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Longitudinal adoption rates of complex decision support tools in primary care
  1. Lauren McCullagh1,
  2. Devin Mann2,
  3. Lisa Rosen1,
  4. Joseph Kannry3,
  5. Thomas McGinn1
  1. 1Department of Medicine, Hofstra North Shore-LIJ School of Medicine, Manhasset, New York, USA
  2. 2Department of Medicine, Section of Preventive Medicine and Epidemiology, Boston University School of Medicine, Boston, Massachusetts, USA
  3. 3Department of Medicine, Division of General Internal Medicine, Mount Sinai School of Medicine, New York, New York, USA
  1. Correspondence to Dr Lauren McCullagh
    Department of Medicine, Hofstra North Shore-LIJ School of Medicine, 300 Community Drive, Manhasset, NY 11201, USA; lmccullagh{at}nshs.edu

Extract

Translating research findings into practice promises to standardise care. Translation includes the integration of evidence-based guidelines at the point of care, discerning the best methods to disseminate research findings and models to sustain the implementation of best practices. By applying usability testing to clinical decision support (CDS) design, overall adoption rates of 60% can be realised. What has not been examined is how long adoption rates are sustained and the characteristics associated with long-term use. We conducted secondary analysis to decipher the factors impacting sustained use of CDS tools. This study was a secondary data analysis from a clinical trial conducted at an academic institution in New York City. Study data was deidentified patients’ electronic health records (EHR). The trial was to test the implementation of an integrated clinical prediction rule (iCPR) into the EHR. The primary outcome variable was iCPR tool acceptance of the tool. iCPR tool completion and iCPR smartest completion were additional outcome variables of interest. The secondary aim was to examine user characteristics associated with iCPR tool use in later time periods. Characteristics of interest included age, resident year, use of electronic health records (yes/no) and use of best practice alerts (BPA) (yes/no). Generalised linear mixed models (GLiMM) were used to compare iCPR use over time for each outcome of interest: namely, iCPR acceptance, iCPR completion and iCPR smartset completion. GLiMM was also used to examine resident characteristics associated with iCPR tool use in later time periods; specifically, intermediate and long-term (ie, 90+ days). The tool was accepted, on average, 82.18% in the first 90 days (short-term period). The use decreases to 56.07% and 45.61% in intermediate and long-term time periods, respectively. There was a significant association between iCPR tool completion and time periods (p<0.0001). There was no significant difference in iCPR tool completion between resident encounters in the intermediate and long-term periods (p<0.6627). There was a significant association between iCPR smartset completion and time periods (p<0.0021). There were no significant associations between iCPR smartest completion and any of the four predictors of interest. We examined the frequencies of components of the iCPR tool being accepted over time by individual clinicians. Rates of adoption of the different components of the tool decreased substantially over time. The data suggest that over time and prolonged exposure to CDS tools, providers are less likely to utilise the tool. It is not clear if it is fatigue with the CDS tool, acquired knowledge of the clinical prediction rule, or gained clinical experience and gestalt that are influencing adoption rates. Further analysis of individual adoption rates over time and the impact it has on clinical outcomes should be conducted.

  • PRIMARY CARE
  • INTERNAL MEDICINE

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Extract

Translating research findings into practice promises to standardise care. Translation includes the integration of evidence-based guidelines at the point of care, discerning the best methods to disseminate research findings and models to sustain the implementation of best practices. By applying usability testing to clinical decision support (CDS) design, overall adoption rates of 60% can be realised. What has not been examined is how long adoption rates are sustained and the characteristics associated with long-term use. We conducted secondary analysis to decipher the factors impacting sustained use of CDS tools. This study was a secondary data analysis from a clinical trial conducted at an academic institution in New York City. Study data was deidentified patients’ electronic health records (EHR). The trial was to test the implementation of an integrated clinical prediction rule (iCPR) into the EHR. The primary outcome variable was iCPR tool acceptance of the tool. iCPR tool completion and iCPR smartest completion were additional outcome variables of interest. The secondary aim was to examine user characteristics associated with iCPR tool use in later time periods. Characteristics of interest included age, resident year, use of electronic health records (yes/no) and use of best practice alerts (BPA) (yes/no). Generalised linear mixed models (GLiMM) were used to compare iCPR use over time for each outcome of interest: namely, iCPR acceptance, iCPR completion and iCPR smartset completion. GLiMM was also used to examine resident characteristics associated with iCPR tool use in later time periods; specifically, intermediate and long-term (ie, 90+ days). The tool was accepted, on average, 82.18% in the first 90 days (short-term period). The use decreases to 56.07% and 45.61% in intermediate and long-term time periods, respectively. There was a significant association between iCPR tool completion and time periods (p<0.0001). There was no significant difference in iCPR tool completion between resident encounters in the intermediate and long-term periods (p<0.6627). There was a significant association between iCPR smartset completion and time periods (p<0.0021). There were no significant associations between iCPR smartest completion and any of the four predictors of interest. We examined the frequencies of components of the iCPR tool being accepted over time by individual clinicians. Rates of adoption of the different components of the tool decreased substantially over time. The data suggest that over time and prolonged exposure to CDS tools, providers are less likely to utilise the tool. It is not clear if it is fatigue with the CDS tool, acquired knowledge of the clinical prediction rule, or gained clinical experience and gestalt that are influencing adoption rates. Further analysis of individual adoption rates over time and the impact it has on clinical outcomes should be conducted.

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