Evidence-Based Decision Support for Managing the Mentally III Inmate Population

被引:0
|
作者
Nardi, Michela [1 ]
Levinson, Carter [1 ]
Lawrence, Meredith [1 ]
Kamauff, Audrey [1 ]
Wilby, James [1 ]
Burge, William [1 ]
机构
[1] Univ Virginia, Dept Syst & Informat Engn, Charlottesville, VA 22903 USA
关键词
Criminal justice; Evidence-based systems engineering; Mental illness; Public safety; Recidivism;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The proportion of incarcerated individuals in the United States who suffer from mental illness has increased significantly in recent years, and the treatment of these individuals within criminal justice systems has come under increased scrutiny. In Charlottesville and Albemarle County, as well as in jurisdictions across the country, there is a lack of coordination between the criminal justice system and mental health services, which leads to a gap in fully understanding the tendencies and trends within the inmate population suffering from mental illness. In an effort ameliorate this lack of coordination, Albemarle/Charlottesville seeks to become the second community in the country (behind Camden, NJ) to have data from community mental health service providers and criminal justice agencies analyzed together and the first to do so with a focus on a released jail cohort. The Albemarle/Charlottesville criminal justice system is a leader in evidence-based decision making practices, which aim to improve public safety and reduce jail overcrowding by using data and datadriven analysis in order to determine how to best manage the inmate population suffering from mental illness. This project contributes to the evidence-based practice by investigating treatment linkages and efficacy of treatments for individuals whom the Albemarle-Charlottesville Regional Jail has referred for further mental health evaluation and treatment. The project required merging data, covering an 18-month study period from July 2015 through December 2016, held by seven participating agencies, including the Albemarle-Charlottesville Regional Jail, Charlottesville Police Department, Emergency Communications Center, Region 10 Community Services, Offender Aid and Restoration, Virginia Department of Corrections, and University of Virginia Hospital. By using data from the Brief Jail Mental Health Screener administered to individuals during jail intake, this project tracks the inmates that have been referred for mental health evaluation or treatment in order to identify any patterns or similarities between these individuals as they are diverted from, move through, or exit the jail system. Identifying information was used to merge disparate datasets, with a core dataset containing 3,848 unique inmates, on a secure computer and server. The merged datasets, stripped of personally identifiable information, were used for analysis to answer research questions including characteristic differences between referred and non-referred cohorts, the efficacy of Crisis Intervention Teams, and the effectiveness of treatment on recidivism reduction. Of the 3,848 inmates in the jail data, 2,151 inmates took a brief mental health screener and 23% of screened individuals were referred. Of this referred cohort, there was roughly a 44% treatment linkage rate of receiving mental health, substance abuse, or other services from Region 10. This unique combination of datasets that merges information from multiple sources lays the foundation for future collaborative efforts among the participating agencies and provides a framework for exploring additional research questions beyond the scope of this effort.
引用
收藏
页码:150 / 155
页数:6
相关论文
共 50 条
  • [31] Decision boxes for clinicians to support evidence-based practice and shared decision making: the user experience
    Anik Giguere
    France Légaré
    Roland Grad
    Pierre Pluye
    R Brian Haynes
    Michel Cauchon
    François Rousseau
    Juliana Alvarez Argote
    Michel Labrecque
    [J]. Implementation Science, 7
  • [32] Managing tooth wear with respect to quality of life: an evidence-based decision on when to intervene
    Mehta, Shamir B.
    Loomans, Bas A. C.
    van Sambeek, Roos M. F.
    Pereira-Cenci, Tatiana
    O'Toole, Saoirse
    [J]. BRITISH DENTAL JOURNAL, 2023, 234 (06) : 455 - 458
  • [33] Decision analysis in evidence-based decision making
    Tavakoli, M
    Davies, HTO
    Thomson, R
    [J]. JOURNAL OF EVALUATION IN CLINICAL PRACTICE, 2000, 6 (02) : 111 - 120
  • [34] Managing tooth wear with respect to quality of life: an evidence-based decision on when to intervene
    Shamir B. Mehta
    Bas A. C. Loomans
    Roos M. F. van Sambeek
    Tatiana Pereira-Cenci
    Saoirse O’Toole
    [J]. British Dental Journal, 2023, 234 : 455 - 458
  • [35] A new software architecture proposal for an evidence-based Decision Support System in dentistry
    Lo Giudice, Giuseppe
    Lizio, Angelo S.
    Lo Giudice, Roberto
    [J]. MINERVA DENTAL AND ORAL SCIENCE, 2021, 70 (01) : 7 - 14
  • [36] Exploring use of images in clinical articles for decision support in evidence-based medicine
    Antani, Sameer
    Demner-Fushman, Dina
    Li, Jiang
    Srinivasan, Balaji V.
    Thoma, George R.
    [J]. DOCUMENT RECOGNITION AND RETRIEVAL XV, 2008, 6815
  • [37] Evidence-based Decision Support for the Clinical Practice of Acupuncture: Data Mining Approaches
    Meng, Chang-rong
    Zhang, Hong-lai
    Zeng, Ling-feng
    Li, Zi-ping
    Huang, Jimmy
    Liang, Zhaohui
    [J]. 2013 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM), 2013,
  • [38] Improving Patient Safety Using Interactive, Evidence-Based Decision Support Tools
    Quinn, Margaret M.
    Mannion, Joseph
    [J]. JOINT COMMISSION JOURNAL ON QUALITY AND PATIENT SAFETY, 2005, 31 (12): : 678 - 683
  • [39] Development of an evidence-based adjuvant treatment decision support tool for endometrial cancer
    Vermij, L.
    Putter, H.
    Jobsen, J.
    Powell, M.
    de Boer, S.
    Leary, A.
    Fyles, A.
    Khaw, P.
    Lutgens, L.
    Jurgenliemk-Schulz, I.
    de Jong, M.
    Haverkort, D.
    Nout, R.
    Smit, V.
    Steyerberg, E.
    Bosse, T.
    Creutzberg, C.
    Horeweg, N.
    [J]. RADIOTHERAPY AND ONCOLOGY, 2023, 182 : S484 - S486
  • [40] Evidence-Based Decision Support for Neurological Diagnosis Reduces Errors and Unnecessary Workup
    Segal, Michael M.
    Williams, Marc S.
    Gropman, Andrea L.
    Torres, Alcy R.
    Forsyth, Rob
    Connolly, Anne M.
    El-Hattab, Ayman W.
    Perlman, Seth J.
    Samanta, Debopam
    Parikh, Sumit
    Pavlakis, Steven G.
    Feldman, Lynn K.
    Betensky, Rebecca A.
    Gospe, Sidney M., Jr.
    [J]. JOURNAL OF CHILD NEUROLOGY, 2014, 29 (04) : 487 - 492