Medical Journal of The Islamic Republic of Iran (MJIRI)
Iran University of Medical Sciences
Thu, Nov 21, 2024
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Volume 36, Issue 1 (1-2022)
Med J Islam Repub Iran 2022 |
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Toofan F, Hosseini S M, Alimohammadzadeh K, Jafari M, Bahadori M. Proposed Model of Management for Patients with Multi-Morbidity in Iranian Hospitals. Med J Islam Repub Iran 2022; 36 (1) :262-275
URL: http://mjiri.iums.ac.ir/article-1-7902-en.html
URL: http://mjiri.iums.ac.ir/article-1-7902-en.html
Firooz Toofan 
, Seyed Mojtaba Hosseini , Khalil Alimohammadzadeh , Mehrnoosh Jafari , Mohammadkarim Bahadori
, Seyed Mojtaba Hosseini , Khalil Alimohammadzadeh , Mehrnoosh Jafari , Mohammadkarim Bahadori
Department of Health Services Management, North Tehran Branch, Islamic Azad University, Tehran, Iran , sm_hosseini@iau-tnb.ac.ir
Abstract: (1267 Views)
Background: Today, multi-morbidity (MM), the presence of more than one disease in the same person at the same time, has been prevalent. This is while the healthcare delivery systems are formed based on a single-disease-oriented approach. Hence, this study intended to address presenting a model for the management of patients with multi-morbidity in Iranian hospitals.
Methods: This was a mixed-method study. The data was gathered from 54 semi-structured interviews with the participation of experts in inpatient care management who were purposefully selected. The qualitative data were analyzed using content analysis. The Interpretive Structural Modeling (ISM) via STATA and Excel software was exploited in the quantitative phase.
Results: The factors affecting the management of patients with multi-morbidity were identified in 26 main themes and 142 sub-themes, and ultimately, a model for improving the management of patients with multi-morbidity in Iranian hospitals at six different levels was offered. The "Comprehensive Health Care Information System (CHIS) and Electronic Health Record (EHR)" had the greatest influence and the lowest dependency. "Efforts to remove patients' confusion" had the highest dependency and the lowest influence. The results of employing the Cross Impact Matrix Multiplication Applied to Classification (MICMAC) analysis demonstrated that most of the variables are placed in the third group of linkage variables that have high driving power and dependence power.
Conclusion: Concerning the sophisticated needs of patients with multi-morbidity for the management of their clinical conditions, the presented model could be provided to policymakers and health care managers as a beneficial performance guideline for improving the quality of care.
Methods: This was a mixed-method study. The data was gathered from 54 semi-structured interviews with the participation of experts in inpatient care management who were purposefully selected. The qualitative data were analyzed using content analysis. The Interpretive Structural Modeling (ISM) via STATA and Excel software was exploited in the quantitative phase.
Results: The factors affecting the management of patients with multi-morbidity were identified in 26 main themes and 142 sub-themes, and ultimately, a model for improving the management of patients with multi-morbidity in Iranian hospitals at six different levels was offered. The "Comprehensive Health Care Information System (CHIS) and Electronic Health Record (EHR)" had the greatest influence and the lowest dependency. "Efforts to remove patients' confusion" had the highest dependency and the lowest influence. The results of employing the Cross Impact Matrix Multiplication Applied to Classification (MICMAC) analysis demonstrated that most of the variables are placed in the third group of linkage variables that have high driving power and dependence power.
Conclusion: Concerning the sophisticated needs of patients with multi-morbidity for the management of their clinical conditions, the presented model could be provided to policymakers and health care managers as a beneficial performance guideline for improving the quality of care.
Keywords: Multi-Morbidity, Inpatients, Hospital, Health Care Management, Interpretive-Structural Modeling (ISM)
Type of Study: Original Research |
Subject:
Health Management and Information Sciences
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