Quantifying Opportunities for Hospital Cost Control: Medical Device Purchasing and Patient Discharge Planning

被引:0
|
作者
Robinson, James C. [1 ]
Brown, Timothy T. [1 ]
机构
[1] Univ Calif Berkeley, Sch Publ Hlth, Berkeley, CA 94720 USA
基金
美国医疗保健研究与质量局;
关键词
ORTHOPEDIC-SURGERY; CARE; CONSUMERS; PAYMENTS; SYSTEMS; PRICES; VOLUME;
D O I
暂无
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Objectives To quantify the potential reduction in hospital costs from adoption of best local practices in supply chain management and discharge planning. Study Design We performed multivariate statistical analyses of the association between total variable cost per procedure and medical device price and length of stay, controlling for patient and hospital characteristics. Methods Ten hospitals in 1 major metropolitan area supplied patient-level administrative data on 9778 patients undergoing joint replacement, spine fusion, or cardiac rhythm management (CRM) procedures in 2008 and 2010. The impact on each hospital of matching lowest local market device prices and lowest patient length of stay (LOS) was calculated using multivariate regression analysis controlling for patient demographics, diagnoses, comorbidities, and implications. Results Average variable costs ranged from $11,315 for joint replacement to $16,087 for CRM and $18,413 for spine fusion. Implantable medical devices accounted for a large share of each procedure's variable costs: 44% for joint replacement, 39% for spine fusion, and 59% for CRM. Device prices and patient length-of-stay exhibited wide variation across hospitals. Total potential hospital cost savings from achieving best local practices in device prices and patient length of stay are 14.5% for joint replacement, 18.8% for spine fusion;, and 29.1% for CRM. Conclusions Hospitals have opportunities for cost reduction from adoption of best local practices in supply chain management and discharge planning.
引用
收藏
页码:E418 / E424
页数:7
相关论文
共 9 条
  • [1] Planning for the Discharge, not for Patient Self-Management at Home - An Observational and Interview Study of Hospital Discharge
    Flink, Maria
    Ekstedt, Mirjam
    INTERNATIONAL JOURNAL OF INTEGRATED CARE, 2017, 17
  • [2] Medical-surgical nurses' documentation of client teaching and discharge planning at a Jamaican hospital
    Abdul-Kareem, K.
    Lindo, J. L. M.
    Stennett, R.
    INTERNATIONAL NURSING REVIEW, 2019, 66 (02) : 191 - 198
  • [3] Patterns of patient coping following hospital discharge from medical and surgical units: A pilot study
    Hodgins, Marilyn J.
    Filiatreault, Sarah
    Keeping-Burke, Lisa
    Logan, Susan M.
    NURSING & HEALTH SCIENCES, 2020, 22 (01) : 118 - 125
  • [4] Australian public hospital inpatient satisfaction related to early patient involvement and shared decision-making in discharge planning
    Chia, Yong Yau Paul
    Ekladious, Adel
    INTERNAL MEDICINE JOURNAL, 2021, 51 (06) : 891 - 895
  • [5] Implementing community health worker-patient pairings at the time of hospital discharge: A randomized control trial
    Carter, Jocelyn
    Walton, Anne
    Donelan, Karen
    Thorndike, Anne
    CONTEMPORARY CLINICAL TRIALS, 2018, 74 : 32 - 37
  • [6] Measuring patient experiences in a Children's hospital with a medical clowning intervention: a case-control study
    Karisalmi, Nina
    Maenpaa, Katja
    Kaipio, Johanna
    Lahdenne, Pekka
    BMC HEALTH SERVICES RESEARCH, 2020, 20 (01)
  • [7] Implementation of a Clinical Pharmacy Specialist-Managed Telephonic Hospital Discharge Follow-Up Program in a Patient-Centered Medical Home
    Anderson, Sarah L.
    Marrs, Joel C.
    Vande Griend, Joseph P.
    Hanratty, Rebecca
    POPULATION HEALTH MANAGEMENT, 2013, 16 (04) : 235 - 241
  • [8] Mobile phone access and preferences among medical inpatients at an urban Canadian hospital for post-discharge planning: A pre-COVID-19 cross-sectional survey
    AboMoslim, Maryam
    Babili, Abdulaa
    Ghaseminejad-Tafreshi, Niloufar
    Manson, Matthew
    Fattah, Fanan
    El Joueidi, Samia
    Staples, John A.
    Tam, Penny
    Lester, Richard T.
    FRONTIERS IN DIGITAL HEALTH, 2022, 4
  • [9] Using machine learning to predict paediatric 30-day unplanned hospital readmissions: a case-control retrospective analysis of medical records, including written discharge documentation
    Zhou, Huaqiong
    Albrecht, Matthew A.
    Roberts, Pamela A.
    Porter, Paul
    Della, Philip R.
    AUSTRALIAN HEALTH REVIEW, 2021, 45 (03) : 328 - 337