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Hospitalization Patterns over 30 Years Across a Statewide System of Public Mental Health Hospitals: Readmission Predictors, Optimal Follow-Up Period, Readmission Clusters and Individuals with Statistically Significant High Healthcare Utilization

  • Alan ShaferEmail author
Original Paper
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Abstract

Four related hospital utilization questions (optimal follow-up period, predictors of readmission, definition of individuals with statistically significant high healthcare utilization, and patterns of readmissions) were examined using data for 491,094 hospital discharges for 250,091 patients across a statewide public mental health hospital system for 30 years (1987 to 2016). Using survival analysis, the first quartile of the survival time, the time when 25% of the entire population of discharges had a readmission was 229 days. Using observed readmissions, rather than the population as in survival analysis, revealed that 50% of all observed readmissions occurred by 222 days. Both suggest that using a one year observation period for determining high utilization may be reasonable. Major predictors of readmission were diagnoses of schizophrenia (OR = 2.11) or bipolar disorder (OR = 1.57) as well as total number of previous discharges (OR = 1.23). Statistically significant z scores (p < .01) were used to determine annual (3 or more discharges) and lifetime (7 or more discharges) criteria for individuals with statistically significant high healthcare utilization that were somewhat lower than in previous research. Cluster analysis of all readmissions revealed four relatively distinct clusters of patients: short stay-quick readmission, extremely long stay, long time in community between readmissions and frequent readmissions. While no cluster corresponded exactly with the annual statistically significant high healthcare utilization criteria, the frequent readmission cluster was somewhat similar to the lifetime statistically significant high healthcare utilization criteria with 46% of this cluster’s patients having 7 or more discharges.

Keywords

Psychiatric hospital readmission High healthcare utilization Readmission patient clusters Predictors of hospital readmission 

Notes

Acknowledgements

In memory of my colleague James Cooley who championed the analysis of high healthcare utilization.

Compliance with Ethical Standards

Conflict of Interest

The author received no funding for this study and has no conflict of interest to declare.

Ethical Approval

All research was conducted in accordance with the ethical standards of the institutional review board and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Data for this study was reviewed and approved by the Texas Department of State Health Services Institutional Review Board #2. Studies using only pre-existing administrative data do not require formal consent.

References

  1. 1.
    Lewis T, Joyce P. The new revolving-door patients: results from a national cohort of first admissions. Acta Psychiatr Scand. 1990;82:130–5.CrossRefGoogle Scholar
  2. 2.
    Gastal F, Andreoli S, Quintana M, Almeida Gameiro M, Leite S, McGrath J. Predicting the revolving door phenomenon among patients with schizophrenic, affective disorders and non-organic psychoses. Rev Saude Publica. 2000;34:280–5.CrossRefGoogle Scholar
  3. 3.
    Soril L, Leggett L, Lorenzetti D, Noseworthy T, Clement F. Characteristics of frequent users of the emergency department in the general adult population: a systematic review of international healthcare systems. Health Policy. 2016;120:452–61.CrossRefGoogle Scholar
  4. 4.
    Scott J, Strickland A, Warner K, Dawson P. Frequent callers to and users of emergency medical systems: a systematic review. Emerg Med J. 2014;31:684–91.CrossRefGoogle Scholar
  5. 5.
    Tricco A, Antony J, Ivers N, Ashoor H, Khan P, Blondal E, et al. Effectiveness of quality improvement strategies for coordination of care to reduce use of health care services: a systematic review and meta-analysis. CMAJ. 2014;186:E568–78.CrossRefGoogle Scholar
  6. 6.
    Jiang H, Barrett M, Sheng M. Characteristics of hospital stays for nonelderly Medicaid super-utilizers, 2012: Healthcare Cost and Utilization Project (HCUP) statistical brief #184. Agency for Healthcare Research and Quality: Rockville; 2012.Google Scholar
  7. 7.
    Jiang H, Weiss A, Barrett M, Sheng M. Characteristics of hospital stays for super-utilizers by payer, 2012: Healthcare Cost and Utilization Project (HCUP) statistical brief #190. Rockville: Agency for Healthcare Research and Quality; 2012.Google Scholar
  8. 8.
    Hadley T, Culhane D, McGurrin M. Identifying and tracking “heavy users” of acute psychiatric inpatient services. Admin Pol Ment Health. 1992;19:279–90.CrossRefGoogle Scholar
  9. 9.
    Hadley T, McGurrin M, Pulice R, Holohean E. Using fiscal data to identify heavy service users. Psychiatr Q. 1990;61:41–8.CrossRefGoogle Scholar
  10. 10.
    Johnson T, Rinehart D, Durfee J, Brewer D, Batal H, Blum J, et al. For many patients who use large amounts of health care services, the need is intense yet temporary. Health Aff (Millwood). 2015;34:1312–9.CrossRefGoogle Scholar
  11. 11.
    Hudson C. Patterns of acute psychiatric hospitalization in Massachusetts. Admin Pol Ment Health. 2005;32:221–40.CrossRefGoogle Scholar
  12. 12.
    Soeken K, Prescott P, Herron D, Creasia J. Predictors of hospital readmission. A meta-analysis. Eval Health Prof. 1991;14:262–81.CrossRefGoogle Scholar
  13. 13.
    Donisi V, Tedeschi F, Wahlbeck K, Haaramo P, Amaddeo F. Pre-discharge factors predicting readmissions of psychiatric patients: a systematic review of the literature. BMC Psychiatry. 2016;16:449.CrossRefGoogle Scholar
  14. 14.
    Sfetcu R, Musat S, Haaramo P, Ciutan M, Scintee G, Vladescu C, et al. Overview of post-discharge predictors for psychiatric re-hospitalisations: a systematic review of the literature. BMC Psychiatry. 2017;17:227.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Texas HHSC Behavioral Health Services Decision SupportAustinUSA

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