Informatics in Primary Care最新文献

Background: Support is often considered an important factor for successful implementation and realising the benefits of health information technology (HIT); however, there is a dearth of research on support and theoretical frameworks to characterise it.

Objective: To develop and present a comprehensive, holistic, framework for characterising enduser support that can be applied to various settings and types of information systems.

Method: Scoping review of the medical informatics and information systems literature.

Results: A theoretical framework of end-user support is presented. It includes the following facets: support source, location of support, support activities, and perceived characteristics of support and support personnel.

Conclusion: The proposed framework may be a useful tool for describing and characterising enduser support for HIT. it may also be used by decision makers and implementation leaders for planning purposes.

General practitioner (GP) computing has its origins in the 1970s when the benefits of clinical coding and prescribing were demonstrated. During the early 1980s Dr James Read, working with Abies Informatics Ltd, developed the eponymous Read Codes, which were broader and more comprehensive than other schemes, yet intuitive and easy to use. In 1988 a joint working party of the Royal College of General Practitioners (RCGP) and the British Medical Association (BMA) recommended that the Read Codes be adopted nationally. The Read Codes have been used by almost all GPs in the UK since the mid-1990s. Many developments in general practice, including GP fundholding (where GPs held the budgets to commission elective care for their patients), the Quality and Outcomes Framework (QOF - pay for performance for improving chronic disease management) and GP commissioning (the current NHS reform in which primary care leads commissioning of services for their patients) would have been impossible without all GPs using a common clinical coding scheme. Systematized Nomenclature For Medicine - Clinical Terms (SNOMED CT) is a merger of the Read Codes with SNOMED RT - the original SNOMED reference terminology developed by the American College of Pathologists.

Background: The quality of electronic medical record (EMR) data is known to be problematic; research on improving these data is needed.

Objective: The primary objective was to explore the impact of using a data entry clerk to improve data quality in primary care EMRs. The secondary objective was to evaluate the feasibility of implementing this intervention.

Methods: We used a before and after design for this pilot study. The participants were 13 community based family physicians and four allied health professionals in Toronto, Canada. Using queries programmed by a data manager, a data clerk was tasked with re-entering EMR information as coded or structured data for chronic obstructive pulmonary disease (COPD), smoking, specialist designations and interprofessional encounter headers. We measured data quality before and three to six months after the intervention. We evaluated feasibility by measuring acceptability to clinicians and workload for the clerk.

Results: After the intervention, coded COPD entries increased by 38% (P = 0.0001, 95% CI 23 to 51%); identifiable data on smoking categories increased by 27% (P = 0.0001, 95% CI 26 to 29%); referrals with specialist designations increased by 20% (P = 0.0001, 95% CI 16 to 22%); and identifiable interprofessional headers increased by 10% (P = 0.45, 95 CI -3 to 23%). Overall, the intervention was rated as being at least moderately useful and moderately usable. The data entry clerk spent 127 hours restructuring data for 11 729 patients.

Conclusions: Utilising a data manager for queries and a data clerk to re-enter data led to improvements in EMR data quality. Clinicians found this approach to be acceptable.

Background: Personalised medicine involves customising management to meet patients' needs. In chronic kidney disease (CKD) at the population level there is steady decline in renal function with increasing age; and progressive CKD has been defined as marked variation from this rate of decline.

Objective: To create visualisations of individual patient's renal function and display smoothed trend lines and confidence intervals for their renal function and other important co-variants.

Method: Applying advanced pattern recognition techniques developed in biometrics to routinely collected primary care data collected as part of the Quality Improvement in Chronic Kidney Disease (QICKD) trial. We plotted trend lines, using regression, and confidence intervals for individual patients. We also created a visualisation which allowed renal function to be compared with six other covariants: glycated haemoglobin (HbA1c), body mass index (BMI), BP, and therapy. The outputs were reviewed by an expert panel.

Results: We successfully extracted and displayed data. We demonstrated that estimated glomerular filtration (eGFR) is a noisy variable, and showed that a large number of people would exceed the 'progressive CKD' criteria. We created a data display that could be readily automated. This display was well received by our expert panel but requires extensive development before testing in a clinical setting.

Conclusions: It is feasible to utilise data visualisation methods developed in biometrics to look at CKD data. The criteria for defining 'progressive CKD' need revisiting, as many patients exceed them. Further development work and testing is needed to explore whether this type of data modelling and visualisation might improve patient care.

Background: Chronic neuropathic pain is a common condition which is challenging to treat. Many people with neuropathic pain are managed in the community, so primary care records may allow more appropriate subjects to be recruited for clinical studies.

Objective: We investigated whether primary care records can be used to identify patients with diseases associated with neuropathic pain.

Method: We analysed demographic, diagnostic and prescribing data from over 100 000 primary care electronic patient records in one part of London, UK.

Results: The prevalence of diagnoses associated with chronic neuropathic pain was 13 per 1000, with the elderly, women and white patients experiencing the greatest burden of disease.

Conclusion: Computerised health records offer an excellent opportunity to improve the identification of patients for clinical research in complex conditions like chronic neuropathic pain. To make full use of data from these records, standardisation of clinical coding and consensus on diagnostic criteria are needed.

Background: Increased electronic prescribing (eRx) rates have the potential to prevent errors, increase patient safety, and curtail fraud. US Federal meaningful use guidelines require at least a 40% electronic prescribing rate.

Objective: We evaluated eRx rates among primary care providers in New York City in order to determine trends as well as identify any obstacles to increased eRx rates required by meaningful use guidelines.

Methods: The data we analysed included automatic electronic data transmissions from providers enrolled in the Primary Care Information Project (PCIP) from 1 January 2009 to 1 July 2010 and follow-up telephone calls to a subset of these providers to identify potential barriers to increased eRx usage.

Results: Over the course of the study, these providers increased the eRx rate from 12.9 to 27.5%, with an average rate of 24.1%. Conversations with providers identified their perceived barriers to increased eRx use as primarily patient preference for paper prescriptions and a belief that many pharmacies do not accept eRx.

Conclusions: The data gathered from our providers indicate that there is an increasing trend in the eRx rate to 27.5% by July 2010, but still short of the 40% meaningful use level. However, obstacles to increased rates remain primarily providers' belief that many patients prefer paper prescriptions and many pharmacies are not yet prepared to accept electronic prescriptions.

Background: Computerised databases of primary care clinical records are widely used for epidemiological research. In Catalonia, the Information System for the Development of Research in Primary Care (SIDIAP) aims to promote the development of research based on high-quality validated data from primary care electronic medical records.

Objective: The purpose of this study is to create and validate a scoring system (Registry Quality Score, RQS) that will enable all primary care practices (PCPs) to be selected as providers of researchusable data based on the completeness of their registers.

Methods: Diseases that were likely to be representative of common diagnoses seen in primary care were selected for RQS calculations. The observed/expected cases ratio was calculated for each disease. Once we had obtained an estimated value for this ratio for each of the selected conditions we added up the ratios calculated for each condition to obtain a final RQS. Rate comparisons between observed and published prevalences of diseases not included in the RQS calculations (atrial fibrillation, diabetes, obesity, schizophrenia, stroke, urinary incontinence and Crohn's disease) were used to set the RQS cutoff which will enable researchers to select PCPs with research-usable data.

Results: Apart from Crohn's disease, all prevalences were the same as those published from the RQS fourth quintile (60th percentile) onwards. This RQS cut-off provided a total population of 1 936 443 (39.6% of the total SIDIAP population).

Conclusions: SIDIAP is highly representative of the population of Catalonia in terms of geographical, age and sex distributions. We report the usefulness of rate comparison as a valid method to establish research-usable data within primary care electronic medical records.