Relapse prevention in drug addiction: addressing a messy problem by IS Action Research

Autor(en): Gerhardt, U.
Breitschwerdt, R.
Thomas, O. 
Stichwörter: Action Research; ALCOHOLISM; CANNABIS USE; CAREGIVERS; COMPUTER NETWORK; Computer Science; Computer Science, Artificial Intelligence; Drug addiction; HEALTH-CARE; Information systems; INFORMATION-SYSTEMS; INTERNET; Mobile technology; OUTCOMES; Relapse prevention; TECHNOLOGY; THERAPEUTIC-COMMUNITY
Erscheinungsdatum: 2015
Herausgeber: SPRINGER
Journal: AI & SOCIETY
Volumen: 30
Ausgabe: 1
Startseite: 31
Seitenende: 43
After primarily successful drug-dependency rehabilitation, relapses occur frequently. There is a tremendous lack of drug-specific outpatient psychotherapy and self-help groups especially in rural areas. Therefore, new approaches must be integrated to reduce relapse rates after inpatient addiction treatment, especially in case of drug dependence. The present paper aims to address this complex problem using an Action Research (AR) methodology. In addition to the systematic literature search, we show the results of interviews with patients and therapists as key stakeholders. After that, the iterative cyclical nature of AR reinforced our research, including the role of adequate ripening concerning inpatient care, the macroeconomic perspective on the insurance companies' patient allocation, the importance of external structure and skills in outpatient aftercare and specific impulses for dedicated information systems (ISs). We finally develop the hypothesis that a mobile technology-based IS could (a) be successfully implemented for patients recovering from drug dependence and (b) improve their outcome.
ISSN: 09515666
DOI: 10.1007/s00146-014-0544-9

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