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Related Experiment Video

Updated: Sep 17, 2025

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform
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Collaborative development of a rules-based electronic health record algorithm for Hospital-at-Home eligibility.

Tsai-Ling Liu1, Timothy C Hetherington1,2, Marc Kowalkowski1,3

  • 1Center for Health System Sciences, Atrium Health, Charlotte, North Carolina, USA.

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|June 29, 2025
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Summary
This summary is machine-generated.

Identifying patients for hospital at Home (HaH) is difficult. A new rules-based algorithm (RBA) using electronic health records (EHR) improves patient selection for efficient HaH operations.

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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.