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IntroductionAcute physiological deterioration is a major contributor to in-hospital morbidity and mortality. Early detection and intervention of deteriorating patients is key to improving patient outcomes. Prior research has demonstrated the effectiveness of Early Warning Systems and other algorithmic approaches in automatically identifying these patients from passively monitoring vital signs.MethodsIn this work, we conduct a prospective pilot study of clinical deployment of the Mayo Clinic Bedside Patient Rescue (BPR) system using an escalating alerting logic enabled by machine learning. Among four units where the BPR system was deployed, time to response and time to intervention for deteriorating patients were significantly reduced relative to matched control units.ResultsIn pilot units, time to response decreased by 35.4% (from 63.2 minutes to 40.8 minutes) and time to intervention decreased by 48.5% (from 106.3 minutes to 55.9 minutes). No significant differences were observed in counterbalance metrics of mortality, ICU transfer rate, and Rapid Response Team activation rate. Furthermore, the automated alerting system was well-received by clinicians participating in the pilot study, as assessed by survey.DiscussionThese results demonstrate a successful clinical deployment of a practice-changing machine learning alert system with demonstrable impact on improving patient care.

We describe the Bedside Patient Rescue (BPR) project, the goal of which is risk prediction of adverse events for non-ICU patients using ~200 variables (vitals, lab results, assessments, ...). There are several missing predictor values for most patients, which in the health sciences is the norm, rather than the exception. A Bayesian approach is presented that addresses many of the shortcomings to standard approaches to missing predictors: (i) treatment of the uncertainty due to imputation is straight-forward in the Bayesian paradigm, (ii) the predictor distribution is flexibly modeled as an infinite normal mixture with latent variables to explicitly account for discrete predictors (i.e., as in multivariate probit regression models), and (iii) certain missing not at random situations can be handled effectively by allowing the indicator of missingness into the predictor distribution only to inform the distribution of the missing variables. The proposed approach also has the benefit of providing a distribution for the prediction, including the uncertainty inherent in the imputation. Therefore, we can ask questions such as: is it possible this individual is at high risk but we are missing too much information to know for sure? How much would we reduce the uncertainty in our risk prediction by obtaining a particular missing value? This approach is applied to the BPR problem resulting in excellent predictive capability to identify deteriorating patients.

Objective We aimed to develop a model for accurate prediction of general care inpatient deterioration. Materials and Methods Training and internal validation datasets were built using 2-year data from a quaternary hospital in the Midwest. Model training used gradient boosting and feature engineering (clinically relevant interactions, time-series information) to predict general care inpatient deterioration (resuscitation call, intensive care unit transfer, or rapid response team call) in 24 hours. Data from a tertiary care hospital in the Southwest were used for external validation. C-statistic, sensitivity, positive predictive value, and alert rate were calculated for different cutoffs and compared with the National Early Warning Score. Sensitivity analysis evaluated prediction of intensive care unit transfer or resuscitation call. Results Training, internal validation, and external validation datasets included 24 500, 25 784 and 53 956 hospitalizations, respectively. The Mayo Clinic Early Warning Score (MC-EWS) demonstrated excellent discrimination in both the internal and external validation datasets (C-statistic = 0.913, 0.937, respectively), and results were consistent in the sensitivity analysis (C-statistic = 0.932 in external validation). At a sensitivity of 73%, MC-EWS would generate 0.7 alerts per day per 10 patients, 45% less than the National Early Warning Score. Discussion Low alert rates are important for implementation of an alert system. Other early warning scores developed for the general care ward have achieved lower discrimination overall compared with MC-EWS, likely because MC-EWS includes both nursing assessments and extensive feature engineering. Conclusions MC-EWS achieved superior prediction of general care inpatient deterioration using sophisticated feature engineering and a machine learning approach, reducing alert rate.

Spine surgeries tend to be lengthy (mean time of 4 hours) and highly variable (with some surgeries lasting 18 hours or more). This variability along with patient preferences driving scheduling decisions resulted in both low operating room (OR) utilization and significant overtime for surgical teams at Mayo Clinic. In this paper we discuss the development of an improved scheduling approach for spine surgeries over a rolling planning horizon. First, data mining and statistical analysis was performed using a large data set to identify categories of surgeries that could be grouped together based on surgical time distributions and could be categorized at the time of case scheduling. These surgical categories are then used in a hierarchical optimization approach with the objective of maximizing a weighted combination of OR utilization and net profit. The optimization model is explored to consider trade-offs and relationships among utilization levels, financial performance, overtime allowance, and case mix. The new scheduling approach was implemented via a custom Web-based application that allowed the surgeons and schedulers to interactively identify best surgical days with patients. A pilot implementation resulted in a utilization increase of 19% and a reduction in overtime by 10%.

Sepsis is a leading cause of in-hospital death over the world and septic shock, the most severe complication of sepsis, reaches a mortality rate as high as 50%. Early diagnosis and treatment can prevent most morbidity and mortality. In this work, Recent Temporal Patterns (RTPs) are used in conjunction with SVM classifier to build a robust yet interpretable model for early diagnosis of septic shock. This model is applied to two different prediction tasks: visit-level early diagnosis and event-level early prediction. For each setting, this model is compared against several strong baselines including atemporal method called Last-Value, six classic machine learning algorithms, and lastly, a state-of-the-art deep learning model: Long Short-Term Memory (LSTM). Our results suggest that RTP-based model can outperform all aforementioned baseline models for both diagnosis tasks. More importantly, the extracted interpretative RTPs can shed lights for the clinicians to discover progression behavior and latent patterns among septic shock patients.

Deep neural network models, especially Long Short Term Memory (LSTM), have shown great success in analyzing Electronic Health Records (EHRs) due to their ability to capture temporal dependencies in time series data. In this paper, we proposed a general deep neural network framework which incorporates two additional components with the aim of improving LSTM. The first component, a Convolutional Neural Network (CNN), is added before LSTM to obtain local characteristics of EHRs. The second component, a fully connected neural network (FC), introduces static information (e.g., age) to LSTM, which is applied to handle dynamic information (e.g., lab result). The medical condition we aim to predict is septic shock – it is the most advanced complication of sepsis and is due to severe abnormalities in circulation and/or cellular metabolism. Our proposed framework was evaluated for two experimental tasks: visit level early diagnosis (left align) and event level early prediction (right align). Our results show that for visit level early diagnosis, by incorporating both CNN and static information, our framework consistently outperforms the original LSTM. For event level early prediction, the same outcome is observed when predicting < 5 hours into the future, however, when predicting ≥ 5 hours into the future, the addition of the CNN component alone obtains the best results.

The primary cause of preventable death in many hospitals is the failure to recognize and/or rescue patients from acute physiologic deterioration (APD). APD affects all hospitalized patients, potentially causing cardiac arrest and death. Identifying APD is difficult, and response timing is critical - delays in response represent a significant and modifiable patient safety issue. Hospitals have instituted rapid response systems or teams (RRT) to provide timely critical care for APD, with thresholds that trigger the involvement of critical care expertise. The National Early Warning Score (NEWS) was developed to define these thresholds. However, current triggers are inconsistent and ignore patient-specific factors. Further, acute care is delivered by providers with different clinical experience, resulting in quality-of-care variation. This article documents a semi-Markov decision process model of APD that incorporates patient and provider heterogeneity. The model allows for stochastically changing health states, while determining patient subpopulation-specific RRT-activation thresholds. The objective function minimizes the total time associated with patient deterioration and stabilization; and the relative values of nursing and RRT times can be modified. A case study from January 2011 to December 2012 identified six subpopulations. RRT activation was optimal for patients in “slightly concerning” health states (NEWS > 0) for all subpopulations, except surgical patients with low risk of deterioration for whom RRT was activated in “concerning” states (NEWS > 4). Clustering methods identified provider clusters considering RRT-activation preferences and estimation of stabilization-related resource needs. Providers with conservative resource estimates preferred waiting over activating RRT. This study provides simple practical rules for personalized acute care delivery.

Objective Create a score to identify patients at risk of death or hospice placement who may benefit from goals of care discussion earlier in the hospitalisation. Design Retrospective cohort study to develop a risk index using multivariable logistic regression. Setting Two tertiary care hospitals in Southeastern Minnesota. Participants 92 879 adult general care admissions (50% male, average age 60 years). Primary and secondary outcome measures Our outcome measure was an aggregate of inhospital death or discharge to hospice. Predictor variables for the model encompassed comorbidities, nutrition status, functional status, demographics, fall risk, mental status, Charlson Comorbidity Index and acuity of illness on admission. Resuscitation status, race, geographic area of residence and marital status were added as covariates to account for confounding. Results Inhospital mortality and discharge to hospice were rare, with incidences of 1.2% and 0.8%, respectively. The Hospital End-of-Life Prognostic Score (HELPS) demonstrated good discrimination (C-statistic=0.866 in derivation set and 0.834 in validation set). The patients with the highest 5% of scores had an 8% risk of the outcome measure, relative risk 12.9 (10.9–15.4) when compared to the bottom 95%. Conclusions HELPS is able to identify patients with a high risk of inhospital death or need for hospice at discharge. These patients may benefit from early goals of care discussions.

BACKGROUND The use of rapid response systems (RRS), which were designed to bring clinicians with critical care expertise to the bedside to prevent unnecessary deaths, has increased. RRS rely on accurate detection of acute deterioration events. Early warning scores (EWS) have been used for this purpose but were developed using heterogeneous populations. Predictive performance may differ in medical vs surgical patients. OBJECTIVE To evaluate the performance of published EWS in medical vs surgical patient populations. DESIGN Retrospective cohort study. SETTING Two tertiary care academic medical center hospitals in the Midwest totaling more than 1500 beds. PATIENTS All patients discharged from January to December 2011. INTERVENTION None. MEASUREMENTS Time-stamped longitudinal database of patient variables and outcomes, categorized as surgical or medical. Outcomes included unscheduled transfers to the intensive care unit, activation of the RRS, and calls for cardiorespiratory resuscitation ("resuscitation call"). The EWS were calculated and updated with every new patient variable entry over time. Scores were considered accurate if they predicted an outcome in the following 24 hours. RESULTS All EWS demonstrated higher performance within the medical population as compared to surgical: higher positive predictive value (P < .0001 for all scores) and sensitivity (P < .0001 for all scores). All EWS had positive predictive values below 25%. CONCLUSIONS The overall poor performance of the evaluated EWS was marginally better in medical patients when compared to surgical patients. Journal of Hospital Medicine 2017;12:217-223.

Introduction Early Warning Scores (EWS) are widely used for early recognition of patient deterioration. Automated alarm/alerts have been recommended as a desirable characteristic for detection systems of patient deterioration. We undertook a comparative analysis of performance characteristics of common EWS methods to assess how they would function if automated. Methods We evaluated the most widely used EWS systems (MEWS, SEWS, GMEWS, Worthing, ViEWS and NEWS) and the Rapid Response Team (RRT) activation criteria in use in our institution. We compared their ability to predict the composite outcome of Resuscitation call, RRS activation or unplanned transfer to the ICU, in a time-dependent manner (3, 8, 12, 24 and 36h after the observation) by determining the sensitivity, specificity and positive predictive values (PPV). We used a large vital signs database (6,948,689 unique time points) from 34,898 unique consecutive hospitalized patients. Results PPVs ranged from less than 0.01 (Worthing, 3h) to 0.21 (GMEWS, 36h). Sensitivity ranged from 0.07 (GMEWS, 3h) to 0.75 (ViEWS, 36h). Used in an automated fashion, these would correspond to 1040–215,020 false positive alerts per year. Conclusions When the evaluation is performed in a time-sensitive manner, the most widely used weighted track-and-trigger scores do not offer good predictive capabilities for use as criteria for an automated alarm system. For the implementation of an automated alarm system, better criteria need to be developed and validated before implementation.

Objective Determine the prolonged effect of rapid response team (RRT) implementation on failure to rescue (FTR). Design Longitudinal study of institutional performance with control charts and Bayesian change point (BCP) analysis. Setting Two academic hospitals in Midwest, USA. Participants All inpatients discharged between 1 September 2005 and 31 December 2010. Intervention Implementation of an RRT serving the Mayo Clinic Rochester system was phased in for all inpatient services beginning in September 2006 and was completed in February 2008. Main Outcome Measure Modified version of the AHRQ FTR measure, which identifies hospital mortalities among medical and surgical patients with specified in-hospital complications. Results A decrease in FTR, as well as an increase in the unplanned ICU transfer rate, occurred in the second-year post-RRT implementation coinciding with an increase in RRT calls per month. No significant decreases were observed pre- and post-implementation for cardiopulmonary resuscitation events or overall mortality. A significant decrease in mortality among non-ICU discharges was identified by control charts, although this finding was not detected by BCP or pre- vs. post-analyses. Conclusions Reduction in the FTR rate was associated with a substantial increase in the number of RRT calls. Effects of RRT may not be seen until RRT calls reach a sufficient threshold. FTR rate may be better at capturing the effect of RRT implementation than the rate of cardiac arrests. These results support prior reports that short-term studies may underestimate the impact of RRT systems, and support the need for ongoing monitoring and assessment of outcomes to facilitate best resource utilization.

Objectives: To quantify the occurrence of myocardial infarction (MI) occurring in the early postoperative period after surgical hip fracture repair and estimate the effect on 1-year mortality. Design: A population-based, historical cohort study of individuals who underwent surgical repair of a hip fracture that used the computerized medical record linkage system of the Rochester Epidemiology Project. Setting: Academic and community hospitals, outpatient offices, and nursing homes in Olmsted County, Minnesota. Participants: Over the 15-year study period (1988�2002), 1,116 elderly adults underwent surgical repair of a hip fracture. Measurements: At the end of the first 7 days after hip fracture repair, participants were classified into one of three groups: clinically verified MI (cv-MI), subclinical myocardial ischemia, and no myocardial ischemia. One-year mortality was compared between these groups. Multivariate models assessed risk factors for early postoperative cv-MI and 1-year mortality. Results: Within the first 7 days after hip fracture repair, 116 (10.4%) participants experienced cv-MI and 41 (3.7%) subclinical myocardial ischemia. Overall 1-year mortality was 22%, with no difference between those with subclinical myocardial ischemia and those with no myocardial ischemia. One-year mortality for those with cv-MI (35.8%) was significantly higher than for the other two groups. Occurrence of early postoperative cv-MI, male sex, and history of heart failure or dementia were independently associated with greater 1-year mortality, whereas prefracture home residence and preoperative higher hemoglobin were protective. Conclusion: Rates of early postoperative, cv-MI after hip fracture repair exceed rates after other major orthopedic surgeries and are independently associated with greater 1-year mortality.

BACKGROUND: Patterns of clinical symptoms and outcomes of perioperative myocardial infarction (PMI) in elderly patients after hip fracture repair surgery are not well defined. METHODS: A retrospective 1:2 case-control study in a cohort of 1212 elderly patients undergoing hip fracture surgery from 1988 to 2002 in Olmsted County, Minnesota. RESULTS: The mean age was 85.3 ± 7.4 years; 76% female. PMI occurred in 167 (13.8%) patients within 7 days, of which 153 (92%) occurred in first 48 hours; 75% of patients were asymptomatic. Among patients with PMI, in-hospital mortality was 14.4%, 30-day mortality was 29 (17.4%), and 1-year mortality was 66 (39.5%). PMI was associated with a higher inpatient mortality rate (odds ratio [OR], 15.1; confidence interval [CI], 4.6–48.8), 30-day mortality (hazard ratio [HR], 4.3; CI, 2.1–8.9), and 1-year mortality (HR, 1.9; CI, 1.4–2.7). CONCLUSION: Elderly patients, after hip fracture surgery, have a higher incidence of PMI and mortality than what guidelines indicate. The majority of elderly patients with PMI did not experience ischemic symptoms and required cardiac biomarkers for diagnosis. The results of our study support the measurement of troponin in postoperative elderly patients for the diagnosis of PMI, in order to implement in-hospital preventive strategies to reduce PMI-associated mortality. Journal of Hospital Medicine 2012. © 2012 Society of Hospital Medicine

Hip fracture and heart failure are becoming more prevalent conditions in hospitalized patients. Despite differences in postoperative outcomes from other intermediate risk procedures, guidelines classify hip fracture repair as an intermediate risk operation.This population-based study sought to examine the prevalence and incidence of heart failure in hip fracture patients.We conducted a population-based historical cohort study of 1116 Olmsted County, MN residents undergoing 1212 hip surgeries from 1988 through 2002. Data were obtained through medical record review. Heart failure was defined by Framingham criteria.The prevalence of preoperative heart failure in our study population was 27% (327 of 1212 cases). Those with preoperative heart failure demonstrated longer lengths of stay, were more often discharged to a skilled facility, and had higher inpatient mortality rates. Rates of postoperative heart failure were 6.7% at seven days and 21.3% at one year. Postoperative heart failure was more common among those with preoperative heart failure (HR 3.0), and those with preoperative heart failure demonstrated higher postoperative mortality rates. Men had a higher risk of postoperative mortality compared to women. Overall survival was lowest among those with both preoperative and postoperative heart failure.Heart failure represents a common and serious perioperative condition in hip fracture patients. Hip fracture patients with and without heart failure carry higher postoperative risk than guidelines may suggest. Future work must focus on the perioperative management of hip fracture patients with and without heart failure to mitigate postoperative morbidity.

Objective Determine the degree of congruence between several measures of adverse events. Design Cross-sectional study to assess frequency and type of adverse events identified using a variety of methods. Setting Mayo Clinic Rochester hospitals. Participants All inpatients discharged in 2005 ( n = 60 599). Interventions Adverse events were identified through multiple methods: (i) Agency for Healthcare Research and Quality-defined patient safety indicators (PSIs) using ICD-9 diagnosis codes from administrative discharge abstracts, (ii) provider-reported events, and (iii) Institute for Healthcare Improvement Global Trigger Tool with physician confirmation. PSIs were adjusted to exclude patient conditions present at admission. Main outcome measure Agreement of identification between methods. Results About 4% (2401) of hospital discharges had an adverse event identified by at least one method. Around 38% (922) of identified events were provider-reported events. Nearly 43% of provider-reported adverse events were skin integrity events, 23% medication events, 21% falls, 1.8% equipment events and 37% miscellaneous events. Patients with adverse events identified by one method were not usually identified using another method. Only 97 (6.2%) of hospitalizations with a PSI also had a provider-reported event and only 10.5% of provider-reported events had a PSI. Conclusions Different detection methods identified different adverse events. Findings are consistent with studies that recommend combining approaches to measure patient safety for internal quality improvement. Potential reported adverse event inconsistencies, low association with documented harm and reporting differences across organizations, however, raise concerns about using these patient safety measures for public reporting and organizational performance comparison.

OBJECTIVES: To determine whether obesity affects cardiac complications after hip fracture repair. DESIGN: A population-based historical study using data from the Rochester Epidemiology Project. SETTING: Olmsted County, Minnesota. PARTICIPANTS: All urgent hip fracture repairs between 1988 and 2002. MEASUREMENTS: Body mass index (BMI) was categorized as underweight (

The decline in hip fracture incidence is now accompanied by a further reduction in the likelihood of a recurrent hip fracture among survivors of the first fracture. Hip fracture incidence is declining in North America, but trends in hip fracture recurrence have not been described. All hip fracture events among Olmsted County, Minnesota residents in 1980–2006 were identified. Secular trends were assessed using Poisson regression, and predictors of recurrence were evaluated with Andersen–Gill time-to-fracture regression models. Altogether, 2,752 hip fractures (median age, 83 years; 76% female) were observed, including 311 recurrences. Between 1980 and 2006, the incidence of a first-ever hip fracture declined by 1.37%/year for women (p

BACKGROUND: The use of specialized orthopedic surgery (SOS) units in total knee arthroplasty (TKA) patients is well established. The number and costs of arthoplasty surgeries continue to increase, requiring institutions to reexamine their existing practices for financial sustainability. OBJECTIVE: The objective of this study was to determine whether having elective TKA patients in SOS units affects resource utilization and outcomes. DESIGN: The study was designed to retrospectively compare elective TKA patients from 1996 to 2004 admitted directly to SOS units with those admitted to nonorthopedic nursing (NON) units. SETTING: The setting was an academic teaching hospital. PATIENTS: Five thousand five hundred and thirty-four patients met inclusion criteria. Of these, 5082 (patients 91.8%) were admitted to SOS units and 452 (8.2%) to NON units. MEASUREMENTS: The primary outcomes measured were length of stay (LOS) and costs, adjusted for age, sex, surgical year, comorbidities, and American Society of Anesthesiologists status. Secondary outcomes were 30-day mortality, readmissions, reoperations, and discharge disposition. RESULTS: Mean age of the patients in SOS and NON units was 68.3 and 67.9 years, respectively (P = .50). Adjusted LOS was 0.234 days shorter in SOS units (95% CI: 0.083, 0.385). Adjusted total and hospital cost savings in the SOS unit group were $600 (95% CI: $122, $1079) and $594 (95% CI: $141, $1047), respectively. More NON-unit patients required unanticipated transfers to the intensive care unit (ICU) from the general postoperative nursing unit (3.1% vs. 1.63%; P = .023); however, the mean number of ICU days did not differ between groups. NON-unit patients were more likely to be discharged with home health care (P < .001). There were no differences in 30-day outcomes. CONCLUSIONS: Patients on SOS units following elective TKA have a reduced LOS and decreased total and hospital costs. Our results should encourage hospitals to reevaluate postoperative patient flow to optimize resource utilization. Journal of Hospital Medicine 2008;3:218–227. © 2008 Society of Hospital Medicine.

Objectives To determine the incidence and 1-year outcomes of an elderly population with perioperative atrial arrhythmia (PAA) within 7 days of hip fracture surgery. Design Retrospective cohort study. Setting The Rochester Epidemiology Project (REP). Participants Elderly adults consecutive undergoing hip fracture repair from 1988 to 2002 in Olmsted County, Minnesota (N = 1,088, mean age 84.0 ± 7.4, 80.2% female). Measurements Baseline clinical variables were analyzed in relation to survival using Cox proportional hazards methods for comparison. Results Sixty-one participants (5.6%) developed PAA within the first 7 days. During 1 year of follow-up, 239 (22%) participants died. PAA was associated with greater mortality (45% vs 21%; hazard ratio (HR) = 2.8, 95% confidence interval (CI) = 1.9–4.2). Other mortality risk factors were male sex (HR = 2.0, 95% CI = 1.5–2.6), congestive heart failure (HR = 2.1, 95% CI = 1.7–2.8), chronic renal insufficiency (HR = 2.0, 95% CI = 1.5–2.8), dementia (HR = 2.9, 95% CI = 2.2–3.7), and American Society of Anesthesiologists risk Class III, IV, or V (HR = 3.3, 95% CI = 1.9–5.9). Conclusion Elderly adults undergoing hip fracture surgery who develop PAA within 7 days have significantly higher 1-year mortality than those who do not. Further studies are indicated to determine whether prevention of PAA will reduce mortality in this population.

As the obese population is increasing rapidly worldwide, there is more interest to study the different aspects of obesity and its impact especially on healthcare outcomes and health related issues. Targeting non-surgical times in the operating room (OR), this study focuses on the effect of obesity along with clinical factors on pre-incision times in OR. Specifically, both the individual and combined effect of clinical factors with obesity on pre-incision times is studied. Results show that with the confidence of 95%, pre-incision time in the OR of obese patients is significantly higher than those for non-obese patients by approximately five percent. Findings also show that more complex cases do not exhibit significant differences between these patient subgroups.

Background: Most nationally standardised quality measures use widely accepted evidence-based processes as their foundation, but the discharge instruction component of the United States standards of Joint Commission on Accreditation of Healthcare Organizations heart failure core measure appears to be based on expert opinion alone. Objective: To determine whether documentation of compliance with any or all of the six required discharge instructions is correlated with readmissions to hospital or mortality. Research design: A retrospective study at a single tertiary care hospital was conducted on randomly sampled patients hospitalised for heart failure from July 2002 to September 2003. Participants: Applying the Joint Commission on Accreditation of Healthcare Organizations criteria, 782 of 1121 patients were found eligible to receive discharge instructions. Eligibility was determined by age, principal diagnosis codes and discharge status codes. Measures: The primary outcome measures are time to death and time to readmission for heart failure or readmission for any cause and time to death. Results: In all, 68% of patients received all instructions, whereas 6% received no instructions. Patients who received all instructions were significantly less likely to be readmitted for any cause (p = 0.003) and for heart failure (p = 0.035) than those who missed at least one type of instruction. Documentation of discharge instructions is correlated with reduced readmission rates. However, there was no association between documentation of discharge instructions and mortality (p = 0.521). Conclusions: Including discharge instructions among other evidence-based heart failure core measures appears justified.

BACKGROUND Hospitalists, defined as hospital-based physicians who take responsibility for managing the medical needs of inpatients, represent a significant trend in physician specialization. However, only limited anecdotal data quantifying the status of hospital medicine groups around the country is available. OBJECTIVE To better understand the extent and nature of the hospitalist movement, utilizing data from the 2003 Annual Survey of the American Hospital Association (AHA). STUDY POPULATION 4895 acute care hospitals in the United States. MEASUREMENTS Number and percentage of hospitals with hospital medicine groups; mean number of hospitalists per group; hospitalists per average daily census (ADC) of 100 patients; distribution of groups by employment model. DESCRIPTIVE VARIABLES Census region; rural/urban status; number of beds; organizational control; teaching status. RESULTS There are approximately 1415 hospital medicine groups and 11 159 hospitalists in the United States. The overall penetration of hospital medicine groups at hospitals is 29% (55% at hospitals with 200 or more beds), and the in-hospital impact at hospitals with hospital medicine groups is 3.93 hospitalists per 100 ADC. The average hospital medicine group has 7.9 hospitalists. There is a fairly equal distribution among the 3 major employment models for hospital medicine groups: hospital employees, independent provider groups, and physician groups. All these measures can vary substantially, depending on the characteristics of individual hospitals. CONCLUSIONS Hospital medicine appears to have become part of the mainstream delivery of health care in the United States. No employment model of hospital medicine group appears to dominate this specialty. We expect there will continue to be growth and diversity in the implementation of hospital medicine groups. Journal of Hospital Medicine 2006;1:75–80. © 2006 Society of Hospital Medicine.

BACKGROUND: The rapid growth of the hospitalist movement presents an opportunity to reconsider paradigms of care for hospitalized older patients. METHODS: To determine the impact of the hospitalist movement on acute care geriatrics, we conducted a cross-sectional survey of the hospitalist community in 2003 and 2004. RESULTS: We identified innovations in geriatric hospital care in only 11 hospitalist programs. These innovations varied widely in complexity, goals, structure, and staffing. The majority targeted patients using age as a criterion and incorporated geriatrics training for nurses or physicians. Several innovations had one or more of the following features: geriatrician-hospitalists or gerontology nurse-practitioners, perioperative management for complex older patients, specialized geriatric services such as skilled nursing units or acute care for elders units, and quality improvement initiatives targeted to the older patient. A case study of the Hospital Internal Medicine group at the Mayo Clinic is presented as an example of a complex innovation highlighting several of these features. CONCLUSIONS: The scarcity of geriatric care approaches among hospitalist groups highlights the need for collaboration between hospitalists and geriatricians, with the goals of rethinking staffing models and organization of care and focusing on quality-improvement activities. In particular, perioperative care and postdischarge care are two clinical areas where innovation in hospital care may particularly benefit older patients. Significant opportunities remain for collaboration, coordination, and research to improve the care of acutely ill older patients at the intersection of geriatric and hospital medicine. Journal of Hospital Medicine 2006;1:29–35. © 2006 Society of Hospital Medicine.

Healthcare data provides a wealth of information that can be used to study and improve patient outcomes. Electronic Medical Records and other sources of healthcare data are often managed in relational database system and archived using modern data warehousing techniques. Contemporary semantic database technology has many advantages over traditional database systems; however, the utility of the semantic data can be limited if the data is not converted properly from a tabular representation. There are a variety of tools which will naively convert tabular data into a Resource Description Format semantic graph. Without proper guidance from the operator, the tools will generate a semantically weak database which doesn't have the necessary richness for semantic analysis. This paper describes the conversion process for two healthcare databases, with the goal of creating a robust dataset for semantic analysis. The “lessons learned” from this process are detailed in order to serve as a resource for other biomedical researchers and clinicians interested in generating a useful semantic dataset from their own relational databases.

The concepts of peer review and the venerable morbidity and mortality conference are familiar improvement approaches to health care providers. These 2 entities are typically provider or patient centric and are not typically extended within hospitals and health systems as a tool for organizational learning for care process or system failures. Out of a desire to deepen our understanding and accelerate learning about quality and safety opportunities in our hospitals, Mayo Clinic embarked on journey to analyze the stories of all patient deaths. This paper illuminates the lessons learned through the development and evolution of the Mayo Clinic Mortality Review System (Rochester, MN).

AimWhile early warning scores (EWS) have the potential to identify physiological deterioration in an acute care setting, the implementation of EWS in clinical practice has yet to be fully realized. The primary aim of this study is to identify optimal patient-centered rapid response team (RRT) activation rules using electronic medical records (EMR)-derived Markovian models.MethodsThe setting for the observational cohort study included 38,356 adult general floor patients hospitalized in 2011. The national early warning score (NEWS) was used to measure the patient health condition. Chi-square and Kruskal Wallis tests were used to identify statistically significant subpopulations as a function of the admission type (medical or surgical), frailty as measured by the Braden skin score, and history of prior clinical deterioration (RRT, cardiopulmonary arrest, or unscheduled ICU transfer).ResultsStatistical tests identified 12 statistically significant subpopulations which differed clinically, as measured by length of stay and time to re-admission (P

To determine whether placement of photographs of physicians in hospital rooms improves patients' satisfaction with their medical care.This is a prospective, controlled study of 224 patients admitted to general internal medicine services in a teaching hospital. The intervention consisted of photographs (8 x 10 in) of attending and resident physicians displayed in the patients' rooms. Before dismissal, patients completed a survey that required them to match names with photographs of physician caregivers and included patient satisfaction questions. The primary outcome was whether patients who had photographs in their hospital room would correctly identify more physicians than those with no photographs in their room.The presence of photographs on the hospital wall was associated with a significant improvement in the number of physicians identified correctly (odds ratio [OR], 1.83; 95% confidence interval [CI], 1.47-2.27; P.001). The percentage of physicians that patients identified by correctly matching their physicians' names to their photographs was significantly associated with satisfaction with physician responsiveness (OR, 1.19; 95% CI, 1.01-1.40; P=.03) and with the way in which physicians addressed questions regarding medical care (OR, 1.23; 95% CI, 1.05-1.44; P=.05).Patients who had photographs of their physicians on the wall of their hospital room were able to identify correctly a larger number of physicians on their team compared with patients who had no photographs. Patient satisfaction was related to the number of physicians' photographs that patients could identify correctly.

60 Background: A significant number of patients with advanced cancer die in the hospital. Examination of patterns of care and palliative care (PC) involvement may identify opportunities for process of care improvements for this vulnerable population. Methods: Patients were identified using the institutional mortality review system registry (Mayo Clinic hospitals from July, 2013-June, 2014). Within this group, patients with a diagnosis of terminal malignancy were identified by chart review and ICD-9 codes. Patient demographics, clinical characteristics, and use of PC resources were characterized in the last 60 days of life. Results: There were 159 of 924 decedents identified whose primary cause of death was advanced malignancy (Table). Ninety-two patients (57%) had PC consultation during the index admission preceding death, while 31 (19%) patients had seen PC as an outpatient or inpatient prior to index admission. Lack of inpatient PC consultation at index admission was associated with hematologic malignancy (p < 0.001), full code status at death (p 0.009), shorter median time between change of code status to DNR and death (13 vs 67 hrs, p < 0.001), death in the ICU (58% vs 31%, p < 0.001), and presence of code blue (22% vs 5%, p-value = 0.003). Patients who received a PC consultation were less likely to have chemotherapy within 14 days of death (15% vs 37%, p-value = 0.001), and had a median composite aggressive end of life score (6 factor composite score based on utilization in last 30 days of life, higher score indicating more aggressive care) of 3 vs 4 (p 0.002). Conclusions: Inpatient PC consultation for cancer patients who die in the hospital is associated with less aggressive cancer care at end of life. The single-institution patient population and small sample size are the limitations of our study. [Table: see text]

BACKGROUND Unplanned (unexpected) transfers to the intensive care unit (ICU) are typically preceded by physiologic instability. However, trends toward instability may be subtle and not accurately reflected by changes in vital signs. The shock index (SI) (heart rate/systolic blood pressure as an indicator of left ventricular function, reference value of 0.54) may be a simple alternative means to predict clinical deterioration. OBJECTIVE To assess the association of the SI with unplanned ICU transfers. DESIGN Retrospective case-control study. SETTING Academic medical center. PATIENTS Fifty consecutive general medical patients with unplanned ICU transfers between 2003 and 2004 and 50 matched controls admitted to the same general medical unit between 2002 and 2004. MEASUREMENTS Demographic data and vital signs abstracted from chart review. RESULTS The SI was associated with unplanned ICU transfer at values of 0.85 or greater (P < 0.02; odds ratio, 3.0) and there was a significant difference between the median of worst shock indices of cases and controls (0.87 vs. 0.72; P < 0.005). There was no significant difference in age, race, admission ward, or Charlson Comorbidity Index, but hospital stay for cases was significantly longer (mean [standard deviation, SD], 14.8 [9.7] days vs. 5.7 [6.3] days; P < 0.001). CONCLUSIONS SI is associated with unplanned transfers to the ICU from general medical units at values of 0.85 or greater. Future studies will determine whether SI is more accurate than simple vital signs as an indicator of clinical decline. If so, it may be a useful trigger to activate medical emergency or rapid response teams (RRTs).

Background A project sponsored by the University Health System Consortium has addressed the inaccuracy and high variability across institutions concerning the use of the failure to rescue (FTR) quality indicator defined by the Agency for Healthcare Research and Quality (AHRQ). Results indicated that of the complications identified by the quality indicator, 29.5% were pre-existing upon hospital admission. Objective The purpose of our study was to investigate the possible bias to FTR measures by including cases of complications that were pre-existing at admission. Methods Hospital discharges between 1 January 1996 and 30 September 2007 were retrospectively gathered from administrative databases. Using definitions outlined by the AHRQ and the National Quality Forum (NQF), FTR rates were calculated. Using present on admission coding, FTR rates were recalculated to differentiate between the rates of pre-existing and that of acquired cases. Results Using the AHRQ definition, the overall FTR rate was 11.60%. The FTR rate for patients with pre-existing complications was 8.85%, whereas patients with complications acquired during hospitalisation had an FTR rate of 18.46% (p

Obese patients are thought to be at higher risk of postoperative medical complications. We determined whether body mass index (BMI) is associated with postoperative in-hospital noncardiac complications following urgent hip fracture repair.We conducted a population-based study of Olmsted County, Minnesota, residents operated on for hip fracture in 1988 to 2002. BMI was categorized as underweight (18.5 kg/m2), normal (18.5-24.9 kg/m2), overweight (25.0-29.9 kg/m2), and obese (or = 30 kg/m2). Postoperative inpatient noncardiac medical complications were assessed. Complication rates were estimated for each BMI category and overall rates were assessed using logistic regression modeling.There were 184 (15.6%) underweight, 640 (54.2%) normal, 251 (21.3%) overweight, and 105 (8.9%) obese hip fracture repairs (mean age, 84.2 +/- 7.5 years; 80% female). After adjustment, the risk of developing an inpatient noncardiac complication for each BMI category, compared to normal BMI, was: underweight (odds ratio [OR], 1.33; 95% confidence interval [CI], 0.95-1.88; P = 0.10), overweight (OR, 1.01; 95% CI, 0.74-1.38; P = 0.95), and obese (OR, 1.28; 95% CI, 0.82-1.98; P = 0.27). Multivariate analysis demonstrated that an ASA status of III-V vs. I-II (OR, 1.84; 95% CI, 1.25-2.71; P = 0.002), a history of chronic obstructive pulmonary disease (COPD) or asthma (OR, 1.58; 95% CI, 1.18-2.12; P = 0.002), male sex (OR, 1.49; 95% CI, 1.10-2.02; P = 0.01), and older age (OR, 1.05; 95% CI, 1.03-1.06; P0.001) contributed to an increased risk of developing a postoperative noncardiac inpatient complication. Underweight patients had higher in-hospital mortality rates than normal BMI patients (9.3 vs. 4.4%; P = 0.01).BMI has no significant influence on postoperative noncardiac medical complications in hip-fracture patients. These results attenuate concerns that obese or frail, underweight hip-fracture patients may be at higher risk postoperatively for inpatient complications.

BACKGROUND: Hip operation (total hip arthroplasty [THA] or fracture repair) is the most common noncardiac surgical procedure performed in patients age 65 years and older. OBJECTIVE: To determine the predictors of ischemic stroke in patients who have undergone hip operation. DESIGN: Population-based historical cohort study, in which postoperative ischemic strokes were identified from medical record review for stroke diagnostic codes and brain imaging results and were confirmed by physician review. SETTING: Tertiary care center in Olmsted County, Minnesota. PATIENTS: Residents of Olmsted County who underwent hip surgical procedure. MEASUREMENTS: Incidence of ischemic stroke within 1 year of hip operation. RESULTS: In total, 1606 patients underwent 1886 hip procedures from 1988 through 2002 and were observed for ischemic stroke for 1 year after their procedure. Sixty-seven ischemic strokes were identified. The rate of stroke at 1 year after hip operation was 3.9%. In univariate analysis, history of atrial fibrillation (hazard ratio [HR], 2.16; P = 0.005), hip fracture repair vs. total hip arthroplasty (HR, 3.80; P < 0.001), age 75 years or older (HR, 2.20; P = 0.02), aspirin use (HR, 1.8; P = 0.01), and history of previous stroke (HR, 4.18; P < 0.001) were significantly associated with increased risk of stroke. In multivariable analysis, history of stroke (HR, 3.27; P < 0.001) and hip fracture repair (HR, 2.74; P = 0.004) were strong predictors of postoperative stroke. CONCLUSIONS: This population-based historical cohort of patients with hip operation had a 3.9% cumulative probability of ischemic stroke over the first postoperative year. Hip fracture repair and history of stroke were the strongest predictors of this complication. Journal of Hospital Medicine 2009;4:298–303. © 2009 Society of Hospital Medicine.

ObjectiveTo determine the impact of BMI on post-operative outcomes and resource utilization following elective total hip arthroplasty (THA).DesignA retrospective cohort analysis on all primary elective THA patients between 1996 and 2004. Primary outcomes investigated using regression analyses included length of stay (LOS) and costs (US dollars).SettingMayo Clinic Rochester, a tertiary care centre.SubjectsPatients were stratified by pre-operative BMI as normal (18·5–24·9 kg/m2), overweight (25·0–29·9 kg/m2), obese (30·0–34·9 kg/m2) and morbidly obese (≥35·0 kg/m2). Of 5642 patients, 1362 (24·1 %) patients had a normal BMI, 2146 (38·0 %) were overweight, 1342 (23·8 %) were obese and 792 (14·0 %) were morbidly obese.ResultsAdjusted LOS was similar among normal (4·99 d), overweight (5·00 d), obese (5·02 d) and morbidly obese (5·17 d) patients (P= 0·20). Adjusted overall episode costs were no different (P= 0·23) between the groups of normal ($17 211), overweight ($17 462), obese ($17 195) and morbidly obese ($17 655) patients. Overall operative and anaesthesia costs were higher in the morbidly obese group ($5688) than in normal ($5553), overweight ($5549) and obese ($5593) patients (P= 0·03). Operating room costs were higher in morbidly obese patients ($3418) than in normal ($3276), overweight ($3291) and obese ($3340) patients (P< 0·001). Post-operative costs were no different (P= 0·30). Blood bank costs differed (P= 0·002) and were lower in the morbidly obese group ($180) compared with the other patient groups (P< 0·05). Other differences in costs were not significant. Morbidly obese patients were more likely to be transferred to a nursing home (24·1 %) than normal (18·4 %), overweight (17·9 %) or obese (16·0 %) patients (P= 0·001 each). There were no differences in the composite endpoint of 30 d mortality, re-admissions, re-operations or intensive care unit utilization.ConclusionsBMI in patients undergoing primary elective THA did not impact LOS or overall institutional acute care costs, despite higher operative costs in morbidly obese patients. Obesity does not increase resource utilization for elective THA.

We retrospectively compared resource use of 2 groups of patients who underwent total hip arthroplasty between 1996 and 2004: those cared for on specialized orthopedic surgery (SOS) units and those cared for on nonorthopedic nursing (NON) units. Of 5546 patients, 5275 (95.1%) were admitted to SOS units and 271 (4.9%) to NON units. Mean overall adjusted cost saving for SOS patients was $622 (SD, $315; 95% CI, $3, $1241). Mean blood bank and room-and-board costs were lower on SOS units: $110 (SD, $36; 95% CI, $40, $181) and $298 (SD, $118; 95% CI, $66, $530), respectively. Difference in length of stay was not significant: mean, 0.19 day; SD, 0.11 day; 95% CI, -0.02 day, 0.40 day. Our results suggest that SOS units, as one way of optimizing patient flow in the postoperative period, may reduce unnecessary inpatients costs.

BACKGROUND We previously demonstrated that a hospitalist service created to medically manage patients with hip fracture reduced time to surgery and length of hospital stay, with no difference in inpatient mortality, compared with patients who received standard care. Whether this improved efficiency affects long-term mortality is unknown. OBJECTIVE This study examined the effects of this hospitalist service versus standard care on mortality up to 1 year and identified predictors of mortality in patients with hip fracture. DESIGN Retrospective cohort study. SETTING Tertiary care center. PATIENTS Four hundred and sixty-six consecutive patients admitted for surgical repair of a hip fracture in 2000–2002 with 93% 1-year follow-up. RESULTS There was no significant difference in survival of the patients between those on the hospitalist care service and those on the standard care service (70.5% [CI: 64.8%, 76.7%] vs. 70.6% [CI: 64.9%, 76.8%]; P = .36), despite the shortened time to surgery and decreased length of stay in the hospitalist group. Predictors of mortality included: admission from a nursing home (hazard ratio [HR] 2.24, [CI: 1.73, 2.90]); age at admission (HR 1.17 [CI: 0.99, 1.38]); inpatient complications, including ICU admission, myocardial infarction, or acute renal failure (HR 1.85 [CI: 1.45, 2.35]); and ASA class III or IV compared with ASA class II (HR 4.20 [CI: 2.21, 7.99]). CONCLUSIONS The improved efficiency in reducing length of stay and time to surgery in the hospitalist group did not adversely affect long-term mortality of this patient population. Journal of Hospital Medicine 2007;2:219–225. © 2007 Society of Hospital Medicine.

BACKGROUND: We learned from a focus group that many patients find discharge to be one of the least satisfying elements of the hospital experience. Patients cited insufficient communication about the day and time of the impending discharge as a cause of dissatisfaction. OBJECTIVE: In partnership with the Institute for Healthcare Improvement, Improvement Action Network collaborative, we tested the practicality of an in-room “discharge appointment” (DA) display. SETTING AND PATIENTS: Eight inpatient care units in 2 hospitals at an academic medical center (Mayo Clinic, Rochester, MN). INTERVENTION: DA displayed on a specially designed bedside dry-erase board. MEASUREMENTS: The primary outcome was the proportion of discharged patients who had been given a DA, including same-day DAs. Secondary outcomes were (1) the proportion of DAs scheduled before the actual dismissal day and (2) the timeliness of the actual departure compared with the DA. RESULTS: During the 4-month period, 2046 patients were discharged. Of those, 1256 patients (61%) were given a posted DA, of which 576 (46%) were scheduled at least a day in advance and 752 (60%) departed from the care unit within 30 minutes of the appointed time. CONCLUSIONS: With a program for in-room display of a DA in various hospital units, more than half the patients had a DA set, and most of the DA patients departed on time. Further investigation is needed to determine the effect of DAs on patient and provider satisfaction. Journal of Hospital Medicine 2007;2:13–16. © 2007 Society of Hospital Medicine.

Perioperative atrial arrhythmia (PAA) following hip fracture surgery in the elderly is known to have worse short-term cardiovascular outcomes. However, Its long-term impact has not been studied in similar population. In a retrospective cohort study, 1088 consecutive elderly patients undergoing hip

We investigated the effect of multiple freeze-thaw cycles on mumps, measles, and rubella virus serum antibody levels with whole-virus immunoglobulin G enzyme-linked immunoassays. Fresh serum samples from nine healthy adult volunteers were divided into six sets of five aliquots each. Samples were taken through a total of 10 freeze-thaw cycles and stored at 4°C until assayed. Each assay measurement was done in replicates of five, and the mean value was reported. After completing 10 freeze-thaw cycles, we found no clinically or statistically significant effect on measured antibody levels and found no discernible detrimental effect on the ability to measure these antibodies by enzyme-linked immunoassays.

This Update summarizes articles that are important to primary inpatient physicians. The selected articles cover quality improvement, invasive procedures, pulmonary diseases, infectious diseases, ca...

Multiple published studies in recent years have provided results and information that hospitalists can apply directly to patient care. This update summarizes some important articles published over the past 18 months. Article summaries are categorized into perioperative care, infectious disease, diagnosis of venous thromboembolism, end of life care, and guidelines for the management of patients with COPD or atrial fibrillation.

Medical morbidity associated with hip fractures in the elderly population is considerable. The all-cause mortality rate is 24% at 12 months. The functional limitations of survivors can be pronounced. As the American population ages, hip fractures will substantially affect the utilization of hospital resources. Several issues, including preoperative clearance and related surgical timing, deep venous thrombosis prophylaxis, delirium, nutrition, and urinary tract management, are important in the care of these patients. A close partnership between orthopedic surgeons and clinicians provides the best strategy of care for the subset of patients with multisystemic complications.

We identified all total knee arthroplasty patients between 1996 and 2004 and classified them by preoperative body mass index (BMI) as normal (BMI, 18.5-24.9 kg/m 2 ), overweight (BMI, 25.0-29.9 kg/m 2 ), obese (30-34.9 kg/m 2 ), or morbidly obese (≥ 35.0 kg/m 2 ). Of 5521 patients, 769 had a normal BMI, 1938 were overweight, 1539 were obese, and 1275 were morbidly obese. Adjusted length of stay was no different between normal (4.85 days), overweight (4.84 days), obese (4.86 days), or morbidly obese patients (4.93 days) ( P = .30). Overall costs were similar among normal ($15 386), overweight ($15 430), obese ($15 646), or morbidly obese patients ($15 752) ( P = .24). Postsurgical costs were no different among normal ($9860), overweight ($9889), obese ($10 063), or morbidly obese patients ($10 136) ( P = .44). Our results suggest that increased BMI does not lead to increased hospital resource use for total knee arthroplasty.

Hospitalists' increased role in perioperative medicine allows for examination of their effects on surgical patients. This study examined the effects of a hospitalist service created to medically manage elderly patients with hip fracture.During a 2-year historical cohort study of 466 patients 65 years or older admitted for surgical repair of hip fracture, we examined outcomes 1 year prior to and subsequent to the change from the standard to the hospitalist model.The mean (SD) time to surgery (38 [47] vs 25 [53] hours; P.001), time from surgery to dismissal (9 [8] vs 7 [5] days; P = .04), and length of stay (10.6 [9] vs 8.4 [6] days; P.001) were shorter in the hospitalist group. Predictors of shorter time to surgery were care by the hospitalist group (P = .002), older age (P = .01), and fall as the mechanism of fracture (P.001), while American Society of Anesthesia scores of 3 and 4 were associated with increased time to surgery (P.001). Receiving care by the hospitalist group (P.001) and diagnosis of delirium (P.001) were associated with increased chance of earlier dismissal, while admission to the intensive care unit decreased this chance (P.001). Diagnosis of delirium was more frequent in the hospitalist group (74 [32.2%] of 230 vs 42 [17.8%] of 236; P.001). There were no differences in inpatient deaths or 30-day readmission rates.In elderly patients with hip fracture, a hospitalist model decreased time to surgery, time from surgery to dismissal, and length of stay without adversely affecting inpatient deaths or 30-day readmission rates.

Background Hospitalists are assuming an increasing role in the care of surgical patients, but the impact of this model of care on postoperative outcomes is unknown. Objective To determine the impact of providing a collaborative, hospitalist-led model of care on postoperative outcomes and costs among patients having hip or knee arthroplasty. Design Randomized, controlled trial. Setting Academic medical center. Participants 526 patients having elective orthopedic surgery who are at elevated risk for postoperative morbidity. Measurements Length of stay, inpatient postoperative medical complications, health care provider satisfaction, and inpatient costs. Interventions A comanagement medical Hospitalist-Orthopedic Team compared with standard postoperative care by orthopedic surgeons with medical consultation. Results More patients in the hospitalist group were discharged from the hospital with no complications (61.6% vs. 49.8%; difference, 11.8 percentage points [95% CI, 2.8 to 20.7 percentage points]). Fewer minor complications were observed among hospitalist patients (30.2% vs. 44.3%; difference, -14.1 percentage points [CI, -22.7 to -5.3 percentage points]). Observed length of stay was not statistically different between treatment groups. However, when adjusted for discharge delays, mean length of stay for patients in the hospitalist model of care was shorter (5.1 days vs. 5.6 days; difference, -0.5 day [CI, -0.8 to -0.1 day]). Total costs did not differ between groups. Orthopedic surgeons and nurses preferred the hospitalist model. Limitations Care providers and patients were aware of intervention assignments, and the study could not capture all costs associated with the hospitalist model. Conclusions The comanagement medical Hospitalist-Orthopedic Team model reduced minor postoperative complication rates with no statistically significant difference in length of stay or cost. The nurses and surgeons strongly preferred the comanagement hospitalist model. Additional research on the clinical and economic impact of the hospitalist model in other surgical populations is warranted.