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Original Investigation |

Excessively Long Hospital Stays After Trauma Are Not Related to the Severity of Illness:  Let’s Aim to the Right Target! FREE

John O. Hwabejire, MD, MPH1; Haytham M. A. Kaafarani, MD, MPH1; Ayesha M. Imam, MD1; Carolina V. Solis, MD1; Justin Verge, MHA1; Nancy M. Sullivan, MBA2; Marc A. DeMoya, MD1; Hasan B. Alam, MD1; George C. Velmahos, MD, PhD1
[+] Author Affiliations
1Division of Trauma, Emergency Surgery, and Surgical Critical Care, Massachusetts General Hospital and Harvard Medical School, Boston
2Case Management Department, Massachusetts General Hospital and Harvard Medical School, Boston
JAMA Surg. 2013;148(10):956-961. doi:10.1001/jamasurg.2013.2148.
Text Size: A A A
Published online

Importance  Reduction in length of hospital stay is a veritable target in reducing the overall costs of health care. However, many existing approaches are flawed because the assumptions of what cause excessive length of stay are incorrect; we methodically identified the right targets in this study.

Objective  To identify the causes of excessively prolonged hospitalization (ExProH) in trauma patients.

Design  The trauma registry, billing databases, and medical records of trauma admissions were reviewed. Excessively prolonged hospitalization was defined by the standard method used by insurers, which is a hospital stay that exceeds the Diagnosis Related Group–based trim point. The causes of ExProH were explored in a unique potentially avoidable days database, used by our hospital’s case managers to track discharge delays.

Setting  Level I academic trauma center.

Participants  Adult trauma patients admitted between January 1, 2006, and December 31, 2010.

Main Outcomes and Measures  Excessively prolonged hospitalization and hospital cost.

Results  Of 3237 patients, 155 (5%) had ExProH. The patients with ExProH compared with non-ExProH patients were older (mean [SD] age, 53 [21] vs 47 [22] years, respectively; P = .001), were more likely to have blunt trauma (92% vs 84%, respectively; P = .03), were more likely to be self-payers (16% vs 11%, respectively; P = .02) or covered by Medicare/Medicaid (41% vs 30%, respectively; P = .002), were more likely to be discharged to post–acute care facilities than home (65% vs 35%, respectively; P < .001), and had higher hospitalization cost (mean, $54 646 vs $18 444, respectively; P < .001). Both groups had similar Injury Severity Scores, Revised Trauma Scores, baseline comorbidities, and in-hospital complication rates. Independent predictors of mortality were discharge to a rehabilitation facility (odds ratio = 4.66; 95% CI, 2.71-8.00; P < .001) or other post–acute care facility (odds ratio = 5.04; 95% CI, 2.52-10.05; P < .001) as well as insurance type that was Medicare/Medicaid (odds ratio = 1.70; 95% CI, 1.06-2.72; P = .03) or self-pay (odds ratio = 2.43; 95% CI, 1.35-4.37; P = .003). The reasons for discharge delays were clinical in only 20% of the cases. The remaining discharges were excessively delayed because of difficulties in rehabilitation facility placement (47%), in-hospital operational delays (26%), or payer-related issues (7%).

Conclusions and Relevance  System-related issues, not severity of illness, prolong hospital stay excessively. Cost-reduction efforts should target operational bottlenecks between acute and postacute care.

Excessively prolonged hospitalization (ExProH) is associated with significant clinical risks and increased cost.15 These clinical risks include nosocomial infections, deep venous thrombosis, disuse atrophy, adverse drug reactions, medication errors, and multiple other adverse events. In a classic prospective study of more than 1000 patients with documented in-hospital complications in a university medical service, Schimmel1 concluded that the risk of complication was directly related to the length of time spent in the hospital. Andrews et al2 showed that the probability of experiencing an adverse event increased about 6% for each day of hospital stay.

The hospital length of stay (LOS) has been identified as one of the major drivers of resource consumption in multiple ways.35 Hospital cost increases because beds and human personnel are occupied by ExProH patients and because of the rise in associated adverse events.6 In addition, there is a societal cost due to ExProH patients’ lost economic productivity.

In the surgical community, there is a prevailing belief that the patient’s physiological condition, as determined by preexisting comorbidities and postoperative complications, is the major determinant of ExProH.3,7,8 A logical conclusion would then be that ExProH can be reduced by more attentive medical care to optimize patients for operation and avoid subsequent complications. There is great pressure to decrease hospital stays and, based on the earlier-mentioned argument, physicians are primarily responsible to do so.9 This study aims to identify trauma patients with ExProH and explore the reasons for it. We expect this information to be interesting to policy makers who are striving to understand the medical system and its associated cost. We hypothesize that the burden of injury, significant comorbidities, and postoperative complications are the major causes of ExProH.

All trauma patients aged 18 years or older who were admitted to the trauma service of the Massachusetts General Hospital, a level I academic trauma center, between January 1, 2006, and December 31, 2010, were retrospectively identified through our trauma registry. Trauma patients admitted to other services (orthopedic, neurosurgical, etc) were excluded from the study.

Data from the trauma registry were supplemented by information from the electronic medical records and the hospital’s billing records. These included demographic characteristics, injury-related characteristics, clinical information (hospital LOS, comorbidities, in-hospital complications, and in-hospital mortality), and financial information (hospital cost and the net margin, the latter being a generally used measure of cost containment and profitability). Additionally, we recorded the patients’ discharge disposition (home, post–acute care facility [including rehabilitation facility, long-term care, skilled nursing facility, transitional care unit, psychiatric units, hospice, and others], in-hospital death, and self-discharge against medical advice).

The primary outcome was ExProH. The Diagnosis Related Group (DRG) of each patient was reviewed and the trim point for LOS for that DRG was determined. The trim point for LOS is defined as 2 SDs above the mean LOS for cases within a DRG.10,11 Insurers use this trim point to determine prolonged hospitalization,10,11 and we used the same value to define ExProH. For example, the accepted average hospital LOS for a fracture of the forearm without complications and comorbidities for a patient older than 17 years (DRG 251, grouper version 23) is 3 days. The trim point is 9 days, and a hospital stay longer than this is considered ExProH. In another example, the average hospital LOS for a patient with major chest trauma with complications and comorbidities (DRG 083, grouper version 23) is 6 days and the trim point is 24 days. Hospital stay beyond this period is considered ExProH. In addition to the 2 outcomes, we identified and grouped the reasons for ExProH. For this, we used a unique database maintained by the Case Management Department. In this database, the case managers track potentially avoidable hospital days and the possible causes, including operational, payer-related, and clinical issues. Operational issues included the following: delays in scheduling surgery even if all preoperative tests had been completed and informed consent was signed; delays in scheduling or interpretation of required diagnostic tests; cancellations of scheduled procedures typically because of delays in diagnostic test interpretation; lack of timely response by consultants; or nonclinical, noninsurance, patient-related issues that were not resolved on time prior to discharge (eg, lack of family preparation for home care or failure of a patient’s guardian to arrive). Payer-related issues included delays resulting from medical necessity reviews by the health insurance provider or appeals when coverage was denied. Clinical reasons included delays in discharge because of changes in the patient’s clinical condition that required further tests or longer in-hospital observation.

Patients with ExProH were compared with patients without ExProH. Summary statistics were used to describe continuous variables, while proportions were calculated for categorical variables. We used χ2 or Fisher exact tests for comparisons between categorical variables. Comparisons between continuous variables were performed using t test for normally distributed data or the Mann-Whitney U test for data that were not normally distributed. Univariate and multivariate analyses were performed to determine independent predictors of ExProH. Statistical significance was defined as P < .05. All analyses were performed using IBM SPSS Statistics 20 software (IBM Corp). The study was approved by our institutional review board.

Of 3237 trauma patients admitted during the study period, 155 (5%) experienced ExProH. Table 1 compares the characteristics and clinical course of ExProH and non-ExProH patients. The ExProH patients were older, more likely to have blunt rather than penetrating trauma, and more likely to be discharged to post–acute care facilities rather than home compared with non-ExProH patients. They were also more likely to be self-payers or covered by Medicare/Medicaid. Their hospital LOS was more than 3 times longer and hospital cost was 3 times higher (mean, $54 646 vs $18 444, respectively; P < .001) (Table 1). In addition, the ExProH group had a net margin of −45.2%, compared with 2.6% for the non-ExProH group (higher is better). In-hospital mortality was lower for ExProH patients. There were no differences in race/ethnicity between the 2 groups.

Table Graphic Jump LocationTable 1.  Patient Characteristics for Excessively Prolonged Hospitalization vs Non–Excessively Prolonged Hospitalization

There were no differences between the ExProH and non-ExProH groups in Injury Severity Score (mean [SD], 18 [12] vs 16 [11], respectively; P = .10), physiology on admission as measured by the Revised Trauma Score (mean [SD], 7.6 [7.0] vs 8.3 [6.8], respectively; P = .19) or the weighted Revised Trauma Score (mean [SD], 4.4 [5.5] vs 5.0 [5.2], respectively; P = .17), and probability of survival (mean [SD], −0.53 [2.6] vs −0.29 [2.4], respectively; P = .22). Similarly, there were no differences in the prevalence of comorbidities (Table 2) or the incidence of in-hospital complications (Table 3) between the 2 groups. In the multivariate analysis, the independent predictors of ExProH were discharge to a rehabilitation facility, discharge to other types of post–acute care facility, and insurance status that was self-pay or Medicare/Medicaid (Table 4).

Table Graphic Jump LocationTable 2.  Baseline Comorbidities for Excessively Prolonged Hospitalization vs Non–Excessively Prolonged Hospitalization
Table Graphic Jump LocationTable 3.  In-Hospital Complication Rates for Excessively Prolonged Hospitalization vs Non–Excessively Prolonged Hospitalization
Table Graphic Jump LocationTable 4.  Independent Predictors of Excessively Prolonged Hospitalization Status

Other variables examined include intensive care unit requirement, ventilation requirement, head injuries, and need for an operation. Forty-three percent of ExProH patients required intensive care unit admission compared with 32% of non-ExProH patients (P = .01). However, in the multivariate logistic regression model, intensive care unit requirement or admission was not a predictor of ExProH (P = .22). Requirement for mechanical ventilation was similar in both the ExProH and non-ExProH groups (37 of 155 patients [24%] vs 723 of 3082 patients [23%], respectively; P = .91) and so was not tested in the multivariate model. Thirty-two percent of ExProH patients had head injuries compared with 26% of non-ExProH patients. This difference was not statistically significant (P = .10). Fifty-six percent of ExProH patients had surgery compared with 39% of non-ExProH patients. However, when having surgery was entered into the logistic regression model, the odds ratio was 0.60 (95% CI, 0.41-0.87; P = .03), implying that having surgery reduces the odds of exceeding the trim point LOS, ie, ExProH, by 40%.

Among ExProH patients, ExProH was caused by difficulties in transfer to a rehabilitation facility in 47%. In-hospital operational delays were the reason for ExProH in 26%, and payer-related issues were the reason in 7%. Clinical deterioration was the reason in only 20%. The specific reasons are displayed in Table 5.

Table Graphic Jump LocationTable 5.  Reasons for Discharge Delays

According to the Institute of Medicine’s Crossing the Quality Chasm,12,13 health care systems in the 21st century should aim to be “safe, effective, patient-centered, timely, efficient, and equitable.”12 In the current health care climate that revolves around improving quality and reducing cost, timeliness (defined by the Institute of Medicine as “reducing waits and sometimes harmful delays for both those who receive and those who give care”12) and efficiency of care (defined by the Institute of Medicine as “avoiding waste, in particular waste of equipment, supplies, ideas, and energy”12) are increasingly receiving close scrutiny. These factors are directly related to the subject of our study, which identifies important opportunities for reducing the hospital LOS in trauma patients. Reasons related to severity of illness or medical care are commonly believed to be the main causes for prolonged hospital stays.1416 Our study shows that delays in discharge are typically not caused by medical factors. In most cases, ExProH was related to administrative issues, predominantly the inability to place a patient in an appropriate rehabilitation facility, as well as to operational and insurance issues.

Other studies have argued that comorbidities and complications prolong hospital stay.16 It is unclear whether these complications prolonged hospital stay or a long hospital stay for nonmedical reasons encouraged the development of complications. Weintraub et al7 identified preprocedural variables such as age, elective vs emergency status, angina class, ejection fraction, and sex as well as postoperative factors like wound infection, pneumonia, arrhythmias, neurologic events, and postoperative infarction as determinants of prolonged hospital stay following coronary bypass surgery. In this study, prolonged hospitalization was arbitrarily defined as a hospital LOS longer than 10 days. Other arbitrary cutoff points of prolonged hospital stay have been used in similar studies.3,8

There is little doubt that the patient’s physiological condition and the postoperative morbidity play a crucial role in the duration of hospital stay. It is reasonable to assume that patients stay in the hospital longer because they are sicker. However, there is a small minority of patients with unreasonably long hospital stays (ExProH). Like others,17,18 we have found that in our institution these few patients (5% of trauma admissions) account for approximately 70% of the unnecessary hospital cost (J.V., M.A.D., H.B.A., G.C.V., Alice Gervasini, PhD, and David R. King, MD, unpublished data, June 2011), and for this reason ExProH patients present a special interest to health care providers and administrators. In contrast to our hypothesis (and to common belief), patients with and without ExProH had similar injury severity, physiological compromise, and comorbidities. The only independent predictors of ExProH were issues related to insurance coverage and discharge disposition.

A strength of our study was the definition of ExProH. As opposed to the preexisting variable and confusing definitions of prolonged hospital stay,3,7,8 we determined ExProH objectively according to the trim point. This can allow comparison with results from other centers. An additional strength was the analysis of the potentially avoidable hospital days database, which is populated prospectively by our case managers. Although some of the information is still crude and lacks the specific granularity that would allow us to identify the precise details leading to ExProH, we were able to group the different causes in broad categories. This database confirmed the findings of our multivariate analysis about nonmedical reasons being the cause of ExProH. Similar conclusions were reached by Brasel et al,19 who examined 120 trauma patients for discharge delays, defined as “a discharge-ready patient not discharged within 24 h.”19 The authors found no difference in injury severity, age, and comorbidities between the delayed and nondelayed groups. Lack of rehabilitation or other subacute care facility bed was the main reason for delay in 83% of the patients. Similarly, Irshad et al20 found that both medical and nonmedical reasons prolonged the hospital stay in a thoracic surgery service, with lack of home support (10.2% of patients) and the unavailability of convalescent facilities (7.1% of patients) being the main social reasons for delayed discharge.

Excessively prolonged hospitalization has significant financial implications for patients, insurers, and trauma centers. The cost of care for ExProH patients tripled compared with that of their non-ExProH counterparts. The increase in cost (and payments) did not result in increased profit for the hospital. Patients with ExProH had a net margin lower than that of non-ExProH patients by nearly 50%. Because the net margin is a measure of both profitability and cost control, the implication of our findings is that ExProH is a major driver of increased consumption of hospital resources and health care costs, while producing a negative financial impact on trauma centers. Based on the ExProH reasons identified, we believe that LOS and cost can be reduced without compromising the quality of trauma care delivered. About 80% of the ExProH cases were not related to clinical issues and therefore could potentially be avoided (Table 5). This means that ExProH could be reduced from 5% to 1%. In our institution with 2500 trauma admissions per year, this means a reduction in cost from $6 830 750 to $1 366 150, a significant financial benefit to the hospital. In the Commonwealth of Massachusetts with about 59 446 trauma discharges per year,21 this indicates hospital cost savings of approximately $130 000 000.

The main limitation of our study is the inability to determine the exact details related to the administrative bottlenecks that led to ExProH. These were difficult to evaluate retrospectively, but we have now designed a prospective study to capture the necessary information. A variety of potential causes of delays, such as the weekend phenomenon, were not explored. The causes recorded by the case managers were based on individual judgments without any consensus process. The generalization of these findings to other facilities is unknown. The specific discharge practices and hurdles of our region’s health care system may be less pertinent to other parts of the country. Despite these limitations, our study uncovers the true causes of ExProH. Trauma patients who stay for excessively prolonged periods in the hospital are not necessarily severely injured, physiologically compromised, or old. They simply stay in the hospital because of unresolved insurance issues, difficult-to-find rehabilitation beds, and in-hospital operational breakdowns. The financial burden of such unnecessary hospitalization is heavy. Although physicians should participate in all aspects of a patient’s care, there is little they can do to improve these specific issues. Health care policy makers should shift the focus toward the right target to reduce excessive hospital stays and cost in trauma patients.

Accepted for Publication: February 28, 2013.

Corresponding Author: George C. Velmahos, MD, PhD, Division of Trauma, Emergency Surgery, and Surgical Critical Care, Massachusetts General Hospital, 165 Cambridge St, Ste 810, Boston, MA 02114 (gvelmahos@partners.org).

Published Online: August 21, 2013. doi:10.1001/jamasurg.2013.2148.

Author Contributions: Velmahos had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Hwabejire, Kaafarani, Solis, Sullivan, DeMoya, Velmahos.

Acquisition of data: Hwabejire, Kaafarani, Imam, Solis.

Analysis and interpretation of data: Hwabejire, Kaafarani, Verge, Alam, Velmahos.

Drafting of the manuscript: Hwabejire, Kaafarani, Imam, Velmahos.

Critical revision of the manuscript for important intellectual content: Hwabejire, Solis, Verge, Sullivan, DeMoya, Alam, Velmahos.

Statistical analysis: Hwabejire.

Administrative, technical, or material support: Verge, Sullivan, Velmahos.

Study supervision: Kaafarani, DeMoya, Alam, Velmahos.

Conflict of Interest Disclosures: None reported.

Previous Presentation: This study was presented at the 93rd Annual Meeting of the New England Surgical Society; September 23, 2012; Rockport, Maine; and is published after peer review and revision.

Schimmel  EM.  The hazards of hospitalization. Ann Intern Med. 1964;60:100-110.
PubMed   |  Link to Article
Andrews  LB, Stocking  C, Krizek  T,  et al.  An alternative strategy for studying adverse events in medical care. Lancet. 1997;349(9048):309-313.
PubMed   |  Link to Article
Peterson  ED, Coombs  LP, Ferguson  TB,  et al.  Hospital variability in length of stay after coronary artery bypass surgery: results from the Society of Thoracic Surgeon’s National Cardiac Database. Ann Thorac Surg. 2002;74(2):464-473.
PubMed   |  Link to Article
Cowper  PA, DeLong  ER, Peterson  ED,  et al.  Variability in cost of coronary bypass surgery in New York State: potential for cost savings. Am Heart J. 2002;143(1):130-139.
PubMed   |  Link to Article
Cowper  PA, DeLong  ER, Peterson  ED,  et al; IHD Port Investigators.  Geographic variation in resource use for coronary artery bypass surgery. Med Care. 1997;35(4):320-333.
PubMed   |  Link to Article
Kaushal  R, Bates  DW, Franz  C, Soukup  JR, Rothschild  JM.  Costs of adverse events in intensive care units. Crit Care Med. 2007;35(11):2479-2483.
PubMed   |  Link to Article
Weintraub  WS, Jones  EL, Craver  J, Guyton  R, Cohen  C.  Determinants of prolonged length of hospital stay after coronary bypass surgery. Circulation. 1989;80(2):276-284.
PubMed   |  Link to Article
Rickard  MJ, Dent  OF, Sinclair  G, Chapuis  PH, Bokey  EL.  Background and perioperative risk factors for prolonged hospital stay after resection of colorectal cancer. ANZ J Surg. 2004;74(1-2):4-9.
PubMed   |  Link to Article
de Jong  JD, Westert  GP, Lagoe  R, Groenewegen  PP.  Variation in hospital length of stay: do physicians adapt their length of stay decisions to what is usual in the hospital where they work? Health Serv Res. 2006;41(2):374-394.
PubMed   |  Link to Article
LAWriter Ohio Laws and Rules. Definitions and DRG reporting requirements. http://codes.ohio.gov/oac/3701-14-01. Accessed September 1, 2012.
Langenbrunner  JC, Cashin  C, O'Dougherty  S, eds. Designing and Implementing Health Care Provider Payment Systems: How-to Manuals. Washington, DC: World Bank; 2009.
Institute of Medicine. Crossing the Quality Chasm. Washington, DC: National Academies Press; 2001.
Agency for Healthcare Research and Quality. Health care efficiency measures: identification, categorization, and evaluation. http://www.ahrq.gov/research/findings/final-reports/efficiency/hcemch1.html. Accessed August 7, 2013.
Kramer  AA, Zimmerman  JE.  A predictive model for the early identification of patients at risk for a prolonged intensive care unit length of stay. BMC Med Inform Decis Mak. 2010;10(27):27.
PubMed   |  Link to Article
Lagoe  RJ, Johnson  PE, Murphy  MP.  Inpatient hospital complications and lengths of stay: a short report. BMC Res Notes. 2011;4(135):135.
PubMed   |  Link to Article
Allman  RM, Goode  PS, Burst  N, Bartolucci  AA, Thomas  DR.  Pressure ulcers, hospital complications, and disease severity: impact on hospital costs and length of stay. Adv Wound Care. 1999;12(1):22-30.
PubMed
Calver  J, Brameld  KJ, Preen  DB, Alexia  SJ, Boldy  DP, McCaul  KA.  High-cost users of hospital beds in Western Australia: a population-based record linkage study. Med J Aust. 2006;184(8):393-397.
PubMed
Morris  JA  Jr, Sanchez  AA, Bass  SM, MacKenzie  EJ.  Trauma patients return to productivity. J Trauma. 1991;31(6):827-834.
PubMed   |  Link to Article
Brasel  KJ, Rasmussen  J, Cauley  C, Weigelt  JA.  Reasons for delayed discharge of trauma patients. J Surg Res. 2002;107(2):223-226.
PubMed   |  Link to Article
Irshad  K, Feldman  LS, Chu  VF, Dorval  JF, Baslaim  G, Morin  JE.  Causes of increased length of hospitalization on a general thoracic surgery service: a prospective observational study. Can J Surg. 2002;45(4):264-268.
PubMed
Executive Office of Health and Human Services, Commonwealth of Massachusetts. Massachusetts injury data facts and highlights. http://www.mass.gov/eohhs/gov/departments/dph/programs/health-stats/injury-suveillance/injury-data-facts-and-highlights.html. Accessed February 15, 2013.

Figures

Tables

Table Graphic Jump LocationTable 1.  Patient Characteristics for Excessively Prolonged Hospitalization vs Non–Excessively Prolonged Hospitalization
Table Graphic Jump LocationTable 2.  Baseline Comorbidities for Excessively Prolonged Hospitalization vs Non–Excessively Prolonged Hospitalization
Table Graphic Jump LocationTable 3.  In-Hospital Complication Rates for Excessively Prolonged Hospitalization vs Non–Excessively Prolonged Hospitalization
Table Graphic Jump LocationTable 4.  Independent Predictors of Excessively Prolonged Hospitalization Status
Table Graphic Jump LocationTable 5.  Reasons for Discharge Delays

References

Schimmel  EM.  The hazards of hospitalization. Ann Intern Med. 1964;60:100-110.
PubMed   |  Link to Article
Andrews  LB, Stocking  C, Krizek  T,  et al.  An alternative strategy for studying adverse events in medical care. Lancet. 1997;349(9048):309-313.
PubMed   |  Link to Article
Peterson  ED, Coombs  LP, Ferguson  TB,  et al.  Hospital variability in length of stay after coronary artery bypass surgery: results from the Society of Thoracic Surgeon’s National Cardiac Database. Ann Thorac Surg. 2002;74(2):464-473.
PubMed   |  Link to Article
Cowper  PA, DeLong  ER, Peterson  ED,  et al.  Variability in cost of coronary bypass surgery in New York State: potential for cost savings. Am Heart J. 2002;143(1):130-139.
PubMed   |  Link to Article
Cowper  PA, DeLong  ER, Peterson  ED,  et al; IHD Port Investigators.  Geographic variation in resource use for coronary artery bypass surgery. Med Care. 1997;35(4):320-333.
PubMed   |  Link to Article
Kaushal  R, Bates  DW, Franz  C, Soukup  JR, Rothschild  JM.  Costs of adverse events in intensive care units. Crit Care Med. 2007;35(11):2479-2483.
PubMed   |  Link to Article
Weintraub  WS, Jones  EL, Craver  J, Guyton  R, Cohen  C.  Determinants of prolonged length of hospital stay after coronary bypass surgery. Circulation. 1989;80(2):276-284.
PubMed   |  Link to Article
Rickard  MJ, Dent  OF, Sinclair  G, Chapuis  PH, Bokey  EL.  Background and perioperative risk factors for prolonged hospital stay after resection of colorectal cancer. ANZ J Surg. 2004;74(1-2):4-9.
PubMed   |  Link to Article
de Jong  JD, Westert  GP, Lagoe  R, Groenewegen  PP.  Variation in hospital length of stay: do physicians adapt their length of stay decisions to what is usual in the hospital where they work? Health Serv Res. 2006;41(2):374-394.
PubMed   |  Link to Article
LAWriter Ohio Laws and Rules. Definitions and DRG reporting requirements. http://codes.ohio.gov/oac/3701-14-01. Accessed September 1, 2012.
Langenbrunner  JC, Cashin  C, O'Dougherty  S, eds. Designing and Implementing Health Care Provider Payment Systems: How-to Manuals. Washington, DC: World Bank; 2009.
Institute of Medicine. Crossing the Quality Chasm. Washington, DC: National Academies Press; 2001.
Agency for Healthcare Research and Quality. Health care efficiency measures: identification, categorization, and evaluation. http://www.ahrq.gov/research/findings/final-reports/efficiency/hcemch1.html. Accessed August 7, 2013.
Kramer  AA, Zimmerman  JE.  A predictive model for the early identification of patients at risk for a prolonged intensive care unit length of stay. BMC Med Inform Decis Mak. 2010;10(27):27.
PubMed   |  Link to Article
Lagoe  RJ, Johnson  PE, Murphy  MP.  Inpatient hospital complications and lengths of stay: a short report. BMC Res Notes. 2011;4(135):135.
PubMed   |  Link to Article
Allman  RM, Goode  PS, Burst  N, Bartolucci  AA, Thomas  DR.  Pressure ulcers, hospital complications, and disease severity: impact on hospital costs and length of stay. Adv Wound Care. 1999;12(1):22-30.
PubMed
Calver  J, Brameld  KJ, Preen  DB, Alexia  SJ, Boldy  DP, McCaul  KA.  High-cost users of hospital beds in Western Australia: a population-based record linkage study. Med J Aust. 2006;184(8):393-397.
PubMed
Morris  JA  Jr, Sanchez  AA, Bass  SM, MacKenzie  EJ.  Trauma patients return to productivity. J Trauma. 1991;31(6):827-834.
PubMed   |  Link to Article
Brasel  KJ, Rasmussen  J, Cauley  C, Weigelt  JA.  Reasons for delayed discharge of trauma patients. J Surg Res. 2002;107(2):223-226.
PubMed   |  Link to Article
Irshad  K, Feldman  LS, Chu  VF, Dorval  JF, Baslaim  G, Morin  JE.  Causes of increased length of hospitalization on a general thoracic surgery service: a prospective observational study. Can J Surg. 2002;45(4):264-268.
PubMed
Executive Office of Health and Human Services, Commonwealth of Massachusetts. Massachusetts injury data facts and highlights. http://www.mass.gov/eohhs/gov/departments/dph/programs/health-stats/injury-suveillance/injury-data-facts-and-highlights.html. Accessed February 15, 2013.

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