Evaluation of the ASSIGN open-source deterministic address-matching algorithm for allocating unique property reference numbers to general practitioner-recorded patient addresses.

INTRODUCTION: Linking places to people is a core element of the UK government's geospatial strategy. Matching patient addresses in electronic health records to their Unique Property Reference Numbers (UPRNs) enables spatial linkage for research, innovation and public benefit. Available algorithms are not transparent or evaluated for use with addresses recorded by health care providers. OBJECTIVES: To describe and quality assure the open-source deterministic ASSIGN address-matching algorithm applied to general practitioner-recorded patient addresses. METHODS: Best practice standards were used to report the ASSIGN algorithm match rate, sensitivity and positive predictive value using gold-standard datasets from London and Wales. We applied the ASSIGN algorithm to the recorded addresses of a sample of 1,757,018 patients registered with all general practices in north east London. We examined bias in match results for the study population using multivariable analyses to estimate the likelihood of an address-matched UPRN by demographic, registration, and organisational variables. RESULTS: We found a 99.5% and 99.6% match rate with high sensitivity (0.999,0.998) and positive predictive value (0.996,0.998) for the Welsh and London gold standard datasets respectively, and a 98.6% match rate for the study population.The 1.4% of the study population without a UPRN match were more likely to have changed registered address in the last 12 months (match rate: 95.4%), be from a Chinese ethnic background (95.5%), or registered with a general practice using the SystmOne clinical record system (94.4%). Conversely, people registered for more than 6.5 years with their general practitioner were more likely to have a match (99.4%) than those with shorter registration durations. CONCLUSIONS: ASSIGN is a highly accurate open-source address-matching algorithm with a high match rate and minimal biases when evaluated against a large sample of general practice-recorded patient addresses. ASSIGN has potential to be used in other address-based datasets including those with information relevant to the wider determinants of health.

Automated electronic reminders to facilitate primary cardiovascular disease prevention: randomised controlled trial.

BACKGROUND: Primary care databases contain cardiovascular disease risk factor data, but practical tools are required to improve identification of at-risk patients. AIM: To test the effects of a system of electronic reminders (the 'e-Nudge') on cardiovascular events and the adequacy of data for cardiovascular risk estimation. DESIGN OF STUDY: Randomised controlled trial. SETTING: Nineteen general practices in the West Midlands, UK. METHOD: The e-Nudge identifies four groups of patients aged over 50 years on the basis of estimated cardiovascular risk and adequacy of risk factor data in general practice computers. Screen messages highlight individuals at raised risk and prompt users to complete risk profiles where necessary. The proportion of the study population in the four groups was measured, as well as the rate of cardiovascular events in each arm after 2 years. RESULTS: Over 38 000 patients' electronic records were randomised. The intervention led to an increase in the proportion of patients with sufficient data who were identifiably at risk, with a difference of 1.94% compared to the control group (95% confidence interval [CI] = 1.38 to 2.50, P<0.001). A corresponding reduction occurred in the proportion potentially at risk but requiring further data for a risk estimation (difference = -3.68%, 95% CI = -4.53 to -2.84, P<0.001). No significant difference was observed in the incidence of cardiovascular events (rate ratio = 0.96, 95% CI = 0.85 to 1.10, P = 0.59). CONCLUSION: Automated electronic reminders using routinely collected primary care data can improve the adequacy of cardiovascular risk factor information during everyday practice and increase the visibility of the at-risk population.

Identifying individuals for primary cardiovascular disease prevention in UK general practice: priorities and resource implications.

Targeted cardiovascular disease prevention relies on risk-factor information held in primary care records. A risk algorithm, the 'e-Nudge', was applied to data from a population of >or=50-year-olds in 19 West Midlands practices, to identify those individuals at risk of cardiovascular disease. Altogether, 5.9% were identified aged 50-74 years at >or=20% 10-year risk based on existing data, and a further 26.4% were potentially at risk but had missing risk-factor information; 9.2% of patients aged over 50 years with established cardiovascular disease had at least one modifiable risk factor outside the audit target of the Quality and Outcomes Framework. Implications for resource allocation are discussed.

Identifying undiagnosed diabetes: cross-sectional survey of 3.6 million patients' electronic records.

BACKGROUND: Around 1% of the UK population has diabetes that is either undiagnosed or unrecorded on practice disease registers. AIM: To estimate the number of people in UK primary care databases with biochemical evidence of undiagnosed diabetes. To develop simple practice-based search techniques to support early recognition of diabetes. DESIGN OF STUDY: Cross-sectional survey of 3 630 296 electronic records. SETTING: Four hundred and eighty UK practices contributing to the QRESEARCH database. METHOD: Electronic searches to identify people with no diabetes diagnosis in one of two categories (A and B), using the most recently recorded blood glucose measurement: random blood glucose level >or=11.1 mmol/l or fasting blood glucose level >or=7.0 mmol/l (A); either a random or a fasting blood glucose level >or=7.0 mmol/l (B). An additional outcome measure was the proportion of the population with at least one blood glucose measurement in the record. RESULTS: The number (percentage) identified in category A was 3758 (0.10% of the total population); the number in category B was 32 785 (0.90%). Projected to a practice of 7000 patients, around eight patients have biochemical evidence of undiagnosed diabetes, and 68 have results suggesting the need for further follow-up. One-third of people aged over 40 years without diabetes have a blood glucose measurement in the past 2 years in their record. CONCLUSION: People with possible undiagnosed diabetes are readily identifiable in UK primary care databases through electronic searches using blood glucose data. People with borderline levels, who may benefit from interventions to reduce their risk of progression to diabetes, can also be identified using practice-based software.

Patient record access--the time has come.

There are now a number of systems across the world that enables patients to view their electronic health records. These include kiosks that have fingerprint authentication and also net-based access. The paper outlines the approach taken by the UK NHS explains the legal underpinning of access. Starting form the premise that record access is here to stay the paper outlines the research on benefits and risks of record access, concluding that, with simple precautions, record access is safe and affords many benefits to both patients and clinicians. It goes on to consider possible impacts of record access on the way records might be written as a co-produced document and emphasizes that national standards for record sharing need to be written.

Automated quality checks on repeat prescribing.

BACKGROUND: Good clinical practice in primary care includes periodic review of repeat prescriptions. Markers of prescriptions that may need review have been described, but manually checking all repeat prescriptions against the markers would be impractical. AIM: To investigate the feasibility of computerising the application of repeat prescribing quality checks to electronic patient records in United Kingdom (UK) primary care. DESIGN OF STUDY: Software performance test against benchmark manual analysis of cross-sectional convenience sample of prescribing documentation. SETTING: Three general practices in Greater Manchester, in the north west of England, during a 4-month period in 2001. METHOD: A machine-readable drug information resource, based on the British National Formulary (BNF) as the 'gold standard' for valid drug indications, was installed in three practices. Software raised alerts for each repeat prescribed item where the electronic patient record contained no valid indication for the medication. Alerts raised by the software in two practices were analysed manually. Clinical reaction to the software was assessed by semi-structured interviews in three practices. RESULTS: There was no valid indication in the electronic medical records for 14.8% of repeat prescribed items. Sixty-two per cent of all alerts generated were incorrect. Forty-three per cent of all incorrect alerts were as a result of errors in the drug information resource, 44% to locally idiosyncratic clinical coding, 8% to the use of the BNF without adaptation as a gold standard, and 5% to the inability of the system to infer diagnoses that, although unrecorded, would be 'obvious' to a clinical reading the record. The interviewed clinicians supported the goals of the software. CONCLUSION: Using electronic records for secondary decision support purposes will benefit from (and may require) both more consistent electronic clinical data collection across multiple sites, and reconciling clinicians' willingness to infer unstated but 'obvious' diagnoses with the machine's inability to do the same.