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Proposed Model of Management for Patients with Multi-Morbidity in Iranian Hospitals
Firooz Toofan1, Seyed Mojtaba Hosseini1, Khalil Alimohammadzadeh1,2
1Department of Health Services Management, North Tehran Branch, Islamic Azad University, Tehran, Iran.
Insights
Managing multi-morbidity (MM) requires a new approach. This study presents a six-level model for Iranian hospitals, emphasizing integrated health information systems for better patient care.
Area of Science:
- Healthcare Management
- Public Health
- Clinical Informatics
Background:
- Multi-morbidity (MM), the simultaneous presence of multiple diseases in one individual, is increasingly prevalent.
- Current healthcare systems are largely designed around single-disease management, creating a gap in care for MM patients.
- This disparity necessitates innovative models for managing complex patient needs within existing healthcare structures.
Purpose of the Study:
- To develop and present a comprehensive management model for patients with multi-morbidity in Iranian hospitals.
- To identify key factors influencing the effective management of multi-morbid patients.
- To provide a framework for improving healthcare delivery for individuals with multiple chronic conditions.
Main Methods:
- A mixed-methods approach combining qualitative content analysis of 54 expert interviews with quantitative Interpretive Structural Modeling (ISM).
- Qualitative data explored challenges and facilitators in MM patient management.
- ISM, utilizing STATA and Excel, analyzed the relationships between identified factors, including MICMAC analysis.
Main Results:
- A six-level model for managing multi-morbidity in Iranian hospitals was developed, encompassing 26 themes and 142 sub-themes.
- The Comprehensive Health Care Information System (CHIS) and Electronic Health Record (EHR) emerged as highly influential with low dependency.
- "Efforts to remove patients' confusion" showed high dependency but low influence.
- MICMAC analysis indicated most variables possess high driving power and dependence, suggesting interconnectedness.
Conclusions:
- The proposed model offers a structured guideline for policymakers and healthcare managers to enhance the quality of care for multi-morbid patients.
- Addressing the complex needs of MM patients requires integrated systems and clear communication strategies.
- The model provides a foundation for optimizing healthcare delivery in resource-constrained settings facing high MM prevalence.
Abstract:
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.
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