Identifying the optimal search strategy for coronary heart disease patients in primary care electronic patient record systems

Identification of major cardiovascular events in patients with diabetes using primary care data

“…In addition, some of the evaluated drugs are used for other indications, such as venous thrombosis. Previous research suggested that nitrate prescriptions can be used to identify patients with CVD [ 13 , 14 , 23 ]. Our study confirms that nitrates indeed have the highest specificity to identify such patients but the sensitivity is lower than found in the other studies (24 % vs 47–55 %) [ 13 , 14 ].…”

Identifying individuals with multiple sclerosis in an electronic medical record

“…Our algorithm performed with similar or better sensitivity and PPV than previously described algorithms for identifying other conditions in primary care EMRs. [16][17][18][19][20] The CPP search algorithm performed well as documentation of MS in the CPP is generally good, likely because of the significant impact of the disease. However, CPP data quality is variable and data may be incomplete or not kept up to date.…”

“…The increasing use of primary care electronic medical records (EMRs) provides a potential source of detailed clinical information in a large group of patients with MS. EMRs provide details on the complex management of patients in a more detailed manner than administrative data. 15 There have been several studies identifying different patient groups in EMRs, including those with diabetes mellitus, 16 hypertension, 17 ischemic heart disease 18,19 and congestive heart failure 20 to aid in monitoring the management of these complex diseases. Similarly, the ability to identify patients with MS in primary care EMRs could enable more detailed assessment of patterns of care, including symptom onset to diagnosis and screening for and management of comorbid conditions compared with control patients.…”

Usability: a neglected theme in informatics

“…9 Diagnostic codes only identified 81% of people with heart disease; the implications of this study may be that pay-for-performance data (the Quality and Outcomes Framework) based on a single disease code may be too simplistic. These findings reinforce Brown and Warmington's work on using data quality probes to improve data quality; 10 and show that we always need to be mindful of the Laws of Informatics (First law -Data can only be used for the purpose for which it is recorded; Second law -You can break the first law if you fully understand the context of the data recording).…”