Informatics in Primary Care最新文献

Background: Adherence to antidepressant therapy remains a major issue worldwide. Most people with depression are treated in a general practice setting, but many stop taking antidepressants before completing a six-month course as recommended by guidelines.

Objectives: To determine antidepressant adherence rates as indicated in primary care prescribing data and pharmacy dispensing data; to demonstrate commonly occurring patterns related to non-adherence, using a prescription visualisation tool we have developed; and to determine whether prescribing data is a good predictor of dispensing based adherence.

Methods: We analysed general practice electronic prescribing data for the year ending 31 December 2006 and linked pharmacy dispensing records by National Health Index. We calculated medication adherence for patients starting antidepressants using a six-month evaluation period and a gap-based adherence measure. Patients with a gap of more than 15 days in antidepressant therapy were considered non-adherent. Using a prescription visualisation tool, we described common modes of non-adherence.

Results: Out of 2713 patients, 153 satisfied our inclusion criteria. Thirty-nine percent of patients showed poor adherence based on prescribing and 68% showed poor adherence on dispensing. Prescribing based non-adherence had a positive predictive value of 98% (95% CI 92%-99%) and negative predictive value of 51% (CI 47%-52%) for dispensing based non-adherence. Three broad categories of non-adherence were identified: 1) failure to return for re-prescription, 2) failure to maintain adherence despite initial attempts and 3) failure to return for re-prescription in a timely manner.

Conclusions: Prescribing data identifies substantial adherence issues in antidepressant therapy. Clinicians should consider adherence issues as part of the overall treatment regime and discuss such issues during consultations.

Background: Medication information is often poorly delineated for paediatric patients, resulting in high off-label and non-licensed use of drugs in this population. Access to accurate medicines information in this population becomes a necessity in order to avoid medication errors. Clinical decision support tools (CDSTs), which are increasingly available on mobile devices (e.g. smartphones), can provide healthcare providers with convenient access to paediatric medicines information at point of care. However, to date no systematic evaluation of the content in these CDSTs has been conducted.

Objectives: To evaluate paediatric medicines information in CDSTs for smartphones and other mobile devices.

Method: Evaluation of CDSTs according to scope and completeness was accomplished via weighted categories of 108 questions distributed evenly across three age groups: infants, children and adolescents.

Results: Three paediatric-specific databases and six general databases were evaluated. The best performer provided 75.9% of the answers for scope and scored 69.7% for completeness. Databases generally performed less effectively in providing answers sourced from clinical guidelines compared with more conservative sources such as package inserts.

Conclusions: Overall, general medicines information CDSTs performed better than paediatric-specific CDSTs in both scope and completeness. Results from this study may help guide CDST selection on mobile devices by healthcare professionals whose patient populations include paediatrics.

Objectives: To demonstrate how the fit between individual, task and technology (FITT) framework can be used for health information system evaluation.

Methods: We developed a prototype information system with an integrated expert system for head- ache patients. The FITT framework was used to evaluate the prototype health information system.

Results: The FITT framework, once applied, positively evaluated 199 integrated headache diagnoses, 349 schemes and 698 symptoms. We assessed 528 internet pages to determine to what extent they met the users' expectations. In two study sections, a total of 70 (of 140) participants used the system. In the second section, the intervention group did significantly better (P=0.031) than the control group.

Conclusions: The FITT framework provided a proper tool for evaluating the prototype health information system and determining which specific set of deltas to focus on in future developments.

Background: The ageing population worldwide is increasingly acquiring multiple chronic diseases. The complex management of chronic diseases could be improved with electronic health records (EHRs) tailored to chronic disease care, but most EHRs in use today do not adequately support longitudinal data management. A key aspect of chronic disease management is that it takes place over long periods, but the way that most EHRs display longitudinal data makes it difficult to trend changes over time and slows providers as they review each patient's unique course.

Methods: We present five clinical scenarios illustrating longitudinal data needs in complex chronic disease management. These scenarios may function as example cases for software development.

Outputs: For each scenario, we describe and illustrate improvements in temporal data views. Two potential solutions are visualisation for numerical data and disease-oriented text summaries for non-numerical data.

Conclusions: We believe that development and widespread implementation of improved temporal data views in EHRs will improve the efficiency and quality of chronic disease management in primary care.

Background: Quality improvement is a priority for health services worldwide. There are many barriers to implementing change at the locality level and misinterpreting responses and behaviours can effectively block change. Electronic health records will influence the means by which knowledge and information are generated and sustained among those operating quality improvement programmes.

Objective: To explain how complex adaptive system (CAS) theory provides a useful tool and new insight into the responses and behaviours that relate to quality improvement programmes in primary care enabled by informatics.

Methods: Case studies in two English localities who participated in the implementation and development of quality improvement programmes. The research strategy included purposefully sampled case studies, conducted within a social constructionist ontological perspective.

Results: Responses and behaviours of quality improvement programmes in the two localities include both positive and negative influences associated with a networked model of governance. Pressures of time, resources and workload are common issues, along with the need for education and training about capturing, coding, recording and sharing information held within electronic health records to support various information requirements.

Conclusions: Primary care informatics enables information symmetry among those operating quality improvement programmes by making some aspects of care explicit, allowing consensus about quality improvement priorities and implementable solutions.

Background: Databases derived from primary care electronic health records (EHRs) are ideally suited to study clinical influences on referral patterns. This is the first study outside the United Kingdom to use an EHR database to describe rates of referral per patient from family physicians to specialists.

Objective: To use a primary care EHR database to describe referrals to specialist physicians; to partition variance in referral rates between the practice and patient levels.

Methods: Retrospective cross-sectional analysis of de-identified EHRs of 33 998 patients from 10 primary care practices in Ontario, Canada. The study cohort included all patients who visited their family physician 1 April 2007 to 31 March 2008 (n ≥ 24856). Specialist referrals for each patient were counted for 12 months following their index visit. Rates of referral were compared by sex, age, number of office visits, practice location and specialist type using t-tests or Pearson's correlation. Variance partitioning determined the proportion of variance in the overall referral rate accounted for by the practice and patient levels.

Results: In total, 7771 patients (31.3%) had one or more referrals. The overall referral rate was 455/1000 patients/year (95% CI, 444-465). Rates were higher for females, older patients and rural practices. The referral rate correlated with the number of family physician office visits. Ninety-two percent of the total variance in referral rates was attributable to the patient (vs. practice) level.

Conclusions: A Canadian primary care EHR database showed similar patterns of referral to those reported from administrative databases. Most variance in referral rates is explained at the patient level.

Background: Practice managers play an important role in the organisation and delivery of primary care, including uptake and implementation of technologies. Little is currently known about practice managers' attitudes to the use of information and communication technologies, such as email or text messaging, to communicate or consult with patients.

Objectives: To investigate practice managers' attitudes to non-face-to-face consultation/communication technologies in the routine delivery of primary care and their role in the introduction and normalisation of these technologies.

Methods: We carried out a mixed-methods study in Scotland, UK. We invited all practice managers in Scotland to take part in a postal questionnaire survey. A maximum variation sample of 20 survey respondents participated subsequently in in-depth qualitative interviews.

Results: Practice managers supported the use of new technologies for routine tasks to manage workload and maximise convenience for patients, but a range of contextual factors such as practice list size, practice deprivation area and geographical location affected whether managers would pursue the introduction of these technologies in the immediate future. The most common objections were medico-legal concerns and lack of perceived patient demand.

Conclusion: Practice managers are likely to play a central role in the introduction of new consultation/communication technologies within general practice. They hold varying views on the appropriateness of these technologies, influenced by a complex mix of contextual characteristics. Managers from areas in which the ethos of the practice prioritises personalised care in service delivery are less enthusiastic about the adoption of remote consultation/communication technologies.

Objective: To identify key elements and characteristics of complex adaptive systems (CAS) relevant to implementing clinical governance, drawing on lessons from quality improvement programmes and the use of informatics in primary care.

Method: The research strategy includes a literature review to develop theoretical models of clinical governance of quality improvement in primary care organisations (PCOs) and a survey of PCOs.

Results: Complex adaptive system theories are a valuable tool to help make sense of natural phenomena, which include human responses to problem solving within the sampled PCOs. The research commenced with a survey; 76% (n16) of respondents preferred to support the implementation of clinical governance initiatives guided by outputs from general practice electronic health records. There was considerable variation in the way in which consultation data was captured, recorded and organised. Incentivised information sharing led to consensus on coding policies and models of data recording ahead of national contractual requirements. Informatics was acknowledged as a mechanism to link electronic health record outputs, quality improvement and resources. Investment in informatics was identified as a development priority in order to embed clinical governance principles in practice.

Conclusions: Complex adaptive system theory usefully describes evolutionary change processes, providing insight into how the origins of quality assurance were predicated on rational reductionism and linearity. New forms of governance do not neutralise previous models, but add further dimensions to them. Clinical governance models have moved from deterministic and 'objective' factors to incorporate cultural aspects with feedback about quality enabled by informatics. The socio-technical lessons highlighted should inform healthcare management.