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Identifying complex patients using Adjusted Clinical Groups risk stratification tool.

Shelley-Ann M Girwar1, Jozefine C Verloop, Marta Fiocco

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Summary
This summary is machine-generated.

A new prediction model using the Johns Hopkins ACG System effectively identifies patients with complex care needs in primary care. While accurate in discrimination, the model requires calibration improvements for precise overestimation.

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Area of Science:

  • Health Services Research
  • Predictive Modeling
  • Primary Care Analytics

Background:

  • Identifying patients with complex care needs is crucial for effective healthcare management.
  • Primary care data offers a rich source for identifying complex patients.
  • A mismatch in care can exacerbate health issues for complex patients.

Purpose of the Study:

  • To develop and validate an efficient primary care-based method for identifying patients with complex care needs.
  • To explore the utility of the Johns Hopkins ACG System and its Aggregated Diagnosis Group (ADG) categories for this identification.
  • To create a prediction model for complex care needs using exclusively primary care data.

Main Methods:

  • A retrospective cross-sectional study design was employed, integrating general practitioners' electronic health records with hospital data.
  • A prediction model was developed using a primary care population (n=105,345), incorporating age, sex, and 32 ADGs.
  • External validation was performed on a separate primary care cohort (n=30,793), assessing discrimination (C statistic) and calibration.

Main Results:

  • The developed model demonstrated strong discriminatory ability, distinguishing between complex and non-complex patients with a C statistic of 0.9 (95% CI, 0.88-0.92).
  • The calibration plot indicated that the model tends to overestimate the prevalence of complex patients.
  • The Johns Hopkins ACG System, specifically ADGs, proved effective in identifying complex care needs.

Conclusions:

  • The ACG System is a valuable tool for identifying complex care needs in primary care settings.
  • The developed prediction model shows high discriminatory power for complex patients.
  • Further refinement is needed to improve the model's calibration accuracy.