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Ellenbogen, Michael I., Laura Prichett, Pamela T. Johnson, and Daniel J. Brotman. "Development of a Simple Index to Measure Overuse of Diagnostic Testing at the Hospital Level Using Administrative Data." Journal of Hospital Medicine 16, no. 2 (February 1, 2021): 77–83. http://dx.doi.org/10.12788/jhm.3547.
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OBJECTIVE: We developed a diagnostic overuse index that identifies hospitals with high levels of diagnostic intensity by comparing negative diagnostic testing rates for common diagnoses. METHODS: We prospectively identified candidate overuse metrics, each defined by the percentage of patients with a particular diagnosis who underwent a potentially unnecessary diagnostic test. We used data from seven states participating in the State Inpatient Databases. Candidate metrics were tested for temporal stability and internal consistency. Using mixed-effects ordinal regression and adjusting for regional and hospital characteristics, we compared results of our index with three Dartmouth health service area-level utilization metrics and three Medicare county-level cost metrics. RESULTS: The index was comprised of five metrics with good temporal stability and internal consistency. It correlated with five of the six prespecified overuse measures. Among the Dartmouth metrics, our index correlated most closely with physician reimbursement, with an odds ratio of 2.02 (95% CI, 1.11-3.66) of being in a higher tertile of the overuse index when comparing tertiles 3 and 1 of this Dartmouth metric. Among the Medicare county-level metrics, our index correlated most closely with standardized costs of procedures per capita, with an odds ratio of 2.03 (95% CI, 1.21-3.39) of being in a higher overuse index tertile when comparing tertiles 3 and 1 of this metric. CONCLUSIONS: We developed a novel overuse index that is preliminary in nature. This index is derived from readily available administrative data and shows some promise for measuring overuse of diagnostic testing at the hospital level.
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Shannon, Elizabeth A., B. Anne Brand, Kevin M. Ratcliffe, and Bruce K. Tranter. "Developing metrics for hospital medical workforce allocation." Australian Health Review 31, no. 3 (2007): 411. http://dx.doi.org/10.1071/ah070411.
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Public hospitals deliver a broad range of specialist treatments to patients, with public demand for hospital services almost always outstripping supply. Health department and hospital managers prioritise requests for additional resources, such as medical staffing, across the full spectrum of services delivered. Without a clear and equitable basis of workload comparison across medical specialties, this decision-making process can be controversial and internally divisive. This paper outlines the development of a metric to guide the allocation of hospital medical staff. It suggests that a valid comparison of workload can be gained from the consideration of the number of inpatients (weighted for case complexity) and the number of outpatient presentations, as seen by each full-time hospital medical practitioner per annum. While this supports a ?common sense? understanding of hospital medical activity, it also reflects limitations in the quality and quantity of data available. The replication and testing of this methodology in other jurisdictions is encouraged.
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Auger, Katherine A., Ronald J. Teufel, J. Mitchell Harris, James C. Gay, Mark A. Del Beccaro, Mark I. Neuman, Javier Tejedor-Sojo, et al. "Children’s Hospital Characteristics and Readmission Metrics." Pediatrics 139, no. 2 (January 25, 2017): e20161720. http://dx.doi.org/10.1542/peds.2016-1720.
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Dyer, April, Elizabeth Dodds Ashley, Angelina Davis, Melissa Johnson, Travis Jones, and Rebekah W. Moehring. "1629. Targeted Antimicrobial Use Admission Provides an Actionable Denominator for Antimicrobial Stewardship Programs Evaluating Inpatient Length of Therapy." Open Forum Infectious Diseases 5, suppl_1 (November 2018): S42. http://dx.doi.org/10.1093/ofid/ofy209.099.
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Abstract Background Actionable, easy to interpret antibiotic use (AU) metrics provide antimicrobial stewardship programs (ASPs) with clear targets. Current aggregate AU metrics lack the ability to discriminate between long courses in a limited number of patients versus short courses in a large number of patients. Methods We developed a novel AU denominator termed “targeted antimicrobial use admission,” defined as an inpatient admission in which a selected agent or group of agents was administered. When used with length of therapy (LOT), it provides the average number of days patients receive the targeted agent(s) during inpatient hospital admissions. To demonstrate the added utility of this metric, we used descriptive statistics to compare it to LOT, LOT/1,000 patient days, LOT/1,000 admissions, and LOT/admission to quantify intravenous (IV) vancomycin use among 25 hospitals in the Duke Antimicrobial Stewardship Outreach Network (DASON) for calendar year 2017. The metric was also used to compare hospitals to one another and track durations at an example hospital over time. Results Total LOT included 128,680 days of IV vancomycin (table). LOT/targeted antimicrobial use admission is the only metric that allows programs to quickly assess agent durations. Conclusion Stewardship programs seeking to shorten durations of therapy can track this metric over time to determine the impact of their ASP efforts (Figure 1). The metric can also be used to compare average durations of IV vancomycin by hospital to determine when and if agent-focused audit and feedback or antibiotic timeouts may be useful (Figure 2). The network mean provides a target for agent-specific de-escalations, in days, for facilities with longer durations. LOT/targeted antimicrobial use admission provides an actionable metric for quantifying antimicrobial durations. This metric is easy to interpret and can feasibly be captured through the electronic prescribing record to aid in selecting ASP strategy. Disclosures All authors: No reported disclosures.
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Kinney, Ashley, Quyen Bui, Jane Hodding, and Jennifer Le. "Pharmacy Dashboard: An Innovative Process for Pharmacy Workload and Productivity." Hospital Pharmacy 52, no. 3 (March 2017): 198–206. http://dx.doi.org/10.1310/hpj5203-198.
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Background Innovative approaches, including LEAN systems and dashboards, to enhance pharmacy production continue to evolve in a cost and safety conscious health care environment. Furthermore, implementing and evaluating the effectiveness of these novel methods continues to be challenging for pharmacies. Objective To describe a comprehensive, real-time pharmacy dashboard that incorporated LEAN methodologies and evaluate its utilization in an inpatient Central Intravenous Additives Services (CIVAS) pharmacy. Methods Long Beach Memorial Hospital (462 adult beds) and Miller Children's and Women's Hospital of Long Beach (combined 324 beds) are tertiary not-for-profit, community-based hospitals that are served by one CIVAS pharmacy. Metrics to evaluate the effectiveness of CIVAS were developed and implemented on a dashboard in real-time from March 2013 to March 2014. Results The metrics that were designed and implemented to evaluate the effectiveness of CIVAS were quality and value, financial resilience, and the department's people and culture. Using a dashboard that integrated these metrics, the accuracy of manufacturing defect-free products was ≥99.9%, indicating excellent quality and value of CIVAS. The metric for financial resilience demonstrated a cost savings of $78,000 annually within pharmacy by eliminating the outsourcing of products. People and value metrics on the dashboard focused on standard work, with an overall 94.6% compliance to the workflow. Conclusion A unique dashboard that incorporated metrics to monitor 3 important areas was successfully implemented to improve the effectiveness of CIVAS pharmacy. These metrics helped pharmacy to monitor progress in real-time, allowing attainment of production goals and fostering continuous quality improvement through LEAN work.
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Lichtman, Judith H., Erica C. Leifheit, Yun Wang, and Larry B. Goldstein. "Hospital Quality Metrics: “America's Best Hospitals” and Outcomes After Ischemic Stroke." Journal of Stroke and Cerebrovascular Diseases 28, no. 2 (February 2019): 430–34. http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2018.10.022.
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Schoppy, David W., Yifei Ma, Kim Felder Rhoads, Michelle M. Chen, Brian Nussenbaum, Ryan K. Orosco, Eben Lloyd Rosenthal, and Vasu Divi. "Association of surgical quality metrics and hospital-level overall survival for patients with head and neck squamous cell carcinoma." Journal of Clinical Oncology 35, no. 8_suppl (March 10, 2017): 206. http://dx.doi.org/10.1200/jco.2017.35.8_suppl.206.
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206 Background: Both negative margins and lymph node yields ≥ 18 from neck dissections in patients with head and neck mucosal squamous cell carcinomas (SCC) have been correlated with improved overall survival. It is unclear whether these patient-level metrics are applicable at the hospital level, and what compliance rates hospitals would need to achieve to show an improvement in survival. Methods: The National Cancer Database (NCDB) was used to identify patients who underwent primary surgery that included a neck dissection for mucosal SCC of the oral cavity, oropharynx, larynx, and hypopharynx. The percentage of patients at each institution with negative margins on primary resection and lymph node yields ≥ 18 from a neck dissection was quantified. A multivariate Cox proportional hazard regression was used to determine the association between hospital compliance rates with these metrics and overall survival. Results: There were 65,097 patients at 1,087 hospitals in the NCDB who underwent a neck dissection for mucosal SCC of the head and neck. A total of 221 hospitals (20%) had lymph node yields of 18 or higher in ≥ 80% of patients, and 137 hospitals (12.6%) achieved negative margins in ≥ 90% of patients. Patients treated at hospitals that attained the combined quality metric of ≥ 80% compliance rate with lymph node counts and a ≥ 90% compliance rate with negative margins, showed a significant improvement in overall survival (hazard ratio [HR] 0.93; 95% CI 0.89 to 0.98). This benefit in survival was independent of the patient level improvement seen from having negative margins (HR 0.73; 95% CI 0.70 to 0.75) and a lymph node count ≥ 18 (HR 0.85; 95% CI 0.83 to 0.88). Treatment at high volume or academic teaching hospitals was not independently associated with improved survival once the model controlled for margin status and lymph node count. Conclusions: Patients with head and neck mucosal SCC experience better survival when treated at hospitals achieving a combined quality metric based on lymph node counts and negative margin rates. National tracking of these modifiable quality metrics may identify facilities that would benefit from quality improvement measures.
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Davies, S. M., O. Saynina, K. M. McDonald, and L. C. Baker. "Limitations of using same-hospital readmission metrics." International Journal for Quality in Health Care 25, no. 6 (October 27, 2013): 633–39. http://dx.doi.org/10.1093/intqhc/mzt068.
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Unger, Scott R., Nicole Campion, Melissa M. Bilec, and Amy E. Landis. "Evaluating quantifiable metrics for hospital green checklists." Journal of Cleaner Production 127 (July 2016): 134–42. http://dx.doi.org/10.1016/j.jclepro.2016.03.167.
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Hostler, Christopher, Rebekah W. Moehring, Arthur W. Baker, Becky Smith, Linda Adcock, Brittain Wood, Evelyn Cook, et al. "The Effect of National Healthcare Safety Network (NHSN) Rebaselining on Community Hospital SIRs." Open Forum Infectious Diseases 4, suppl_1 (2017): S50—S51. http://dx.doi.org/10.1093/ofid/ofx162.119.
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Abstract Background The NHSN recently updated risk adjustment models and “rebaselined” Standardized Infection Ratios (SIRs) for healthcare-associated infections. The CDC expected that hospital SIRs would generally increase. However, the impact of rebaselining on individual hospitals’ SIRs was unknown. Accordingly, we assessed the impact of rebaselining on SIRs in a network of community hospitals. Methods We analyzed 2016 SIR data for CAUTI, MRSA LabID events, CDI LabID events, colon SSIs (COLO), and abdominal hysterectomy SSIs (HYST) from 38 hospitals in the Duke Infection Control Outreach Network (DICON). SIRs calculated using the old and new baselines were compared. Wilcoxon signed rank test was performed to determine whether hospitals’ SIRs changed significantly following rebaselining. Hospitals were ranked by SIR for each metric, and change in rank following rebaselining was determined. Meaningful change in rank was defined as increase or decrease by ≥4 places (greater than a decile). Hospitals that did not have an SIR calculated for a given metric were excluded from that metric’s analysis. Results Median hospital SIRs for CAUTI and CDI increased significantly after rebaselining (0.587 vs 0.307, P < 0.001; and 0.825 vs 0.783, p = 0.04, respectively). Median MRSA SIRs increased (0.903 vs 0.797, P = 0.5), and COLO and HYST SIRs decreased (0.457 vs 0.586, P = 0.1; and 0 vs 0.489, P = 0.4); however, these changes were not statistically significant (Figure 1). For all metrics, a minority of hospitals had meaningful change in SIR rank following rebaselining (Figure 2). Conclusion SIRs increased following rebaselining for CAUTI and CDI but did not change significantly for MRSA, COLO, or HYST. The majority of hospitals’ SIR rank did not change meaningfully following rebaselining. Disclosures D. Sexton, Centers for Disease Control and Prevention: Grant Investigator, Grant recipient; Centers for Disease Control and Prevention Foundation: Grant Investigator, Grant recipient; UpToDate: Collaborator, Royalty Recipient
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Walker, Suzan, Herman Hedriana, Suzanne Wiesner, Barbara Pelletreau, Jane Hitti, Thomas Benedetti, and Laurence Shields. "A Comparison of the Nulliparous-Term-Singleton-Vertex and Society of Maternal–Fetal Medicine Cesarean Birth Metrics Based on Hospital Size." American Journal of Perinatology 35, no. 04 (November 3, 2017): 390–96. http://dx.doi.org/10.1055/s-0037-1607985.
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Objective The purpose of this study was to compare the nulliparous-term-singleton-vertex (NTSV) and the Society of Maternal–Fetal Medicine (SMFM) cesarean birth metrics as tools for quality improvement efforts based on hospital size. Materials and Methods Cesarean birth rates from 275 hospitals from six states were used to evaluate the NTSV metric and 81 hospitals from four states for the SMFM metric. Data were assessed based on delivery volume, their use as an effective tool for ongoing quality improvement programs, and their ability to serve as performance-based payline indicators. Results The average NTSV and SMFM cesarean birth rates were 25.6 and 13.0%, respectively. The number of deliveries included in the NTSV metric was stable across all hospital sizes (33.1–36.2%). With the SMFM metric, there was a progressive decline in the number of deliveries included, 90.0 versus 69.6%, in relatively small to large facilities. Variability was less and precision increased with the SMFM metric, which reduced the number of hospitals that could be incorrectly categorized when using performance-based predefined cesarean birth rate paylines. Conclusion The SMFM metric appears to be better suited as a tool for rapid process improvement programs aimed at reducing cesarean birth rates in low-risk patients.
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Thomas, Mini, Aemilio W. Ha, Victor C. Joe, Theresa L. Chin, and Nicole O. Bernal. "584 Maintaining Success in Patient Safety and Quality Metrics Through Teamwork and Contextual Modification." Journal of Burn Care & Research 41, Supplement_1 (March 2020): S133—S134. http://dx.doi.org/10.1093/jbcr/iraa024.210.
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Abstract Introduction Hospitals constantly invest heavily in improving patient quality and safety metrics. Oftentimes, success is achievable for a single parameter but becomes challenging to achieve in all quality metrics. Our Burn Unit aimed for overarching principles of teamwork and contextual modification to obtain outstanding quality metrics in all six areas of patient quality. This quality improvement project aimed at reducing patient harms related to CLABSI, CAUTI, VTE, C.Diff, Falls, and HAPI. Methods Hospital leadership launched a dashboard of all six patient harm areas that is updated and reported daily during the leadership huddle. Apart from ensuring best practices for each quality metric, Burn Unit focused on teamwork and contextual modifications. Team work: Utilizing engaged multidisciplinary team, apparent cause analysis of identified cases helped in detecting fallibilities. Additionally, proactive actions were implemented for high risk patients by routine audits and discussions during daily rounds. Frequent reminders, appreciations, and celebration of success were also helpful strategies. Contextual modification: Unit and population specific interventions were actively sought for each quality metric. For HAPI, delineating existing burns or skin conditions from that of hospital acquired pressure injury helped to eliminate false positives. Identification and modification of patient care environment was vital in reducing the number of falls. Chlorhexidine gluconate bathing of burn wounds reduced the bioburden to help decrease CAUTI/CLABSIs. Results Burn Unit events related to CLABSI, CAUTI, VTE, C.Diff, Falls, and HAPI were compared to other ICUs, Step down, Telemetry, and Med-Surg units. The BICU was below national benchmarks for all six quality indicators and had outstanding success within the hospital. Our last patient harm was 328 days ago compared to the other 11 units which averaged 32 days. In the non-ICU units, Falls were the most frequent patient harm compared to CLABSI or CAUTIS for the ICUs. In Comparing to previous year, in 2019, BICU progressed in CAUTI, Falls, and HAPI while sustaining 2018 excellence in the other three areas. Conclusions Quality metrics can be simultaneously achieved by strengthening team work and contextual modifications beyond following best practices and protocols. Applicability of Research to Practice Quality metrics can be simultaneously achieved by strengthening team work and contextual modifications beyond following best practices and protocols. Strong teamwork from engaged staff and contextual modifications should be intertwined with scientific evidences for obtaining quality metrics in patient care.
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Tambeur, Wim, Pieter Stijnen, Guy Vanden Boer, Pieter Maertens, Caroline Weltens, Frank Rademakers, Dirk De Ridder, Kris Vanhaecht, and Luk Bruyneel. "Standardised mortality ratios as a user-friendly performance metric and trigger for quality improvement in a Flemish hospital network: multicentre retrospective study." BMJ Open 9, no. 9 (September 2019): e029857. http://dx.doi.org/10.1136/bmjopen-2019-029857.
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ObjectiveTo illustrate the development and use of standardised mortality rates (SMRs) as a trigger for quality improvement in a network of 27 hospitals.DesignThis research was a retrospective observational study. The primary outcome was in-hospital mortality. SMRs were calculated for All Patient Refined—Diagnosis-Related Groups (APR-DRGs) that reflect 80% of the Flemish hospital network mortality. Hospital mortality was modelled using logistic regression. The metrics were communicated to the member hospitals using a custom-made R-Shiny web application showing results at the level of the hospital, patient groups and individual patients. Experiences with the metric and strategies for improvement were shared in chief medical officer meetings organised by the Flemish hospital network.Setting27 Belgian hospitals.Participants1 198 717 hospital admissions for registration years 2009–2016.ResultsPatient gender, age, comorbidity as well as admission source and type were important predictors of mortality. Altogether the SMR models had a C-statistic of 88%, indicating good discriminatory capability. Seven out of ten APR-DRGs with the highest percentage of hospitals statistically significantly deviating from the benchmark involved malignancy. The custom-built web application and the trusted environment of the Flemish hospital network created an interoperable strategy to get to work with SMR findings. Use of the web application increased over time, with peaks before and after key discussion meetings within the Flemish hospital network. A concomitant reduction in crude mortality for the selected APR-DRGs from 6.7% in 2009 to 5.9% in 2016 was observed.ConclusionsThis study reported on the phased approach for introducing SMR reporting to trigger quality improvement. Prerequisites for the successful use of quality metrics in hospital benchmarks are a collaborative approach based on trust among the participants and a reporting platform that allows stakeholders to interpret and analyse the results at multiple levels.
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Brown, Diane Storer, and Robert Wolosin. "Safety Culture Relationships with Hospital Nursing Sensitive Metrics." Journal For Healthcare Quality 35, no. 4 (July 2013): 61–74. http://dx.doi.org/10.1111/jhq.12016.
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Bogucki, Sandy. "Novel Metrics for Quality of Hospital Surge Capacity." Academic Emergency Medicine 19, no. 3 (March 2012): 336–37. http://dx.doi.org/10.1111/j.1553-2712.2012.01312.x.
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Pepe, Dana, Meghan Maloney, Vivian Leung, Adora Harizaj, David Banach, Louise Dembry, Louise Dembry, and Sonali D. Advani. "1162. An Evaluation of Metrics for Catheter-Associated Urinary Tract Infections (CAUTIs): A Statewide Comparison." Open Forum Infectious Diseases 6, Supplement_2 (October 2019): S415—S416. http://dx.doi.org/10.1093/ofid/ofz360.1025.
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Abstract Background The Standardized Infection Ratio (SIR) is a metric used to gauge catheter-associated urinary tract infection (CAUTI) prevention, both locally and nationally. The device utilization ratio (DUR) is a process metric that captures catheter harm. More recently, the cumulative attributable difference (CAD) was introduced, which identifies the number of excess infections that need to be prevented to reach the desired goal. Our objective was to evaluate these metrics across all acute care hospitals in Connecticut (CT) by facility size. Methods A CAUTI Targeted Assessment for Prevention (TAP) Report for acute care hospitals across CT was generated from 1/1/2018 to December 31/2018, using the National Healthcare Safety Network (NHSN) database. CAUTI events, SIR, DUR, and CAD were compared across all hospitals. The SIR goal of 0.75 was used to calculate the CAD. Hospitals were stratified into large ( >425 beds), medium (250 to 424 beds), and small ( <249 beds) based on the Healthcare Cost and Utilization Project NIS Description of Data Elements, Agency for Healthcare Research and Quality for urban hospitals in the northeast region. Results A comparison of CAUTI metrics for 29 acute care hospitals across CT is shown in Table 1. Median SIR and DUR were 0.97, 1.02, 0.77, and 22%, 14%, 14.5% for large, medium and small hospitals, respectively. Of the 20 small hospitals, SIR could not be calculated for 5 hospitals, while 2 hospitals had an SIR = 0, as they had no reported infections. Median CAD for large, medium and small hospitals was 6.17, 1.3 and 0.25, respectively. Of note, 40% of small hospitals (J – CC, as in Table 1) had a negative CAD. Interestingly, 5 of these 8 hospitals with a negative CAD had a DUR higher than 16%. Conclusion Based on CT hospital data, metrics like CAD and SIR may be more suitable for larger hospitals or hospitals with higher CAUTI events, whereas DUR may be a more useful metric for smaller hospitals or hospitals with rare events. Hospitals with high SIR and low DUR may represent a population with high-risk catheter use, poor catheter care or higher rates of urine culturing. On the other hand, hospitals with high DUR and low SIR may represent low-risk populations, better catheter care practices or lower rates of urine culturing. Ultimately, we need a combination of metrics to measure preventable catheter harm. Disclosures Louise Dembry, MD, MS, MBA, ReadyDock: Consultant, Stock options.
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Zinoviev, Radoslav, Harlan M. Krumholz, Richard Ciccarone, Rick Antle, and Howard P. Forman. "Multicentre methodological study to create a publicly available score of hospital financial standing in the USA." BMJ Open 11, no. 7 (July 2021): e046500. http://dx.doi.org/10.1136/bmjopen-2020-046500.
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ObjectivesTo create a straightforward scoring procedure based on widely available, inexpensive financial data that provides an assessment of the financial health of a hospital.DesignMethodological study.SettingMulticentre study.ParticipantsAll hospitals and health systems reporting the required financial metrics in the USA in 2017 were included for a total of 1075 participants.InterventionsWe examined a list of 232 hospital financial indicators and used existing models and financial literature to select 30 metrics that sufficiently describe hospital operations. In a set of hospital financial data from 2017, we used principal coordinate analysis to assess collinearity among variables and eliminated redundant variables. We isolated 10 unique variables, each assigned a weight equal to the share of its coefficient in a regression onto Moody’s Credit Rating, our predefined gold standard. The sum of weighted variables is a single composite score named the Yale Hospital Financial Score (YHFS).Primary outcome measuresAbility to reproduce both financial trends from a ‘gold-standard’ metric and known associations with non-fiscal data.ResultsThe validity of the YHFS was evaluated by: (1) cross-validating it with previously excluded data; (2) comparing it to existing models and (3) replicating known associations with non-fiscal data. Ten per cent of the initial dataset had been reserved for validation and was not used in creating the model; the YHFS predicts 96.7% of the variation in this reserved sample, demonstrating reproducibility. The YHFS predicts 90.5% and 88.8% of the variation in Moody’s and Standard and Poor’s bond ratings, respectively, supporting its validity. As expected, larger hospitals had higher YHFS scores whereas a greater share of Medicare discharges correlated with lower YHFS scores.ConclusionsWe created a reliable and publicly available composite score of hospital financial stability.
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Teichman, Jennifer R., Sumit Gupta, and Angela Punnett. "Development of Quality Metrics to Evaluate Pediatric Hematologic Oncology Care in the Outpatient Setting." Blood 124, no. 21 (December 6, 2014): 1305. http://dx.doi.org/10.1182/blood.v124.21.1305.1305.
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Abstract Introduction: Systems to quantify and incentivize quality of care (QoC) have been developed in multiple healthcare settings. In pediatric oncology, lists of QoC metrics or recommendations have been procured through consensus methodologies such as the Delphi process. To date, no QoC metrics have been developed for outpatient pediatric oncology. Objectives: The aim of this study was to develop a list of QoC metrics for the leukeumia-lymphoma (LL) clinic at the Hospital for Sick Children in Toronto, using a consensus process that could be adapted to other clinic settings. Methods: A modified Delphi process following the American Society of Clinical Oncology (ASCO) guidelines was used to generate consensus on a list of QoC metrics (Loblaw et al., 2012). A Medline-Ovid search was conducted for quality indicators, metrics and recommendations relevant to pediatric oncology. Results were screened for (a) system-level metrics that could be translated to a clinic level and (b) clinic-level recommendations that could be converted to measurable quantities. Additional metrics outside the literature search were considered. A provisional list was compiled and circulated electronically to local stakeholders, including medical and nursing staff (n=10). Stakeholders ranked each metric on a 5-point Likert scale based on importance and feasibility of measurement (round 1). Stakeholders provided feedback on the metrics and suggested additional metrics. Median, interquartile range and full ranges were calculated for each metric. A metric was considered to reach consensus if the percent of respondents ranking within two consecutive scores was ≥70%. Results and comments from round 1 were re-circulated to stakeholders in personalized reports. This allowed each stakeholder to compare his or her previous scores with overall scores for each metric. Stakeholders were asked to re-rank each metric (round 2). Results: The literature search yielded 2 relevant publications from which a provisional list of 27 metrics was generated. Metrics were grouped into 7 categories (Table 1). In round 1, 19/27 (70%) metrics reached consensus. Stakeholders’ comments resulted in 4 new metrics and edits to 8 original metrics. All metrics were included in round 2 for a total of 31. Twenty-four of 31 (77%) metrics reached consensus after round 2 (Table 1). Thirteen were chosen for the final list based on highest consensus scores, highest interquartile and full ranges, and minimizing redundancy. Conclusion: This study demonstrates the feasibility of using a modified Delphi process to generate QoC metrics for a pediatric hematology oncology clinic, and provides a model other clinics may employ for local use. The final metrics will be used to evaluate the quality of care in the LL clinic, and to identify areas for improvement in clinic function. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.
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Chen, Ming, Amie Goodin, Hong Xiao, Qiong Han, Driss Raissi, and Joshua Brown. "Hospitalization metrics associated with hospital-level variation in inferior vena cava filter utilization for patients with venous thromboembolism in the United States: Implications for quality of care." Vascular Medicine 23, no. 4 (May 20, 2018): 365–71. http://dx.doi.org/10.1177/1358863x18768685.
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Variation in the use of inferior vena cava filters (IVCFs) across hospitals has been observed, suggesting differences in quality of care. Hospitalization metrics associated with venous thromboembolism (VTE) patients have not been compared based on IVCF utilization rates using a national sample. We conducted a descriptive retrospective study using the Nationwide Readmissions Database (NRD) to delineate the variability of hospitalization metrics across the hospital quartiles of IVCF utilization for VTE patients. The NRD included all-payer administrative inpatient records drawn from 22 states. Adult (≥ 18 years) patients with VTE hospitalizations with or without IVCF were identified from January 1, 2013 through December 31, 2014 and hospitals were divided into quartiles based on the IVCF utilization rate as a proportion of VTE patients. Primary outcome measures were observed rates of in-hospital mortality, 30-day all-cause readmissions and VTE-related readmissions, cost, and length of stay. Patient case-mix characteristics and hospital-level factors by hospital quartiles of IVCF utilization rates, were compared. Overall, 12.29% of VTE patients had IVCF placement, with IVCF utilization ranging from 0% to 46.84%. The highest quartile had fewer pulmonary embolism patients relative to deep vein thrombosis patients, and older patient ages were present in higher quartiles. The highest quartile of hospitals placing IVCFs were more often private, for-profit, and non-teaching. Patient and hospital characteristics and hospitalization metrics varied by IVCF utilization rates, but hospitalization outcomes for non-IVCF patients varied most between quartiles. Future work investigating the implications of IVCF utilization rates as a measure of quality of care for VTE patients is needed.
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Montalbano, Amanda, Ricardo A. Quinonex, Matt Hall, Rustin Morse, Stacey L. Ishman, James W. Antoon, Jessica Gold, et al. "Achievable Benchmarks of Care for Pediatric Readmissions." Journal of Hospital Medicine 14, no. 9 (May 10, 2019): 534–50. http://dx.doi.org/10.12788/jhm.3201.
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BACKGROUND: Most inpatient care for children occurs outside tertiary children’s hospitals, yet these facilities often dictate quality metrics. Our objective was to calculate the mean readmission rates and the Achievable Benchmarks of Care (ABCs) for pediatric diagnoses by different hospital types: metropolitan teaching, metropolitan nonteaching, and nonmetropolitan hospitals. METHODS: We used a cross-sectional retrospective study of 30-day, all-cause, same-hospital readmission of patients less than 18 years old using the 2014 Healthcare Utilization Project National Readmission Database. For each hospital type, we calculated the mean readmission rates and corresponding ABCs for the 17 most common readmission diagnoses. We define outlier as any hospital whose readmission rate fell outside the 95% CI for an ABC within their hospital type. RESULTS: We analyzed 690,949 discharges at 525 metropolitan teaching hospitals (550,039 discharges), 552 metropolitan nonteaching hospitals (97,207 discharges), and 587 nonmetropolitan hospitals (43,703 discharges). Variation in readmission rates existed among hospital types; however, sickle cell disease (SCD) had the highest readmission rate and ABC across all hospital types: metropolitan teaching hospitals 15.7% (ABC 7.0%), metropolitan nonteaching 14.7% (ABC 2.6%), and nonmetropolitan 12.8% (ABC not calculated). For diagnoses in which ABCs were available, outliers were prominent in bipolar disorders, major depressive disorders, and SCD. CONCLUSIONS: ABCs based on hospital type may serve as a better metric to explain case-mix variation among different hospital types in pediatric inpatient care. The mean rates and ABCs for SCD and mental health disorders were much higher and with more outlier hospitals, which indicate high-value targets for quality improvement.
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Bayram, Jamil D., Shawki Zuabi, and Italo Subbarao. "Disaster Metrics: Quantitative Benchmarking of Hospital Surge Capacity in Trauma-Related Multiple Casualty Events." Disaster Medicine and Public Health Preparedness 5, no. 2 (June 2011): 117–24. http://dx.doi.org/10.1001/dmp.2010.19.
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ABSTRACTObjectives: Hospital surge capacity in multiple casualty events (MCE) is the core of hospital medical response, and an integral part of the total medical capacity of the community affected. To date, however, there has been no consensus regarding the definition or quantification of hospital surge capacity. The first objective of this study was to quantitatively benchmark the various components of hospital surge capacity pertaining to the care of critically and moderately injured patients in trauma-related MCE. The second objective was to illustrate the applications of those quantitative parameters in local, regional, national, and international disaster planning; in the distribution of patients to various hospitals by prehospital medical services; and in the decision-making process for ambulance diversion.Methods: A 2-step approach was adopted in the methodology of this study. First, an extensive literature search was performed, followed by mathematical modeling. Quantitative studies on hospital surge capacity for trauma injuries were used as the framework for our model. The North Atlantic Treaty Organization triage categories (T1-T4) were used in the modeling process for simplicity purposes.Results: Hospital Acute Care Surge Capacity (HACSC) was defined as the maximum number of critical (T1) and moderate (T2) casualties a hospital can adequately care for per hour, after recruiting all possible additional medical assets. HACSC was modeled to be equal to the number of emergency department beds (#EDB), divided by the emergency department time (EDT); HACSC = #EDB/EDT. In trauma-related MCE, the EDT was quantitatively benchmarked to be 2.5 (hours). Because most of the critical and moderate casualties arrive at hospitals within a 6-hour period requiring admission (by definition), the hospital bed surge capacity must match the HACSC at 6 hours to ensure coordinated care, and it was mathematically benchmarked to be 18% of the staffed hospital bed capacity.Conclusions: Defining and quantitatively benchmarking the different components of hospital surge capacity is vital to hospital preparedness in MCE. Prospective studies of our mathematical model are needed to verify its applicability, generalizability, and validity.(Disaster Med Public Health Preparedness. 2011;5:117–124)
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Graham, Laura A., Hillary J. Mull, Todd H. Wagner, Melanie S. Morris, Amy K. Rosen, Joshua S. Richman, Jeffery Whittle, et al. "Comparison of a Potential Hospital Quality Metric With Existing Metrics for Surgical Quality–Associated Readmission." JAMA Network Open 2, no. 4 (April 19, 2019): e191313. http://dx.doi.org/10.1001/jamanetworkopen.2019.1313.
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Nair, Rajeshwari, Yubo Gao, Mary Vaughan-Sarrazin, Eli N. Perencevich, Saket Girotra, and Ambarish Pandey. "828. Evaluation of Home Time as a Patient-Centered Metric for Pneumonia Hospitalizations: A Retrospective Cohort Study of Medicare Fee-For-Service Beneficiaries." Open Forum Infectious Diseases 7, Supplement_1 (October 1, 2020): S455—S456. http://dx.doi.org/10.1093/ofid/ofaa439.1017.
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Abstract Background The Centers for Medicare & Medicaid Services (CMS) uses hospital readmission to incentivize hospital care delivery for acute conditions including pneumonia. However, current CMS performance metrics do not account for the competing risk of mortality in the post-discharge period or during the hospital stay. Our objective was to assess home time within 30 days after discharge among pneumonia hospitalizations, as a patient-centered metric. Methods A retrospective observational study was conducted in a cohort of Medicare fee-for-service beneficiaries admitted between 01/01/2015 and 11/30/2017. Home time was the number of days spent alive, out of an acute care setting, skilled nursing facility, or a rehabilitation facility within 30 days of discharge. If a patient spends any part of a day in a care facility or died after discharge, then that day was not included in the calculation for home time. Hospital-level rates of risk-adjusted home time were calculated using multilevel regression models. We compared hospital performance on 30-day risk-standardized home time with its performance on 30-day risk standardized readmission rate (RSRR) and mortality rate (RSMR). Characteristics of hospitals with high and low risk-adjusted home-time were compared. Results Among 1.7 million pneumonia admissions admitted to 3,116 hospitals, the median 30-day risk-standardized home time was 20.5 days (interquartile range: 18.9-21.9 days). Hospital-level characteristics such as case volume, bed size, for-profit ownership, rural location of hospital, teaching status, and participation in the bundle payment program were significantly associated with home-time. RSRR (rho: -0.233, p< 0.0001) and RSMR (rho: -0.223, p< 0.0001) had weak, inverse correlations with home time. Using the home time metric, 35.5% of hospitals were reclassified as high performers compared with their average or poor performance on the RSRR or RSMR metric. Conclusion Home time is a novel, patient-centered, hospital-level metric that can be easily calculated using claims data, accounts for differences in post-discharge mortality and can be intuitively interpreted. Utilization of this metric could potentially have policy implications in assessing hospital performance on delivery of healthcare to pneumonia patients. Disclosures Rajeshwari Nair, PhD, Merck and Company, Inc. (Research Grant or Support)
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Kumar, Bhanumathy. "Patient Safety and Quality Metrics in Pediatric Hospital Medicine." Pediatric Clinics of North America 63, no. 2 (April 2016): 283–91. http://dx.doi.org/10.1016/j.pcl.2015.11.002.
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Baghdadi, Jonathan, and Anthony D. Harris. "Working Toward Better Metrics for Nonventilator Hospital-Acquired Pneumonia." JAMA Network Open 2, no. 10 (October 18, 2019): e1913662. http://dx.doi.org/10.1001/jamanetworkopen.2019.13662.
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White, Andrew A., Thomas McIlraith, Anton M. Chivu, Rachel Cyrus, Christopher Cockerham, Hardik Vora, and Pactrick Vulgamore. "Collaboration, Not Calculation: A Qualitative Study of How Hospital Executives Value Hospital Medicine Groups." Journal of Hospital Medicine 14, no. 11 (July 24, 2019): 662–67. http://dx.doi.org/10.12788/jhm.3249.
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receive financial support from hospitals. Determining a fair amount of financial support requires negotiation between HMG and hospital leaders. As the hospital medicine care model evolves, hospital leaders may regularly challenge HMGs to demonstrate the financial value of activities that do not directly generate revenue. OBJECTIVE: To describe current attitudes and beliefs of hospital executives regarding the value of contributions made by HMGs. DESIGN: Thematic content analysis of key informant interviews. PARTICIPANTS: Twenty-four healthcare institutional leaders, including hospital presidents, chief medical officers, chief executive officers, and chief financial officers. Participants comprised a diverse sample from all regions in the United States, including rural, suburban, and urban locations, and academic and nonacademic institutions. RESULTS: Executives highly valued hospitalist groups that demonstrate alignment with hospital priorities, and often used this concept to summarize the HMG’s success across several value domains. Most executives evaluated only a few key HMG metrics, but almost no executives reported calculating the HMG return on investment by summing pertinent quantitative contributions. Respondents described an evolving concept of hospitalist value and believed that HMGs generate substantial value that is difficult to measure financially. CONCLUSIONS: Hospital executives appear to make financial support decisions based on a small number of basic financial or care quality metrics combined with a subjective assessment of the HMG’s broader alignment with hospital priorities. HMG leaders should focus on building relationships that facilitate dialog about alignment with hospital needs.
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Al-Hasan, Majdi N., Hana Rac Winders, P. Brandon Bookstaver, and Julie Ann Justo. "Direct Measurement of Performance: A New Era in Antimicrobial Stewardship." Antibiotics 8, no. 3 (August 24, 2019): 127. http://dx.doi.org/10.3390/antibiotics8030127.
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For decades, the performance of antimicrobial stewardship programs (ASPs) has been measured by incidence rates of hospital-onset Clostridioides difficile and other infections due to multidrug-resistant bacteria. However, these represent indirect and nonspecific ASP metrics. They are often confounded by factors beyond an ASP’s control, such as changes in diagnostic testing methods or algorithms and the potential of patient-to-patient transmission. Whereas these metrics remain useful for global assessment of healthcare systems, antimicrobial use represents a direct metric that separates the performance of an ASP from other safety and quality teams within an institution. The evolution of electronic medical records and healthcare informatics has made measurements of antimicrobial use a reality. The US Centers for Disease Control and Prevention’s initiative for reporting antimicrobial use and standardized antimicrobial administration ratio in hospitals is highly welcomed. Ultimately, ASPs should be evaluated based on what they do best and what they can control, that is, antimicrobial use within their own institution. This narrative review critically appraises existing stewardship metrics and advocates for adopting antimicrobial use as the primary performance measure. It proposes novel formulas to adjust antimicrobial use based on quality of care and microbiological burden at each institution to allow for meaningful inter-network and inter-facility comparisons.
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Juran, Sabrina, P. Niclas Broer, Stefanie J. Klug, Rachel C. Snow, Emelda A. Okiro, Paul O. Ouma, Robert W. Snow, Andrew J. Tatem, John G. Meara, and Victor A. Alegana. "Geospatial mapping of access to timely essential surgery in sub-Saharan Africa." BMJ Global Health 3, no. 4 (August 2018): e000875. http://dx.doi.org/10.1136/bmjgh-2018-000875.
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IntroductionDespite an estimated one-third of the global burden of disease being surgical, only limited estimates of accessibility to surgical treatment in sub-Saharan Africa exist and these remain spatially undefined. Geographical metrics of access to major hospitals were estimated based on travel time. Estimates were then used to assess need for surgery at country level.MethodsMajor district and regional hospitals were assumed to have capability to perform bellwether procedures. Geographical locations of hospitals in relation to the population in the 47 sub-Saharan countries were combined with spatial ancillary data on roads, elevation, land use or land cover to estimate travel-time metrics of 30 min, 1 hour and 2 hours. Hospital catchment was defined as population residing in areas less than 2 hours of travel time to the next major hospital. Travel-time metrics were combined with fine-scale population maps to define burden of surgery at hospital catchment level.ResultsOverall, the majority of the population (92.5%) in sub-Saharan Africa reside in areas within 2 hours of a major hospital catchment defined based on spatially defined travel times. The burden of surgery in all-age population was 257.8 million to 294.7 million people and was highest in high-population density countries and lowest in sparsely populated or smaller countries. The estimated burden in children <15 years was 115.3 million to 131.8 million and had similar spatial distribution to the all-age pattern.ConclusionThe study provides an assessment of accessibility and burden of surgical disease in sub-Saharan Africa. Yet given the optimistic assumption of adequare surgical capability of major hospitals, the true burden of surgical disease is expected to be much greater. In-depth health facility assessments are needed to define infrastructure, personnel and medicine supply for delivering timely and safe affordable surgery to further inform the analysis.
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Stevens, Philip Andrew, Lucy Stokes, and Mary O'Mahony. "Metrics, Targets and Performance." National Institute Economic Review 197 (July 1, 2006): 80–92. http://dx.doi.org/10.1177/0027950106070037.
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The setting and use of targets in the public sector has generated a growing amount of interest in the UK. This has occurred at a time when more analysts and policymakers are grasping the nettle of measuring performance in and of the public sector. We outline a typology of performance indicators and a set of desiderata. We compare the outcome of a performance management system — star ratings for acute hospital trusts in England — with a productivity measure analogous to those used in the analysis of the private sector. We find that the two are almost entirely unrelated. Although this may be the case for entirely proper reasons, it does raise questions as to the appropriateness of such indicators of performance, particularly over the long term.
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Patil, Vishal. "Smart Hospital Management System." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 20, 2021): 1878–81. http://dx.doi.org/10.22214/ijraset.2021.35440.
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Information and Communication Technologies (ICTs) are commonly using in healthcare organizations worldwide. There are different kinds of healthcare applications developed in android Smartphone’s which help patients and their caregivers to reduce time and cost efficiency. Hospitals are the largest and most complex organizations where health care is provided. Safe and effective patient care services in hospitals depend on the efficient decisions made by hospital executives. The main task of hospital executives is to ensure the hospital can provide high quality patient care and services. This Android application used for displaying hospital performance metrics on a daily basis. This application allows hospital executives to review and monitor hospital operational data with ease of access and in a portable manner. Thus, reducing the effort of the hospital executives to perform their tasks. In this research work, an application is developed that locates the nearest hospital. The System is designed for Any Hospital to replace their existing manual, paper-based system. The new system is to control the following information; List of Hospitals, bed availability, Book Appointment, List of Doctors, Facilities and Book Ambulance. With the help of this application, a patient can find the nearest hospital according to specialized consultant availability.
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Cassel, Brian, Nevena Skoro, Kathleen Kerr, Lisa Shickle, Patrick J. Coyne, and Egidio Del Fabbro. "Retrospective assessment of quality of cancer care in last 6 months of life." Journal of Clinical Oncology 30, no. 34_suppl (December 1, 2012): 234. http://dx.doi.org/10.1200/jco.2012.30.34_suppl.234.
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234 Background: National organizations such as the Centers for Medicare and Medicaid Services (CMS) and the National Quality Forum (NQF) have developed metrics that assess the quality of cancer care. These metrics include consensus standards by the NQF for management of symptoms and end-of-life-care. Cancer centers need feasible methods for self-evaluating their performance on such metrics. Methods: Claims for our cancer patients were matched to Social Security Death Index data to determine date of death.3,128 adult cancer patients died between January 2009 and July 2011 and had at least 1 contact with our center in their last six month of life. All inpatient and outpatient claims data generated in the last six months of life at our hospital were analyzed. Results: 32% of patients had an admission in their last 30 days of life, with 15% dying in the hospital. 19% had at least one 30-day readmission in their last six months of life. 6.7% had chemotherapy in the 2 weeks prior to death, and 11.4% in the last month. 27.5% had some contact with the specialist palliative care (SPC) team. Solid tumor patients with SPC earlier than 1 month until death had fewer in-hospital deaths (15.6%) versus those with later or no SPC (19.5%), p=.041. There was no SPC difference for 30-day mortality, or 14- or 30-day chemotherapy metrics. Conclusions: Hospitals can self-evaluate their own performance on NQF endorsed measures, and CMS outcome measures. These data provide additional impetus for earlier integration of specialist palliative care teams. SPC in the last 1-3 weeks of life did not improve most utilization metrics.[Table: see text]
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Furuya, E. Yoko, Elaine Larson, Timothy Landers, Haomiao Jia, Barbara Ross, and Maryam Behta. "Challenges of Applying the SHEA/HICPAC Metrics for Multidrug-Resistant Organisms to a Real-World Setting." Infection Control & Hospital Epidemiology 32, no. 4 (April 2011): 323–32. http://dx.doi.org/10.1086/658939.
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Objective.To test in a real-world setting the recommendations for measuring infection with multidrug-resistant organisms (MDRO) from the Society for Healthcare Epidemiology of America (SHEA) and the Centers for Disease Control and Prevention's Healthcare Infection Control Practices Advisory Committee (HICPAC).Methods.Using data from 3 hospital settings within a healthcare network, we applied the SHEA/HICPAC recommendations to measure methicillin-resistant Staphylococcus aureus (MRSA) infection and colonization. Data were obtained from the hospitals' electronic surveillance system and were supplemented by manual medical record review as necessary. Additionally, we tested (1) different definitions for nosocomial incidence, (2) the effect of excluding patients not at risk from the denominator for hospital-onset incidence, and (3) the appropriate time period to use when including or excluding patients with a prior history of MRSA infection or colonization from nosocomial rates. Negative binomial regression models were used to test for differences between rate definitions. A rating scale was created for each metric, assessing the extent to which manual or electronic data elements were required.Results.There was no statistically significant difference between using 72 hours or 3 calendar days as the cutoff to define hospital-onset incidence. Excluding patients not at risk from the denominator when calculating hospital-onset incidence led to statistically significant increases in rates. When excluding patients with a prior history of MRSA infection or colonization from nosocomial incidence rates, rates were similar regardless of whether we looked at 1, 2, or 3 years' worth of prior data.Conclusions.The SHEA/HICPAC MDRO metrics are useful but can be challenging to implement. We include in our description of the data sources and processes required to calculate these metrics information that may simplify the process for institutions.
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Pandey, Ambarish, Neil Keshvani, Mary S. Vaughan-Sarrazin, Yubo Gao, and Saket Girotra. "Evaluation of Risk-Adjusted Home Time After Acute Myocardial Infarction as a Novel Hospital-Level Performance Metric for Medicare Beneficiaries." Circulation 142, no. 1 (July 7, 2020): 29–39. http://dx.doi.org/10.1161/circulationaha.119.044765.
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Background: The utility of 30-day risk-standardized readmission rate (RSRR) as a hospital performance metric has been a matter of debate. Home time is a patient-centered outcome measure that accounts for rehospitalization, mortality, and postdischarge care. We aim to characterize risk-adjusted 30-day home time in patients with acute myocardial infarction (AMI) as a hospital-level performance metric and to evaluate associations with 30-day RSRR, 30-day risk-standardized mortality rate (RSMR), and 1-year RSMR. Methods: The study included 984 612 patients with AMI hospitalization across 2379 hospitals between 2009 and 2015 derived from 100% Medicare claims data. Home time was defined as the number of days alive and spent outside of a hospital, skilled nursing facility, or intermediate-/long-term acute care facility 30 days after discharge. Correlations between hospital-level risk-adjusted 30-day home time and 30-day RSRR, 30-day RSMR, and 1-year RSMR were estimated with the Pearson correlation. Reclassification in hospital performance using 30-day home time versus 30-day RSRR and 30-day RSMR was also evaluated. Results: Median hospital-level risk-adjusted 30-day home time was 24.0 days (range, 15.3–29.0 days). Hospitals with higher home time were more commonly academic centers, had available cardiac surgery and rehabilitation services, and had higher AMI volume and percutaneous coronary intervention use during the AMI hospitalization. Of the mean 30-day home time days lost, 58% were to intermediate-/long-term care or skilled nursing facility stays (4.7 days), 30% to death (2.5 days), and 12% to readmission (1.0 days). Hospital-level risk-adjusted 30-day home time was inversely correlated with 30-day RSMR ( r =−0.22, P <0.0001) and 30-day RSRR (r =−0.25, P <0.0001). Patients admitted to hospitals with higher risk-adjusted 30-day home time had lower 30-day readmission (quartile 1 versus 4, 21% versus 17%), 30-day mortality rate (5% versus 3%), and 1-year mortality rate (18% versus 12%). Furthermore, 30-day home time reclassified hospital performance status in ≈30% of hospitals versus 30-day RSRR and 30-day RSMR. Conclusions: Thirty-day home time for patients with AMI can be assessed as a hospital-level performance metric with the use of Medicare claims data. It varies across hospitals, is associated with postdischarge readmission and mortality outcomes, and meaningfully reclassifies hospital performance compared with the 30-day RSRR and 30-day RSMR metrics.
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Mohapatra, Sanjay. "Using Biometrics Devices for Improving Automation in Hospital Management System." International Journal of Healthcare Delivery Reform Initiatives 3, no. 2 (April 2011): 40–48. http://dx.doi.org/10.4018/jhdri.2011040104.
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This study discusses the best practices of a hospital in a semi-urban area in India and how the hospital management system has gained extended use through the usage of bio metrics device. Using the information system, all the stakeholders have benefitted and the monetary benefits have justified IT investment. Integration of information systems with patient care activities has reduced the patient care cost, making it a sustainable investment, making this a benefit to all hospitals.
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Kim, Kidong, Suyeon Jeong, Kyogu Lee, Hyeoun-Ae Park, Yul Min, Joo Lee, Yekyung Kim, Sooyoung Yoo, Gippeum Doh, and Soyeon Ahn. "Metrics for Electronic-Nursing-Record-Based Narratives: cross-sectional analysis." Applied Clinical Informatics 07, no. 04 (October 2016): 1107–19. http://dx.doi.org/10.4338/aci-2016-07-ra-0119.
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Summary ObjectivesWe aimed to determine the characteristics of quantitative metrics for nursing narratives documented in electronic nursing records and their association with hospital admission traits and diagnoses in a large data set not limited to specific patient events or hypotheses. MethodsWe collected 135,406,873 electronic, structured coded nursing narratives from 231,494 hospital admissions of patients discharged between 2008 and 2012 at a tertiary teaching institution that routinely uses an electronic health records system. The standardized number of nursing narratives (i.e., the total number of nursing narratives divided by the length of the hospital stay) was suggested to integrate the frequency and quantity of nursing documentation. ResultsThe standardized number of nursing narratives was higher for patients aged ≥ 70 years (median = 30.2 narratives/day, interquartile range [IQR] = 24.0–39.4 narratives/day), long (≥ 8 days) hospital stays (median = 34.6 narratives/day, IQR = 27.2–43.5 narratives/day), and hospital deaths (median = 59.1 narratives/day, IQR = 47.0–74.8 narratives/day). The standardized number of narratives was higher in “pregnancy, childbirth, and puerperium” (median = 46.5, IQR = 39.0–54.7) and “diseases of the circulatory system” admissions (median = 35.7, IQR = 29.0–43.4). ConclusionsDiverse hospital admissions can be consistently described with nursing-documentderived metrics for similar hospital admissions and diagnoses. Some areas of hospital admissions may have consistently increasing volumes of nursing documentation across years. Usability of electronic nursing document metrics for evaluating healthcare requires multiple aspects of hospital admissions to be considered. Citation: Kim K, Jeong S, Lee K, Park H-A, Min YH, Lee JY, Kim Y, Yoo S, Doh G, Ahn S. Metrics for electronicnursing-record-based narratives: cross-sectional analysis.
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Missios, Symeon, and Kimon Bekelis. "How well do subjective Hospital Compare metrics reflect objective outcomes in spine surgery?" Journal of Neurosurgery: Spine 25, no. 2 (August 2016): 264–70. http://dx.doi.org/10.3171/2016.1.spine151155.
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OBJECTIVE The accuracy of public reporting in health care is an issue of debate. The authors investigated the association of patient satisfaction measures from a public reporting platform with objective outcomes for patients undergoing spine surgery. METHODS The authors performed a cohort study involving patients undergoing elective spine surgery from 2009 to 2013 who were registered in the New York Statewide Planning and Research Cooperative System database. This cohort was merged with publicly available data from the Centers for Medicare and Medicaid Services (CMS) Hospital Compare website. A mixed-effects regression analysis, controlling for clustering at the hospital level, was used to investigate the association of patient satisfaction metrics with outcomes. RESULTS During the study period, 160,235 patients underwent spine surgery. Using a mixed-effects multivariable regression analysis, the authors demonstrated that undergoing elective spine surgery in hospitals with a higher percentage of patient-assigned high satisfaction scores was not associated with a decreased rate of discharge to rehabilitation (OR 0.77, 95% CI 0.57–1.06), mortality (OR 0.96, 95% CI 0.90–1.01), or hospitalization charges (β 0.04, 95% CI −0.16 to 0.23). However, it was associated with decreased length of stay (LOS; β −0.19, 95% CI −0.33 to −0.05). Similar associations were identified for hospitals with a higher percentage of patients who claimed they would recommend these institutions to others. CONCLUSIONS Merging a comprehensive all-payer cohort of spine surgery patients in New York state with data from the CMS Hospital Compare website, the authors were not able to demonstrate an association of improved performance in patient satisfaction measures with decreased mortality, rate of discharge to rehabilitation, and hospitalization charges. Increased patient satisfaction was associated with decreased LOS.
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Goto, Michihiko, Rajeshwari Nair, Bruce Alexander, Brice Beck, Christopher Richards, Eli N. Perencevich, and Daniel J. Livorsi. "2894. Metrics of Antimicrobial Use Within Inpatient Settings: Impacts of Statistical Methods and Case-Mix Adjustments." Open Forum Infectious Diseases 6, Supplement_2 (October 2019): S81. http://dx.doi.org/10.1093/ofid/ofz359.172.
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Abstract Background The necessary data elements and optimal statistical methods for benchmarking hospital-level antimicrobial use are still being debated. We aimed to describe the relative influence of case-mix adjustment and different statistical methods when ranking hospitals on antimicrobial use (AU) within inpatient settings. Methods Using administrative data from the Veterans Health Administration (VHA) system in October 2016, we calculated total antimicrobial days of therapy (DOT) and days present according to the National Healthcare Safety Network (NHSN) protocol. Patient-level demographics, comorbidities, and recent procedures were used for case-mix adjustments. We compared hospital rankings across 4 different methods: (A) crude antimicrobial DOT per 1,000 days present, aggregated at the hospital-level; (B) observed/expected (O/E) AU ratio with risk adjustment for ward-level variables (analogous to NHSN’s Standardized Antimicrobial Administration Ratio); (C) O/E AU ratio with risk adjustment for ward-/patient-level variables; (D) predicted/expected (P/E) AU ratio with risk adjustment for ward-/patient-level variables, based on a multilevel model accounting for clustering effects at hospital- and ward-levels. Results The cohort included 165,949 DOTs and 318,321 days present at 122 acute care hospitals within VHA. Crude DOTs per 1,000 days present ranged from 153.6 to 900.8 (Figure A), and ward-level risk adjustment only modestly changed rankings (Figure B). When adjusted for ward- and patient-level variables (including demographics, 14 comorbidities and 22 procedures), rankings changed substantially (Figure C). Risk-adjustment by a multilevel model changed rankings even further, while shrinking variabilities (Figure D). Ten hospitals in the lowest and 11 hospitals in the highest quartiles by O/E risk adjustment for only ward-level variables were classified to different quartiles on P/E risk adjustment. Conclusion We observed that the selection of variables and statistical methods for case-mix adjustment had a substantial impact on hospital rankings for antimicrobial use within inpatient settings. Careful consideration of methodologies is warranted when providing benchmarking metrics for hospitals. Disclosures All Authors: No reported Disclosures.
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So, Conan, Daniel Lage, Chloe Slocum, and Jeffrey C. Schneider. "Predictive Role of Functional Metrics in the Acute Hospital Setting." Archives of Physical Medicine and Rehabilitation 99, no. 10 (October 2018): e112. http://dx.doi.org/10.1016/j.apmr.2018.07.398.
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Harris, Jack A., Jessica Y. Norrlinger, Thomas B. Dodson, and Yisi D. Ji. "Hospital Quality and Safety Metrics in Oral and Maxillofacial Surgery." Journal of Oral and Maxillofacial Surgery 79, no. 8 (August 2021): 1593–94. http://dx.doi.org/10.1016/j.joms.2021.04.006.
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Barnes, Beau Grant, Steve Buchheit, and Linda M. Parsons. "Threshold-Based Medicare Incentives and Aggressive Patient Reporting in U.S. Hospitals." Accounting and the Public Interest 17, no. 1 (May 1, 2017): 84–106. http://dx.doi.org/10.2308/apin-51793.
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ABSTRACT This paper examines regulatory reporting by large urban hospitals in response to financial incentives designed to increase the provision of health care services to certain underserved individuals. We find strong evidence that hospitals have used aggressive reporting to extract substantial unwarranted funding from Medicare's Disproportionate Share Hospital (DSH) program—a program designed to ease the burden on hospitals treating low income populations. Our evidence suggests that the accuracy of basic performance metrics (in this case, the number of low income patients served) can be unreliable when threshold-driven incentives based on such metrics benefit the reporting party (in this case, large urban hospitals). Similarities between the DSH program and current payment reforms, along with policy implications, are discussed. JEL Classifications: H51; I18.
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McRae, A., G. Innes, M. Schull, E. Lang, E. Grafstein, B. Rowe, and R. Rosychuk. "LO10: Associations between ED crowding metrics and 72h-hour ED re-visits: Which crowding metrics are most highly associated with patient-oriented adverse outcomes?" CJEM 21, S1 (May 2019): S10. http://dx.doi.org/10.1017/cem.2019.53.
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Introduction: Emergency Department (ED) crowding is a pervasive problem and is associated with adverse patient outcomes. Yet, there are no widely accepted, universal ED crowding metrics. The objective of this study is to identify ED crowding metrics with the strongest association to the risk of ED revisits within 72 hours, which is a patient-oriented adverse outcome. Methods: Crowding metrics, patient characteristics and outcomes were obtained from administrative data for all ED encounters from 2011-2014 for three adult EDs in Calgary, AB. The data were randomly divided into three partitions for cross-validation, and further divided by CTAS category 1, 2/3 and 4/5. Twenty unique ED crowding metrics were calculated and assigned to each patient seen on each calendar day or shift, to standardize the exposure. Logistic regression models were fitted with 72h ED revisit as the dependent variable, and an individual crowding metric along with a common list of confounders as independent variables. Adjusted odds ratios (OR) for the 72h return visits were obtained for each crowding metric. The strength of associations between 72h revisits and crowding metrics were compared using Akaike's Information Criterion and Akaike weights. Results: This analysis is based on 1,149,939 ED encounters. Across all CTAS groups, INPUT metrics (ED census, ED occupancy, waiting time, EMS offload delay, LWBS%) were only weakly associated with the risk of 72h re-visit. Among THROUGHPUT metrics, ED Length of Stay and MD Care Time had similar adjusted ORs for 72h ED re-visit (range 0.99-1.15). Akaike weights ranging from 0.3/1.00 to 0.4/1.00 indicate that both THROUGHPUT metrics are reasonable predictors of 72h ED re-visits. All OUTPUT metrics (boarding time, # of boarded patients, % of beds occupied by boarded patients, hospital occupancy) had statistically significant ORs for 72h ED re-visits. The median boarding time had the highest adjusted OR for 72h ED re-visit (adjusted OR 1.40, 95% CI 1.33-1.47) and highest Akaike weight (0.97/1.00) compared to all other OUTPUT metrics, indicating that median boarding time had the strongest association with 72h re-visits. Conclusion: ED THROUGHPUT and OUTPUT metrics had consistent associations with 72h ED re-visits, while INPUT metrics had little to no association with 72h re-visits. Median boarding time is the strongest predictor of 72h re-visits, indicating that this may be the most meaningful measure of ED crowding.
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Graham, Judy M., Maria E. Sabeta, Joseph T. Cooke, Elaine R. Berg, and Wayne M. Osten. "A System's Approach to Improve Organ Donation." Progress in Transplantation 19, no. 3 (September 2009): 216–20. http://dx.doi.org/10.1177/152692480901900304.
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Using lessons learned from the US Department of Health and Human Services National Donation Breakthrough Collaborative, New York-Presbyterian Healthcare System (NYPHS) partnered with 5 donor service areas covering its member hospitals to improve donation across the system. By integrating established communication networks with the “spread” techniques of the Breakthrough Collaborative, the NYPHS identified hospital champions and best practices and established standardized outcome metrics. The improvements that resulted were a sustained increase of 40.23% in consent rate and an initial 41.7% increase in conversion rate during the first 6 months, although that conversion rate was not sustainable. During the 8 measured periods, 21 hospitals met or exceeded the 75% conversion rate during 1 or more quarters. NYPHS was able to spread these successes and outcome metrics through its established communication networks of quarterly report cards, regular senior leader meetings, and real-time access to a secure member-only Web site, thus keeping organ and tissue donation at the forefront of hospital leaders' priorities.
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Hartung, Nicole L. W., Rhonda M. Henschel, Katie B. Smith, and Dean H. Gesme. "Creating Virtual Integration and Improved Oncology Care Quality Through a Co-Management Services Agreement." Journal of Oncology Practice 12, no. 9 (September 2016): e839-e847. http://dx.doi.org/10.1200/jop.2015.010645.
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Purpose: Implementation of a co-management services agreement (Co-MSA) creates agreed-upon cancer care delivery quality metrics, a forum for discussion of service line oversight, and virtually integrated care without institutional employment of oncologists. The goal of this project was to demonstrate that a Co-MSA improved predefined quality metrics and provided enhanced communications between a health system’s oncology service line and a group of independent oncologists. Methods: Iterative planning discussions were scheduled biweekly over an 18-month period. Contractual, quality, and clinical data with benchmarking were considered in the creation of the Co-MSA. Review of the first year’s implementation occurred through examination of the metric achievements and qualitative themes that arose through committee meetings, clinical implementation processes, and cross-organizational discussions. Results: Metrics designed for the Co-MSA included improved adherence to the breast cancer, colon cancer, and non–small-cell lung cancer level I pathways; improvement of the medical oncology physician communication component of the hospital system’s Hospital Consumer Assessment of Healthcare Providers and Systems survey scores; and increased delivery of survivorship care plans to appropriate patients. Nonquantifiable themes from the first year of implementation included the need for technology to collect data, both organizations needing a wider understanding of quality improvement techniques, and a need for greater executive leadership involvement. Conclusion: In its first year, the Co-MSA resulted in improvement of the delivery of survivorship care plans and adherence to value pathways powered by the National Comprehensive Cancer Network. Improvement of Hospital Consumer Assessment of Healthcare Providers and Systems scores did not occur.
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Nuño, Miriam, Christine Carico, Debraj Mukherjee, Diana Ly, Alicia Ortega, Keith L. Black, and Chirag G. Patil. "Association between in-hospital adverse events and mortality for patients with brain tumors." Journal of Neurosurgery 123, no. 5 (November 2015): 1247–55. http://dx.doi.org/10.3171/2014.10.jns141516.
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OBJECT The Agency for Healthcare Research and Quality patient safety indicators (PSIs) and the Centers for Medicare and Medicaid Services hospital-acquired conditions (HACs) are administrative data-based metrics. The use of these outcomes as standard performance measures has been discussed in previous studies. With the objective of determining the applicability of these events as performance metrics among patients undergoing brain tumor surgery, this study had 2 aims: 1) to evaluate the association between PSIs, HACs, and in-hospital mortality rates; and 2) to determine a correlation between hospital volume, PSIs, and HACs. METHODS Patients with brain tumors treated between 1998 and 2009 were captured in the Nationwide Inpatient Sample database. Hospitals were categorized into groups according to surgical volume. Associations between PSIs, HACs, and in-hospital mortality rates were studied. Factors associated with a PSI, HAC, and mortality were evaluated in a multivariate setting. RESULTS A total of 444,751 patients with brain tumors underwent surgery in 1311 hospitals nationwide. Of these, 7.4% of patients experienced a PSI, 0.4% an HAC, and 1.9% died during their hospitalization. The occurrence of a PSI was strongly associated with mortality. Patients were 7.6 times more likely to die (adjusted odds ratio [aOR] 7.6, CI 6.7–8.7) with the occurrence of a PSI in a multivariate analysis. Moderate to strong associations were found between HACs, PSIs, and hospital volume. Patients treated at the highest-volume hospitals compared with the lowest-volume ones had reduced odds of a PSI (aOR 0.9, CI 0.8–1.0) and HAC (aOR 0.5, CI 0.5–0.08). CONCLUSIONS Patient safety-related adverse events were strongly associated with in-hospital mortality. Moderate to strong correlations were found between PSIs, HACs, and hospital procedural volume. Patients treated at the highest-volume hospitals had consistently lower rates of mortality, PSIs, and HACs compared with those treated at the lowest-volume facilities.
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Tsai, Thomas C., Ashish K. Jha, Atul A. Gawande, Robert S. Huckman, Nicholas Bloom, and Raffaella Sadun. "Hospital Board And Management Practices Are Strongly Related To Hospital Performance On Clinical Quality Metrics." Health Affairs 34, no. 8 (August 2015): 1304–11. http://dx.doi.org/10.1377/hlthaff.2014.1282.
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van Woerden, Hugo C., Neil J. Walker, Vasiliki Kiparoglou, and Yaling Yang. "Demonstration of a Fair Level of Agreement Between Escalation Scores Reported by Hospital Managers and Analysis of Stress-Related Hospital Metrics." Health Services Research and Managerial Epidemiology 6 (January 1, 2019): 233339281881929. http://dx.doi.org/10.1177/2333392818819291.
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Background: The National Health System in Wales has developed a novel national electronic dashboard which reports a daily “escalation score,” reflecting management’s opinion of the pressure each hospital is facing, primarily due to unscheduled care. The aim of this study was to examine the possibility of replacing human scores with a quantitative model, based on the relationship between reported escalation scores and selected hospital metrics. Methods: Generalized linear mixed models were used to model the association between hospital metrics and escalation scores between October one year and October the next year utilizing hospital bed occupancy rate, ambulance hours lost waiting outside emergency departments, number of “boarded out” patients in the hospital, and the daily ratio of admissions to discharges in the hospital. These models were tested against a subsequent period (December unto May the following year), using three models: “general,” “hospital-specific,” and “group-specific.” The model generated by the initial time frame was tested against data from the subsequent time frame using weighted κ. Results: Across 16 hospitals, using 3343 escalation scores, the rates of agreement and weighted κ were: general model (48.8%; 0.16), hospital-specific model (45.0%; 0.25), and group-specific model (43.1%; 0.25). A 17th small hospital was excluded due to missing data. Conclusions: This is novel research as no similar studies were identified, although the topic is important as it addresses a major current health-care challenge. Automated scores can be derived which have the advantage of being derived objectively, avoiding human inter- and intraindividual variation. Prospective testing is recommended to assess potential service planning benefit.
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Lopez Ramos, Christian, Robert C. Rennert, Michael G. Brandel, Peter Abraham, Brian R. Hirshman, Jeffrey A. Steinberg, David R. Santiago-Dieppa, et al. "The effect of hospital safety-net burden on outcomes, cost, and reportable quality metrics after emergent clipping and coiling of ruptured cerebral aneurysms." Journal of Neurosurgery 132, no. 3 (March 2020): 788–96. http://dx.doi.org/10.3171/2018.10.jns18103.
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OBJECTIVESafety-net hospitals deliver care to a substantial share of vulnerable patient populations and are disproportionately impacted by hospital payment reform policies. Complex elective procedures performed at safety-net facilities are associated with worse outcomes and higher costs. The effects of hospital safety-net burden on highly specialized, emergent, and resource-intensive conditions are poorly understood. The authors examined the effects of hospital safety-net burden on outcomes and costs after emergent neurosurgical intervention for ruptured cerebral aneurysms.METHODSThe authors conducted a retrospective analysis of the Nationwide Inpatient Sample (NIS) from 2002 to 2011. Patients ≥ 18 years old who underwent emergent surgical clipping and endovascular coiling for aneurysmal subarachnoid hemorrhage (SAH) were included. Safety-net burden was defined as the proportion of Medicaid and uninsured patients treated at each hospital included in the NIS database. Hospitals that performed clipping and coiling were stratified as low-burden (LBH), medium-burden (MBH), and high-burden (HBH) hospitals.RESULTSA total of 34,647 patients with ruptured cerebral aneurysms underwent clipping and 23,687 underwent coiling. Compared to LBHs, HBHs were more likely to treat black, Hispanic, Medicaid, and uninsured patients (p < 0.001). HBHs were also more likely to be associated with teaching hospitals (p < 0.001). No significant differences were observed among the burden groups in the severity of subarachnoid hemorrhage. After adjusting for patient demographics and hospital characteristics, treatment at an HBH did not predict in-hospital mortality, poor outcome, length of stay, costs, or likelihood of a hospital-acquired condition.CONCLUSIONSDespite their financial burden, safety-net hospitals provide equitable care after surgical clipping and endovascular coiling for ruptured cerebral aneurysms and do not incur higher hospital costs. Safety-net hospitals may have the capacity to provide equitable surgical care for highly specialized emergent neurosurgical conditions.
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Roa, Lina, Isabelle Citron, Jania A. Ramos, Jessica Correia, Berenice Feghali, Julia R. Amundson, Saurabh Saluja, Nivaldo Alonso, and Rodrigo Vaz Ferreira. "Cross-sectional study of surgical quality with a novel evidence-based tool for low-resource settings." BMJ Open Quality 9, no. 1 (March 2020): e000880. http://dx.doi.org/10.1136/bmjoq-2019-000880.
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BackgroundAdverse events from surgical care are a major cause of death and disability, particularly in low-and-middle-income countries. Metrics for quality of surgical care developed in high-income settings are resource-intensive and inappropriate in most lower resource settings. The purpose of this study was to apply and assess the feasibility of a new tool to measure surgical quality in resource-constrained settings.MethodsThis is a cross-sectional study of surgical quality using a novel evidence-based tool for quality measurement in low-resource settings. The tool was adapted for use at a tertiary hospital in Amazonas, Brazil resulting in 14 metrics of quality of care. Nine metrics were collected prospectively during a 4-week period, while five were collected retrospectively from the hospital administrative data and operating room logbooks.Results183 surgeries were observed, 125 patient questionnaires were administered and patient charts for 1 year were reviewed. All metrics were successfully collected. The study site met the proposed targets for timely process (7 hours from admission to surgery) and effective outcome (3% readmission rate). Other indicators results were equitable structure (1.1 median patient income to catchment population) and equitable outcome (2.5% at risk of catastrophic expenditure), safe outcome (2.6% perioperative mortality rate) and effective structure (fully qualified surgeon present 98% of cases).ConclusionIt is feasible to apply a novel surgical quality measurement tool in resource-limited settings. Prospective collection of all metrics integrated within existing hospital structures is recommended. Further applications of the tool will allow the metrics and targets to be refined and weighted to better guide surgical quality improvement measures.
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Catalano Jr, Edward W., Stephen Gerard Ruby, Michael L. Talbert, and Douglas G. Knapman. "College of American Pathologists Considerations for the Delineation of Pathology Clinical Privileges." Archives of Pathology & Laboratory Medicine 133, no. 4 (April 1, 2009): 613–18. http://dx.doi.org/10.5858/133.4.613.
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Abstract Context.—The Joint Commission (JC) established new medical staff privileging requirements effective January 2008. The new requirements include the development of ongoing professional practice evaluation (OPPE) and focused professional practice evaluation (FPPE) processes and incorporate the general competencies of patient care, medical knowledge, practice-based learning and improvement, interpersonal and communication skills, professionalism and systems-based practice jointly developed by the Accreditation Council for Graduate Medical Education (ACGME) and the American Board of Medical Specialties (ABMS). The College of American Pathologists makes resources available to assist members and their facilities in implementing the new requirements and improving patient care. Objectives.—To review the general requirements for privileging and identify how they may apply to pathologists, to identify currently available activities and metrics that may be useful in addressing these requirements, and to present identified concepts, activities, and metrics for consideration by pathologists and hospitals for their adaptation into the policies and procedures that address the new JC physician privileging requirements. Design.—Review available pathology privileging documentation that addressed the previous JC requirements, review the new requirements, and search for and review available and applicable resources, activities, and metrics. Results.—Common pathology activities and metrics can be incorporated into the privileging processes. Current and new activities and metrics can be incorporated or developed to address the 6 ACGME/ABMS “General Competencies.” Conclusion.—Each hospital has unique privileging and physician evaluation requirements. Providing concepts, activities, and metrics for pathologists and hospitals to consider in pathology privileging will help implement the OPPE and FPPE processes and meet medical staff privileging requirements.
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Glasgow, Justin M., Zugui Zhang, Linsey D. O’Donnell, Roshni T. Guerry, and Vinay Maheshwari. "Hospital palliative care consult improves value-based purchasing outcomes in a propensity score–matched cohort." Palliative Medicine 33, no. 4 (February 7, 2019): 452–56. http://dx.doi.org/10.1177/0269216318824270.
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Background: Hospital-based palliative care consultation is consistently associated with reduced hospitalization costs and more importantly with improved patient quality of life. As healthcare systems move toward value-based purchasing rather than fee-for-service models, understanding how palliative care consultation is associated with value-based purchasing metrics can provide evidence for expanded health system support for a greater palliative care presence. Aim: To understand how a palliative care consultation impacts rates of patient readmission and hospital-acquired infections associated with value-based purchasing metrics. Design: Retrospective propensity-matched case–control study evaluating the impact of palliative care consultation on hospital charges, hospital and intensive care unit length of stay, readmission rates, and rates of hospital-acquired conditions. Setting/participants: All adult patients admitted to a two hospital healthcare system over a 2-year period from 1 April 2015 to 31 March 2017. The palliative care team involved three physicians, five advanced practice providers, a social worker, and a chaplain during the study period. Results: A total of 3415 patients receiving a palliative consult were propensity matched to 25,028 controls. Compared to controls, cases had decreased charges per day and decreased rates of 7-, 30-, and 90-day readmissions. Conclusion: Through value-based purchasing, hospitals have 3% of their Medicare reimbursements at risk based on readmission rates. By clarifying prognosis and patient goals, palliative care consultation reduces readmission rates. Hospital systems may want to invest in larger palliative care programs as part of their efforts to reduce hospital readmissions.