Characteristics associated with higher cost per day or longer length of stay in hospitalized patients who died during the hospitalization or were discharged to hospice

Youngwerth, Jeanie; Bartley, Jess B.; Yamashita, Traci E.; Kutner, Jean S.

doi:10.1002/jhm.889

Cited by 12 publications

(5 citation statements)

References 23 publications

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“…According to this report and another study (Kardamanidis et al, 2007), the death of a young person will be more expensive than the death of an old person. As another study has done (Youngwerth et al, 2011), we also demonstrate that the cost per day for a patient who dies during hospitalisation decreases for each age category above 65 years. Youngwerth et al (2011) explain the lower hospital cost of hospital deaths of older patients by the fact that older patients receive less aggressive care.…”

Section: Cost Of Hospital Deathsupporting

confidence: 76%

“…As another study has done (Youngwerth et al, 2011), we also demonstrate that the cost per day for a patient who dies during hospitalisation decreases for each age category above 65 years. Youngwerth et al (2011) explain the lower hospital cost of hospital deaths of older patients by the fact that older patients receive less aggressive care. Indeed, expensive, cure-focused hospital interventions may be inappropriate for elderly patients with complex comorbidities (Ireland, 2017).…”

Section: Cost Of Hospital Deathsupporting

confidence: 76%

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Profile of hospitalised elderly patients in Belgium—Analysis of factors affecting hospital costs

Foor

Senterre

Leclercq

et al. 2020

The Journal of the Economics of Ageing

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The objectives of this research are (i) to describe the medicoadministrative profiles of inpatients aged 65 and over and (ii) to determine the main hospitalisation-related diseases in order (iii) to assess and identify predictors influencing hospital costs and lengths of stay thanks to data collected in hospital administrative databases. Methods: The study is based on 45,469 hospital stays of inpatients aged 65 and over, with data collected among ten Belgian general hospitals in 2014. We conduct a linear regression in order to isolate factors influencing hospital costs and length of stay. Results: The mean length of stay (SD) for older patients (65+) is 11 days (18.17) and the mean hospitalisation cost (SD) €6706 (9422). The main predictor of hospital costs is the length of stay. A linear regression (n = 41,348, R 2 = 0.4520) reveals that predictors of a longer LOS for hospitalised older patients are: to be a male, to come from another hospital, to be sent to hospital by a general practitioner, to be admitted through the emergency department, to undergo a surgical procedure, to suffer from a major or extreme SOI or run a major or extreme mortality risk, to have a high score on the Charlson Comorbidity Index (CCI), to stay in an ICU, to belong to older age categories (85+), and to be institutionalised after discharge. Conclusion: Advanced age is by itself only a weak predictor of hospital cost, but it is a good predictor of a longer hospital stay.

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Section: Cost Of Hospital Deathsupporting

confidence: 76%

Section: Cost Of Hospital Deathsupporting

confidence: 76%

Profile of hospitalised elderly patients in Belgium—Analysis of factors affecting hospital costs

Foor

Senterre

Leclercq

et al. 2020

The Journal of the Economics of Ageing

View full text Add to dashboard Cite

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“…Only SOI scores of major+ (β=2.08 days; p<0.001; 95% CI, 1.03 to 3.13) and minor (β=−0.25 days; p=0.009; 95% CI, −0.43 to −0.06) were significant predictors of LOS relative to patients with moderate SOI scores. This represents a statistically and clinically significant finding, as an additional 2.08 days in LOS averages a costly $5,095 per day 23 , 24) . ASA scores ≥3 were not significant predictors of LOS, supporting previous research that also questioned the association between ASA scores and LOS 25) .…”

Section: Discussionmentioning

confidence: 82%

Using Illness Rating Systems to Predict Discharge Location Following Total Knee Arthroplasty

Rudasill¹,

Dattilo

Liu

et al. 2018

Knee Surg Relat Res

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PurposeTotal knee arthroplasty (TKA) is increasing in frequency and cost. Optimization of discharge location may reduce total expenditure while maximizing patient outcomes. Although preoperative illness rating systems—including the American Society for Anesthesiologists Physical Classification System (ASA), severity of illness scoring system (SOI), and Mallampati rating scale (MP)—are associated with patient morbidity and mortality, their predictive value for discharge location, length of stay (LOS), and total costs remains unclear.Materials and MethodsWe conducted a retrospective analysis of 677 TKA patients (550 primary and 127 revision) treated at a single institution. The influence of ASA, SOI, and MP scores on discharge locations, LOS, and total costs was assessed using multivariable regression analyses.ResultsNone of the systems were significant predictors of discharge location following TKA. SOI scores of major or higher (β=2.08 days, p<0.001) and minor (β=−0.25 days, p=0.009) significantly predicted LOS relative to moderate scores. Total costs were also significantly predicted by SOI scores of major or higher (β=$6,155, p=0.022) and minor (β=−$1,163, p=0.007).ConclusionsSOI scores may be harnessed as a predictive tool for LOS and total costs following TKA, but other mechanisms are necessary to predict discharge location.

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“…Length of stay (LOS) is a common outcome in research studies and is associated with health care cost (Gupta, Vashi, Lammersfeld, & Braun, 2011;Laky, Janda, Kondalsamy-Chennakesavan, Cleghorn, & Obermair, 2010;Youngwerth, Bartley, Yamashita, & Kutner, 2011). It also serves as a proxy quality indicator.…”

Section: Los In Oncology Unitsmentioning

confidence: 99%

Use of standardized nursing terminologies in electronic health records for oncology care

Tseng¹

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The purpose of this study was to identify the characteristics of cancer patients and the most frequently chosen nursing diagnoses, outcomes and interventions chosen for care plans from a large Midwestern acute care hospital. In addition the patients' outcome change scores and length of stay from the four oncology specialty units are investigated. Donabedian's structure-process-outcome model is the framework for this study. This is a descriptive retrospective study. The sample included a total of 2,237 patients admitted on four oncology units from June 1 to December 31, 2010. Data were retrieved from medical records, the nursing documentation system, and the tumor registry center. Demographics showed that 63% of the inpatients were female, 89% were white, 53 % were married and 26% were retired. Most patients returned home (82%); and 2% died in the hospital. Descriptive analysis identified that the most common nursing diagnoses for oncology inpatients were Acute Pain (78%), Risk for Infection (31%), and Nausea (26%). Each cancer patient had approximately 3.1 nursing diagnoses (SD=2.5), 6.3 nursing interventions (SD=5.1), and 3.7 nursing outcomes (SD=2.9). Characteristics of the patients were not found to be related to LOS (M=3.7) or outcome change scores for Pain Level among the patients with Acute Pain. Specifically, 88% of patients retained or improved outcome change scores. The most common linkage of NANDA-I, NOC, and NIC (NNN), a set of standardized nursing terminologies used in the study that represents nursing diagnoses, nursing-sensitive patient outcomes and nursing interventions, prospectively, was Acute Pain-Pain Level-Pain Management. Pain was the dominant concept in the nursing care provided to oncology patients. Risk for Infection was the most frequent nursing diagnosis in the Adult Leukemia and Bone Transplant Unit. Patients with both Acute Pain and Risk for Infection may differ among units; while the traditional study strategies rarely demonstrate this finding. Identifying the pattern of core diagnoses, interventions, and iii ACKNOWLEDGMENTS It is hard to express my gratitude to my PhD advisor, Professor Sue Moorhead. Without her inspirational guidance, her enthusiasm, her encouragement, her unselfish help, I could have never finished my doctoral work at the University of Iowa. My special thanks go also to the members of my advisory committee for their guidance and helpful discussions. I am grateful for the support from the hospital, Department of Nursing, the Cancer Center, and the Institute for Clinical and Translational Science. I want to thank the Nursing Informatics specialist, Teresa Clark in the Department of Nursing, the health information technicians, Julie Risinger and Tania Viet in the Cancer Center, the data analyst, Lisa Sturtz in the Joint Office for Compliance, and all the team that assisted me during the data collection process. I would like to thank Elena Perkhounkova, who trained me to manage the data for data analysis. I would like to express my thankfulness to Sharon Sweeney, my wa...

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Characteristics associated with higher cost per day or longer length of stay in hospitalized patients who died during the hospitalization or were discharged to hospice

Cited by 12 publications

References 23 publications

Profile of hospitalised elderly patients in Belgium—Analysis of factors affecting hospital costs

Profile of hospitalised elderly patients in Belgium—Analysis of factors affecting hospital costs

Using Illness Rating Systems to Predict Discharge Location Following Total Knee Arthroplasty

Use of standardized nursing terminologies in electronic health records for oncology care

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