enhancing quality. Nevertheless, caution must be exercised to mitigate potential issues such as patient selection bias and upcoding.

Development and Validation of the Bone Tumor Surgery Complexity Score

Bone tumors often manifest with non-specific symptoms such as pain and swelling, often posing diagnostic challenges. Optimal treatment requires centralized care in specialized centers, emphasizing the need for complete tumor removal and interdisciplinary collaboration. We developed the Bone Tumor Surgery Complexity Score (BT-SCS) based on a retrospective study of 501 patients. The BT-SCS, structured around patient demographics, tumor biology, and surgical parameters, categorizes surgical cases into four groups to comprehensively assess complexity. Application of the BT-SCS resulted in scores ranging from 3 to 33, with an average score of 14 ± 7.2. Patients with malignant tumors had higher scores (19.6 ± 5.2) compared to those with benign (10.0 ± 3.8) or intermediate malignant tumors (14.6 ± 7.1). Patients with pelvic tumors registered the highest scores (16.0), followed by extremities/trunk (14.3) and spinal tumors (13.6). The BT-SCS was validated against the Case Mix Index (CMI), using an independent cohort of bone and soft tissue cases. This validation process, utilizing Loess smoothing, illustrated the BT-SCS’s granular differentiation of surgical complexity, particularly in the lower-to-mid-range of case severities. The BT-SCS represents a significant shift from volume-based to complexity-based assessments in surgical care, aligning with evolving healthcare paradigms. It serves as a tool for strategic patient allocation to treatment centers, aiming to improve outcomes and benchmarking in sarcoma care. The score’s development and application in clinical practice align with the focus on patient-centered and value-based healthcare. Future enhancements, including machine learning integration and outcome data, will refine its categorization process, enhancing clinical utility.

Implementation of a Real-Time Documentation Assistance Tool: Automated Diagnosis (AutoDx).

Clinical documentation improvement programs are utilized by most health care systems to enhance provider documentation. Suggestions are sent to providers in a variety of ways, and are commonly referred to as coding queries. Responding to these coding queries can require significant provider time and do not often align with workflows. To enhance provider documentation in a more consistent manner without creating undue burden, alternative strategies are required. The aim of this study is to evaluate the impact of a real-time documentation assistance tool, named AutoDx, on the volume of coding queries and encounter-level outcome metrics, including case-mix index (CMI). The AutoDx tool was developed utilizing tools existing within the electronic health record, and is based on the generation of messages when clinical conditions are met. These messages appear within provider notes and required little to no interaction. Initial diagnoses included in the tool were electrolyte deficiencies, obesity, and malnutrition. The tool was piloted in a cohort of Hospital Medicine providers, then expanded to the Neuro Intensive Care Unit (NICU), with addition diagnoses being added. The initial Hospital Medicine implementation evaluation included 590 encounters pre- and 531 post-implementation. The volume of coding queries decreased 57% (p < 0.0001) for the targeted diagnoses compared with 6% (p = 0.77) in other high-volume diagnoses. In the NICU cohort, 829 encounters pre-implementation were compared with 680 post. The proportion of AutoDx coding queries compared with all other coding queries decreased from 54.9 to 37.1% (p < 0.0001). During the same period, CMI demonstrated a significant increase post-implementation (4.00 vs. 4.55, p = 0.02). The real-time documentation assistance tool led to a significant decrease in coding queries for targeted diagnoses in two unique provider cohorts. This improvement was also associated with a significant increase in CMI during the implementation time period.

The value of admission avoidance: cost-consequence analysis of one-year activity in a consolidated service.

Many advantages of hospital at home (HaH), as a modality of acute care, have been highlighted, but controversies exist regarding the cost-benefit trade-offs. The objective is to assess health outcomes and analytical costs of hospital avoidance (HaH-HA) in a consolidated service with over ten years of delivery of HaH in Barcelona (Spain). A retrospective cost-consequence analysis of all first episodes of HaH-HA, directly admitted from the emergency room (ER) in 2017-2018, was carried out with a health system perspective. HaH-HA was compared with a propensity-score-matched group of contemporary patients admitted to conventional hospitalization (Controls). Mortality, re-admissions, ER visits, and direct healthcare costs were evaluated. HaH-HA and Controls (n = 441 each) were comparable in terms of age (73 [SD16] vs. 74 [SD16]), gender (male, 57% vs. 59%), multimorbidity, healthcare expenditure during the previous year, case mix index of the acute episode, and main diagnosis at discharge. HaH-HA presented lower mortality during the episode (0 vs. 19 (4.3%); p < 0.001). At 30 days post-discharge, HaH-HA and Controls showed similar re-admission rates; however, ER visits were lower in HaH-HA than in Controls (28 (6.3%) vs. 34 (8.1%); p = 0.044). Average costs per patient during the episode were lower in the HaH-HA group (€ 1,078) than in Controls (€ 2,171). Likewise, healthcare costs within the 30 days post-discharge were also lower in HaH-Ha than in Controls (p < 0.001). The study showed higher performance and cost reductions of HaH-HA in a real-world setting. The identification of sources of savings facilitates scaling of hospital avoidance. ClinicalTrials.gov (26/04/2017; NCT03130283).

Outcomes of Chengdu Pediatric Emergency Triage Criteria: A Retrospective Study of 198,628 Pediatric Patient Records.

BACKGROUND The Chengdu pediatric emergency triage criteria were developed at our hospital and consist of 4 triage levels: immediate treatment (level 1), treatment within 10 min (level 2), treatment within 30 min (level 3), and treatment within 240 min (level 4). This study aimed to evaluate outcomes from the levels 1 to 4 of this triage criteria. MATERIAL AND METHODS A self-designed survey form was used to collect pediatric Emergency Department (ED) patients' general data, including age, sex, and chief concern, and clinical data, including triage level, whether the patient had died, and whether the patient was admitted to our hospital. A total of 198,628 patient records that were triaged during January to May 2022 using Chengdu pediatric emergency triage criteria were included in this retrospective study. The numbers of patients triaged to levels 1, 2, 3, and 4 were 128, 1164, 14,560, and 182,776, respectively. RESULTS Statistically significant differences were found in waiting time for treatment, hospital admission rates, admission conversion rates, and case mix index at admission under different triage levels. The higher the triage priority level, the shorter the waiting time for ED treatment, higher the hospital admission and admission conversion rates, and higher case mix index value. CONCLUSIONS The Chengdu pediatric emergency triage criteria developed and applied within our hospital appears to be characterized by good clinical validity. Equipped with this triage criteria, triage nurses are more capable of determining the severity and emergency of the pediatric ED patients' health conditions and effectively triaging the patients.