Search

Objectives Patient-accessible electronic health records (PAEHRs) are being implemented at international scale. Comparing policies and systems could allow countries to learn from each other to address global and nation-specific challenges. We compare national PAEHR policy (hard and soft regulation) and services in 10 countries. Methods PAEHR policy and system documentation was gathered from Australia, Denmark, Estonia, Finland, France, the Netherlands, New Zealand, Norway, Sweden and the United States. A basic analytic model for policy analysis was used to delimit our focus to policy content, followed by an inductive thematic analysis across countries, in which we clustered initial themes into a set of categories of PAEHR service “approaches” related to three specific content areas. Results Although all 10 countries ensured some patient rights to access medical records, policies and systems were highly variable, as were the technological processes arising from these. In particular, three policy areas showed great variability. Depending upon country of origin, a patient would encounter differences in: login procedures (security), access to own and other patients’ data during adolescence (user rights), and types of medical data made available to the patient (data sets). Conclusions Individuals encounter very different access rights to their medical data depending on where they live. Countries may be able to develop improved policies by examining how other nations have solved common problems. Harmonizing policies is also an initial step likely to be needed before cross-national PAEHRs could be possible. [ABSTRACT FROM AUTHOR]

Objective: To examine whether a structured data quality report (SDQR) and feedback sessions with practice principals and managers improve the quality of routinely collected data in EHRs.Methods: The intervention was conducted in four general practices participating in the Fairfield neighborhood electronic Practice Based Research Network (ePBRN). Data were extracted from their clinical information systems and summarised as a SDQR to guide feedback to practice principals and managers at 0, 4, 8 and 12 months. Data quality (DQ) metrics included completeness, correctness, consistency and duplication of patient records. Information on data recording practices, data quality improvement, and utility of SDQRs was collected at the feedback sessions at the practices. The main outcome measure was change in the recording of clinical information and level of meeting Royal Australian College of General Practice (RACGP) targets.Results: Birth date was 100% and gender 99% complete at baseline and maintained. DQ of all variables measured improved significantly (p<0.01) over 12 months, but was not sufficient to comply with RACGP standards. Improvement was greatest with allergies. There was no significant change in duplicate records.Conclusions: SDQRs and feedback sessions support general practitioners and practice managers to focus on improving the recording of patient information. However, improved practice DQ, was not sufficient to meet RACGP targets. Randomised controlled studies are required to evaluate strategies to improve data quality and any associated improved safety and quality of care. [ABSTRACT FROM AUTHOR]

Background Improving healthcare for people with chronic conditions requires clinical information systems that support integrated care and information exchange, emphasizing a semantic approach to support multiple and disparate Electronic Health Records (EHRs). Using a literature review, the Australian National Guidelines for Type 2 Diabetes Mellitus (T2DM), SNOMED-CT-AU and input from health professionals, we developed a Diabetes Mellitus Ontology (DMO) to diagnose and manage patients with diabetes. This paper describes the manual validation of the DMO-based approach using real world EHR data from a general practice ( n = 908 active patients) participating in the electronic Practice Based Research Network (ePBRN). Method The DMO-based algorithm to query, using Semantic Protocol and RDF Query Language (SPARQL), the structured fields in the ePBRN data repository were iteratively tested and refined. The accuracy of the final DMO-based algorithm was validated with a manual audit of the general practice EHR. Contingency tables were prepared and Sensitivity and Specificity (accuracy) of the algorithm to diagnose T2DM measured, using the T2DM cases found by manual EHR audit as the gold standard. Accuracy was determined with three attributes – reason for visit (RFV), medication (Rx) and pathology (path) – singly and in combination. Results The Sensitivity and Specificity of the algorithm were 100% and 99.88% with RFV; 96.55% and 98.97% with Rx; and 15.6% and 98.92% with Path. This suggests that Rx and Path data were not as complete or correct as the RFV for this general practice, which kept its RFV information complete and current for diabetes. However, the completeness is good enough for this purpose as confirmed by the very small relative deterioration of the accuracy (Sensitivity and Specificity of 97.67% and 99.18%) when calculated for the combination of RFV, Rx and Path. The manual EHR audit suggested that the accuracy of the algorithm was influenced by data quality such as incorrect data due to mistaken units of measurement and unavailable data due to non-documentation or documented in the wrong place or progress notes, problems with data extraction, encryption and data management errors. Conclusion This DMO-based algorithm is sufficiently accurate to support a semantic approach, using the RFV, Rx and Path to define patients with T2DM from EHR data. However, the accuracy can be compromised by incomplete or incorrect data. The extent of compromise requires further study, using ontology-based and other approaches. [ABSTRACT FROM AUTHOR]

Introduction: There are increasing numbers of mHealth initiatives in middle and low income countries aimed at improving health outcomes. Bangladesh is no exception with more than 20 mobile health (mHealth) initiatives in place. A recent study in Bangladesh examined community readiness for mHealth using a framework based on quantitative data. Given the importance of a framework and the complementary role of qualitative exploration, this paper presents data from a qualitative study which complements findings from the quantitative study.Methods: The study was conducted in the Chakaria sub-district of Bangladesh. In total, 37 in-depth interviews were conducted between December 2012 and March 2013. Participants included the general public, students, community leaders, school teachers, and formal and informal healthcare providers. Thematic analysis was used to develop a logical and relevant framework to examine community readiness. As in the quantitative exploration, this study approached the investigation with four types of readiness in mind: core readiness, technological readiness, human resource readiness and motivational readiness.Results: Community members, community leaders and healthcare providers expressed their interest in the use of mHealth in rural Bangladesh. Awareness of mHealth and its advantages was low among uneducated people. Participants who have used mHealth were attracted to the speed of access to qualified healthcare providers, time savings and low cost. Some participants did not see the value of using mobile phones for healthcare compared to a face-to-face consultation. Illiteracy, lack of English language proficiency, lack of trust and technological incapability were identified as barriers to mHealth use. However, a sense of ownership, evidence of utility, a positive attitude to the use of mHealth, and intentions towards future use of mHealth were driving forces in the adoption of mHealth services.Conclusions: This study re-affirmed the mHealth readiness conceptual framework with different dimensions of readiness and identified potential barriers and possible solutions for mHealth. Moving forward, emphasis should be placed on training users, providing low-cost services and improving trust of users. [ABSTRACT FROM AUTHOR]

Introduction Evidence in favor of mHealth for healthcare delivery in settings where trained health workforce is limited or unavailable is accumulating. With rapid growth in access to mobile phones and an acute shortage of health workforce in Bangladesh, mHealth initiatives are increasing with more than 20 current initiatives in place. “Readiness” is a crucial prerequisite to the successful implementation of telehealth programs. However, systematic assessment of the community readiness for mHealth-based services in the country is lacking. We report on a recent study describing the influence of community readiness for mHealth of a rural Bangladesh community. Methods A conceptual framework for mHealth readiness was developed, which included three categories: technological, motivational and resource readiness. This guided the questionnaire development for the survey conducted in the Chakaria sub-district of Bangladesh from November 2012 to April 2013. Multivariate logistic regression was used to examine ownership of mobile phones, use of the technology, and knowledge regarding awareness of mHealth services as predictors of the community readiness to adopt mHealth. Results A total of 4915 randomly selected household members aged 18 years and over completed the survey. The data explained the sub-categories of the readiness dimensions. In terms of access, 45% of respondents owned a mobile phone with ownership higher among males, younger participants and those in the highest socioeconomic quintiles. Results related to technological readiness showed that among mobile phone owners, 50% were aware of SMS but only sending and receiving SMS. Only 37% generally read the received SMS. Only 5% of respondents used the internet capabilities on their phone and 25% used voice messages. The majority (73%) of the participants were interested in joining mHealth programs in the future. Multivariate analysis showed that ownership of a mobile phone (aOR 1.3, 95% CI 1.1–1.5), younger age (aOR 2.6, 95% CI 2.1–3.3), males (aOR 1.8, 95% CI 1.6–2.1), educated respondents (11 years or more education) (aOR 11.1, 95% CI 6.2–19.2) and those belonging to the highest socio-economic group (aOR 3.7, 95% CI 2.9–4.7) were significantly independently associated with knowledge regarding awareness of current mHealth services. Conclusions We developed a conceptual framework to assess community readiness for mHealth. We described three high level dimensions of readiness and have partially tested the conceptual framework in a rural sub-district in Bangladesh. We found that the community has some technological readiness but inequity was observed for human resource readiness and technological capabilities. The study population is motivated to use mHealth. Our conceptual framework is a promising tool to assist policy-makers in planning and implementing mHealth programs. [ABSTRACT FROM AUTHOR]