Factors Associated with Long-Stays in an Italian Psychiatric Intensive Treatment Facility: 1-Year Retrospective Observational Analysis

Psychiatr Q. 2019 Mar;90(1):185-196. doi: 10.1007/s11126-018-9616-1.

Abstract

Psychiatric Intensive Treatment Facilities (PITF) are health inpatient settings for patients affected by sub-acute psychiatric disorders with impaired personal and social functioning. The aim of this study is to analyse the demographic and clinical variables related to long-stays in an Italian PITF in order to highlight the risk factors for stay lengthening. We retrospectively collected the selected variables from all patients and their stays in a PITF from 1 to 11-2016 to 31-10-2017. We divided the stays according to the median of duration, ≤29 and > 29 days, to compare selected variables in the two groups of stay length. Patients hospitalized for >29 days more frequently presented "Self-neglect", nursing diagnosis NANDA-I, and needed economic social service support. Multiple linear regression revealed that the presence of some variables as "many medical consultations", "economic social service support", "clinical interviews extended to institutional figures" were statistically significantly associated with an increased stay duration, suggesting that both clinical severity and difficult economic conditions were associated with the lengthening of stay. The knowledge of these factors can contribute to improve psychiatric treatments, reducing potential risk conditions for patient institutional dependence.

Keywords: Duration of stay; Nursing diagnoses; Psychiatric intensive treatment facility; Rehabilitative programs.

Publication types

  • Observational Study

MeSH terms

  • Adult
  • Aged
  • Female
  • Hospitals, Psychiatric / statistics & numerical data*
  • Humans
  • Italy
  • Length of Stay / statistics & numerical data*
  • Male
  • Mental Disorders / therapy*
  • Middle Aged
  • Outcome and Process Assessment, Health Care / statistics & numerical data*
  • Residential Treatment / statistics & numerical data*
  • Retrospective Studies
  • Risk Factors