Alys Havard1,2, Jo-Anne Manski-Nankervis3, Jill Thistlethwaite4,5, Benjamin Daniels4,6, Rimma Myton4, Karen Tu7,8, Kendal Chidwick4. 1. Alys Havard, NPS MedicineWise, PO Box 1147 , Strawberry Hills, NSW, 2012, Sydney, Australia. ahavard@nps.org.au. 2. Medicines Policy Research Unit, Centre for Big Data Research in Health, Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia. ahavard@nps.org.au. 3. Department of General Practice, University of Melbourne, Melbourne, VIC, Australia. 4. Alys Havard, NPS MedicineWise, PO Box 1147 , Strawberry Hills, NSW, 2012, Sydney, Australia. 5. University of Technology Sydney, Sydney, NSW, Australia. 6. Medicines Policy Research Unit, Centre for Big Data Research in Health, Faculty of Medicine, UNSW Sydney, Sydney, NSW, Australia. 7. Department of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada. 8. North York General Hospital, Toronto, Ontario, Canada.
Abstract
BACKGROUND: MedicineInsight is a database containing de-identified electronic health records (EHRs) from over 700 Australian general practices. It is one of the largest and most widely used primary health care EHR databases in Australia. This study examined the validity of algorithms that use information from various fields in the MedicineInsight data to indicate whether patients have specific health conditions. This study examined the validity of MedicineInsight algorithms for five common chronic conditions: anxiety, asthma, depression, osteoporosis and type 2 diabetes. METHODS: Patients' disease status according to MedicineInsight algorithms was benchmarked against the recording of diagnoses in the original EHRs. Fifty general practices contributing data to MedicineInsight met the eligibility criteria regarding patient load and location. Five were randomly selected and four agreed to participate. Within each practice, 250 patients aged ≥ 40 years were randomly selected from the MedicineInsight database. Trained staff reviewed the original EHR for as many of the selected patients as possible within the time available for data collection in each practice. RESULTS: A total of 475 patients were included in the analysis. All the evaluated MedicineInsight algorithms had excellent specificity, positive predictive value, and negative predictive value (above 0.9) when benchmarked against the recording of diagnoses in the original EHR. The asthma and osteoporosis algorithms also had excellent sensitivity, while the algorithms for anxiety, depression and type 2 diabetes yielded sensitivities of 0.85, 0.89 and 0.89 respectively. CONCLUSIONS: The MedicineInsight algorithms for asthma and osteoporosis have excellent accuracy and the algorithms for anxiety, depression and type 2 diabetes have good accuracy. This study provides support for the use of these algorithms when using MedicineInsight data for primary health care quality improvement activities, research and health system policymaking and planning.
BACKGROUND: MedicineInsight is a database containing de-identified electronic health records (EHRs) from over 700 Australian general practices. It is one of the largest and most widely used primary health care EHR databases in Australia. This study examined the validity of algorithms that use information from various fields in the MedicineInsight data to indicate whether patients have specific health conditions. This study examined the validity of MedicineInsight algorithms for five common chronic conditions: anxiety, asthma, depression, osteoporosis and type 2 diabetes. METHODS:Patients' disease status according to MedicineInsight algorithms was benchmarked against the recording of diagnoses in the original EHRs. Fifty general practices contributing data to MedicineInsight met the eligibility criteria regarding patient load and location. Five were randomly selected and four agreed to participate. Within each practice, 250 patients aged ≥ 40 years were randomly selected from the MedicineInsight database. Trained staff reviewed the original EHR for as many of the selected patients as possible within the time available for data collection in each practice. RESULTS: A total of 475 patients were included in the analysis. All the evaluated MedicineInsight algorithms had excellent specificity, positive predictive value, and negative predictive value (above 0.9) when benchmarked against the recording of diagnoses in the original EHR. The asthma and osteoporosis algorithms also had excellent sensitivity, while the algorithms for anxiety, depression and type 2 diabetes yielded sensitivities of 0.85, 0.89 and 0.89 respectively. CONCLUSIONS: The MedicineInsight algorithms for asthma and osteoporosis have excellent accuracy and the algorithms for anxiety, depression and type 2 diabetes have good accuracy. This study provides support for the use of these algorithms when using MedicineInsight data for primary health care quality improvement activities, research and health system policymaking and planning.
Entities:
Keywords:
Chronic disease; Electronic health records; Primary health care; Validation study
Authors: Michelle Greiver; Neil Drummond; Richard Birtwhistle; John Queenan; Anita Lambert-Lanning; Dave Jackson Journal: Can Fam Physician Date: 2015-01 Impact factor: 3.275