The proposed EHR-based approach will quantify the T2DM severity for primary care performance management and inform the methodology for measuring severity of other primary care-managed chronic conditions .
INTRODUCTION The increasing prevalence of type 2 diabetes mellitus ( T2DM ) presents a significant burden on affected individuals and healthcare systems internationally .
There is , however , no agreed vali date d measure to infer diabetes severity from electronic health records ( EHRs ) .
We aim to quantify T2DM severity and vali date it using clinical adverse outcomes .
Automatic identification of type 2 diabetes, hypertension, ischaemic heart disease, heart failure and their levels of severity from Italian General Practitioners' electronic medical records: a validation study
Objectives The Italian project MATRICE aim ed to assess how well cases of type 2 diabetes ( T2DM ) , hypertension , ischaemic heart disease ( IHD ) and heart failure ( HF ) and their levels of severity can be automatically extracted from the Health Search /CSD Longitudinal Patient Data base ( HSD ) . From the medical records of the general practitioners ( GP ) who volunteered to participate , cases were extracted by algorithms based on diagnosis codes , keywords , drug prescriptions and results of diagnostic tests . A r and om sample of identified cases was vali date d by interviewing their GPs . Setting HSD is a data base of primary care medical records . A panel of 12 GPs participated in this validation study . Participants 300 patients were sample d for each disease , except for HF , where 243 patients were assessed . Outcome measures The positive predictive value ( PPV ) was assessed for the presence/absence of each condition against the GP 's response to the question naire , and Cohen 's κ was calculated for agreement on the severity level . Results The PPV was 100 % ( 99 % to 100 % ) for T2DM and hypertension , 98 % ( 96 % to 100 % ) for IHD and 55 % ( 49 % to 61 % ) for HF . Cohen 's kappa for agreement on the severity level was 0.70 for T2DM and 0.69 for hypertension and IHD . Conclusions This study shows that individuals with T2DM , hypertension or IHD can be validly identified in HSD by automated identification algorithms . Automatic queries for levels of severity of the same diseases compare well with the corresponding clinical definitions , but some misclassification occurs . For HF , further research is needed to refine the current algorithm
The proposed EHR-based approach will quantify the T2DM severity for primary care performance management and inform the methodology for measuring severity of other primary care-managed chronic conditions .
INTRODUCTION The increasing prevalence of type 2 diabetes mellitus ( T2DM ) presents a significant burden on affected individuals and healthcare systems internationally .
There is , however , no agreed vali date d measure to infer diabetes severity from electronic health records ( EHRs ) .
We aim to quantify T2DM severity and vali date it using clinical adverse outcomes .