Collaborative development of a rules-based electronic health record algorithm for Hospital-at-Home eligibility

Area of Science:

• Health Informatics
• Healthcare Management
• Clinical Operations

Background:

• Hospital at Home (HaH) models face challenges in patient identification due to large patient volumes, fluctuating conditions, and narrow eligibility windows.
• Efficient patient selection is critical for the successful implementation and operation of HaH programs.

Purpose of the Study:

• To develop and implement a rules-based algorithm (RBA) to enhance the identification of suitable patients for Hospital at Home (HaH) programs.
• To leverage electronic health record (EHR) data for improved accuracy and efficiency in HaH patient selection.

Main Methods:

• Development of a rules-based algorithm (RBA) utilizing electronic health record (EHR) data.
• Incorporation of clinician feedback to refine the algorithm and align with clinical workflows and analytics resources.

Main Results:

• The study successfully developed an RBA to improve the identification of patients eligible for HaH.
• Algorithm adjustments based on clinical input enhanced its alignment with operational needs and clinical workflows.

Conclusions:

• Interdisciplinary collaboration is essential for optimizing analytics in emerging care models like HaH.
• The developed RBA demonstrates the potential of data analytics to improve efficiency and patient selection for Hospital at Home services.