EMR-based prediction models developed and deployed in the HIV care continuum: a systematic review

EMR-based prediction models are being introduced and can be used in clinical decision support to prioritize high-risk patients, target personalized care, and improve patient outcomes.
Published in Healthcare & Nursing
EMR-based prediction models developed and deployed in the HIV care continuum: a systematic review
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Electronic medical record-based prediction models developed and deployed in the HIV care continuum: a systematic review - Discover Health Systems

Objective To assess the methodological issues in prediction models developed using electronic medical records (EMR) and their early-stage clinical impact on the HIV care continuum. Methods A systematic search of entries in PubMed and Google Scholar was conducted between January 1, 2010, and January 17, 2022, to identify studies developing and deploying EMR-based prediction models. We used the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies), PROBAST (Prediction Model Risk of Bias Assessment Tool), and TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) statements to assess the methodological issues. In addition, we consulted reporting guidelines for early-stage clinical evaluation of decision support systems to assess the clinical impact of the models. Results The systematic search yielded 35 eligible articles: 24 (68.6%) aimed at model development and 11 (31.4%) for model deployment. The majority of these studies predicted an individual’s risk of carrying HIV (n = 12/35, 34.3%), the risk of interrupting HIV care (n = 9/35), and the risk of virological failure (n = 7/35). The methodological assessment for those 24 studies found that they were rated as high risk (n = 6/24), some concerns (n = 14/24), and a low risk of bias (n = 4/24). Several studies didn’t report the number of events (n = 14/24), missing data management (n = 12/24), inadequate reporting of statistical performance (n = 18/24), or lack of external validation (n = 21/24) in their model development processes. The early-stage clinical impact assessment for those 9/11-deployed models showed improved care outcomes, such as HIV screening, engagement in care, and viral load suppression. Conclusions EMR-based prediction models have been developed, and some are practically deployed as clinical decision support tools in the HIV care continuum. Overall, while early-stage clinical impact is observed with those deployed models, it is important to address methodological concerns and assess their potential clinical impact before widespread implementation. Systematic review registration: PROSPERO CRD42023454765.

Study findings and insights

1.      The Role of EMRs in HIV Care

Using electronic medical records (EMRs) to predict patient outcomes is an exciting development in HIV care. EMRs are digital records with information about a patient's medical history, treatments, and test results. By analyzing this data, healthcare providers can identify patterns and predict when patients might be at risk of missing appointments, stopping treatment, or facing other challenges.

2.      Benefits of EMR-Based Predictions

This approach has already shown promise. For example, some hospitals have used EMR-based models to increase the number of people being tested for HIV and to ensure those who test positive get connected to care faster. In some cases, EMR tools have helped increase monthly HIV screenings from a few people to hundreds and improve the number of patients staying in care.

3.      Challenges of Implementing EMR Models

However, there are still challenges to making these models work well. For instance, missing information in a patient’s record can lead to biased predictions. Also, most of the current studies have been done in high-income countries, so it’s not yet clear how well these models will work in other settings.

4.      Future Potential of EMR-Based Tools

Despite these hurdles, the potential of EMR-based prediction models is significant. With ongoing improvements and wider testing, these tools could transform HIV care, making it more personalized and efficient. By understanding when and where patients need the most support, healthcare providers can help more people stay engaged in their treatment, reduce the spread of HIV, and ultimately improve the quality of life for those living with the virus.

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