Applying the planned care model to intimate partner violence

Area of Science:

• Public Health
• Health Services Research
• Domestic Violence Studies

Background:

• Intimate partner violence (IPV) is prevalent, leading to significant physical and mental health issues.
• Current health care approaches to IPV are often acute, lacking comprehensive, planned strategies.
• A systematic, collaborative approach is needed to improve care for IPV victims.

Purpose of the Study:

• To detail the steps for health organizations to implement the Planned Care Model (PCM) for responding to intimate partner violence (IPV).
• To emphasize the necessity of integrating IPV care into routine health services through a planned, systematic approach.
• To highlight the role of community collaboration and organizational commitment in managing IPV.

Main Methods:

• Adopting the Planned Care Model (PCM) framework for IPV response.
• Implementing practice design for patient identification, risk stratification, and care coordination.
• Utilizing evidence-based decision support, including validated screening tools and management guidelines.
• Incorporating patient self-management resources and robust data information systems for confidential registries and audits.
• Establishing process and outcome measures for monitoring and feedback.

Main Results:

• The PCM provides a structured framework for health organizations to manage IPV.
• Key components include practice design, evidence-based decision support, patient self-management, and data systems.
• Successful implementation requires organizational prioritization, staff training, and continuous monitoring of outcomes.
• Community collaboration with IPV agencies is critical for comprehensive care delivery.

Conclusions:

• Adapting the Planned Care Model (PCM) moves beyond traditional screen-identify-refer protocols for IPV.
• This approach necessitates embracing new partnerships and innovative evaluation methods.
• Managing IPV within the PCM framework involves addressing ethical considerations and designing multifactorial, cross-system evaluations.