Pharmacists' use of electronic health records: Silent leaders no more

Influence of pharmacy practice on community pharmacists' integration of medication and lab value information from electronic health records

Sophisticated methodological approaches and measures for scaling up are key elements of effective evaluation Digital health technologies and services are significant contributors to the transformation of health care delivery. It has been estimated that 80% of technology projects fail1 due to uncertainty, abandonment and lack of organisational willingness to adopt.2 In response to the high failure rate, the discipline of benefits management has emerged, with the aim of measuring and optimising the value of digital health initiatives. The development and application of benefits management has received some attention,3 but owing to the infancy of the discipline there has been limited assessment of methodological frameworks and their application. In this article, we describe the framework that is being used to measure and quantify the benefits of the My Health Record system in Australia. We consider the strengths and limitations of this framework in the context of existing frameworks, and its ability to demonstrate digital health system benefits. We also identify priority areas for further development of digital health benefit evaluation frameworks. Further, we provide an overview of the approach to digital health system benefits evaluation in Australia, in the context of the My Health Record system. A role of the Australian Digital Health Agency has been to lead the development of the National Digital Health Strategy, to lay the foundations for a safe, seamless and secure health system.4 This strategy comprises seven priority areas to be achieved by 2022. A key strategic priority is to provide health information whenever and wherever it is needed, and this is underpinned by the My Health Record system. My Health Record is a secure online summary of health information which can be accessed by people and their health care providers, and is patient-controlled. It is a personal health record, enabling people to access, manage and share their health information with their clinicians using a range of privacy controls. This functionality includes the ability to decline access to specific documents, set a control to restrict access to the entire record, see an audit trail of any organisations that have accessed the record, and block organisations from viewing the record. The record can contain summary information from general practice, hospital, pharmacy and other health care settings. It may also contain results of investigations, plus documents that patients create themselves (such as advance care plans and personal notes). In addition, it can provide access to Medicare documents such as Medicare Benefits Schedule and Pharmaceutical Benefits Scheme information, the Australian Immunisation Register and the Australian Organ Donor Register. A multimethod evaluation framework has been developed to comprehensively evaluate the benefits of the My Health Record system. It draws on approaches that have been used overseas and assesses the range of clinical contexts in which the system is used. Connecting people to their own health information has been shown to produce a range of benefits relating to patient engagement and a 60–80% improvement in their adherence to treatment regimens.5 In addition, enabling self-management has been associated with significant financial savings in terms of health care costs and avoided hospital admissions.6 It is also theorised that enabling clinicians across different health care settings to share information will result in improved patient safety (eg, fewer medication errors), improved care coordination, a reduction in unnecessarily duplicated investigations, and efficiency gains for clinicians in terms of time savings. In Australia, 2–3% of hospital admissions each year relate to medication misadventure, costing an estimated $1.2 billion annually.7 Improved access to medication information from a range of settings through the medicines information view in My Health Record should provide clinicians with more comprehensive information, and it is hoped that this will result in a reduction of medication misadventure events. Moreover, the use of digital health records to enable test results to be shared has been shown to reduce duplicated pathology tests by 18% per week,8 and in primary care settings it has been found that 13.6% of visits were missing important clinical information.9 Several lessons relating to the application of digital health research and evaluation frameworks have been learned. Many different "key measures" have been described, and there is an overall lack of consensus as to the "who, why, how, when and what" that should apply to an evaluation.10 The impact of this has been a failure to capture the complete range of players involved in the successful delivery of a system — players who do not necessarily share identical perspectives. A recent systematic review recommends that future frameworks present better methods for stakeholder identification and have a greater focus on understanding the context in which the system is delivered. From a systems perspective, this includes usability and organisational impact.10 The foundations of Australia's digital health system benefits evaluation framework are based on applying a range of measurement methods to capture a broad variety of outcome measures, reflecting a multistakeholder National Digital Health Strategy which offers different types of benefits to its different stakeholders. Five benefit workstreams have been introduced to evaluate the My Health Record system using qualitative, quantitative and mixed-method designs, as well as behavioural economic and health economic evaluation methods (Box). These workstreams are: Perceived improvement in access to patients' information and reduction in the need to order pathology tests and diagnostic imaging Self-reported experience of being able to view information that was previously unknown, and saving time requesting information Reduction in adverse medicine-related events through having access to a patient's medicines information Reduction in unnecessary duplication of pathology tests and diagnostic imaging The workstreams have been designed with data sources in mind to assist with planning and prioritising evaluation measures. These have been ranked by feasibility11 and impact, taking into account the relative importance of each measure to different stakeholders.10 To support two of these workstreams — customer and market insights, and impact evaluations — we have taken a similar approach to Canada's Clinical Adoption Framework.3 The impact evaluations workstream in particular focuses on outcomes and how these are being realised. For example, it focuses on measuring usability and adoption of digital tools from clinicians and consumers from their inception and throughout their development. The behavioural economics workstream refers to the evaluation methods which aim to measure change in the behaviour of system users (eg, clinicians and patients), organisational issues which can affect adoption, and indicators that would inform change management requirements to improve usability and adoption of digital health services.12 The data analytics workstream enables monitoring of trends in adoption and usage. For example, this workstream is investigating, through data modelling, whether My Health Record use is associated with fewer medication errors and reduced unnecessary duplication of pathology tests, to provide comprehensive results from a range of settings. The health economics evaluations workstream evaluates and forecasts indirect population health outcomes and downstream economic benefits using health economics modelling. For example, it is developing a health economics model to inform the ongoing business case for the My Health Record system, and conducting modelling that will support measurement of current and future benefits. To support an iterative process in product and service design, the Australian Digital Health Agency also undertakes user experience research and agile project management methods.13 The advantage of this approach is that it can deliver user insights which inform continual design and development of new features. Further, conducting field research to observe a range of digital system users in their environment is a way of identifying attributes that can influence the methods and measures used more broadly in our benefits evaluation framework. Current approaches and frameworks that have been developed overseas to support evaluation and benefits measurement of digital health services are at various stages of maturity. An increasing range of methods to evaluate digital health technologies is being supported by publications, which recognise that they are being deployed in complex health systems that require a contextual understanding of users, clinical settings and the policy environment in which they operate. We must not underestimate the challenges of evaluating benefits of digital health system delivery, and new methods to support evaluations continue to be developed and validated.2 Benefits evaluation frameworks offer a platform that can guide researchers and policy makers in generating and translating evidence to support future directions and ongoing investment in digital health services. Recent debates highlight the importance of fostering evaluation designs which combine different research methods, using qualitative, quantitative and co-design principles, as well as process measures1 that we have embedded into our benefits framework. To strengthen our current benefits evaluation framework and overall approach to digital health service evaluation, we are actively introducing methods that ascertain how services can be scaled up, to identify enablers and barriers to implementation across a range of settings.1 In this context, we define scalability as "the ability … to be expanded under real-world conditions to reach a greater proportion of the eligible population, while retaining effectiveness".14 Measuring scalability is not a commonly undertaken process and has been described as poorly understood.15 But the Australian Digital Health Agency is working towards applying these methods to build an understanding of impact. These findings form a picture of how, where and for whom the intervention could have the greatest impact and, conversely, what adaptations are needed for interventions to work across different population groups. A practical example of how this is being undertaken through the Agency is the introduction of and investment in a range of "test bed" studies. Test beds are projects assessing new digital-enabled models of care that are instigated and delivered cooperatively through sustainable and viable partnerships between industry, government and other organisations. Their purpose is to promote innovation to address Australia's highest priority health challenges and generate evidence on how the new approaches improve health outcomes. Currently, there are 15 test beds across Australia which are testing digital infrastructure and integration of digital health into clinical workflows.16 Despite ongoing interest in digital health benefits evaluation frameworks, few examples of their use in evaluation of digital health services have been published. The Australian Digital Health Agency's benefits evaluation framework will be used to justify future funding of digital health and to inform community and clinical education material. Moreover, the findings will be used to inform enhancements of the My Health Record system, ensuring that its progress is relevant and appropriate for clinicians and consumers. We are grateful to Darian Eckersley, from the Australian Digital Health Agency, for advice on product design methods. Many thanks to the broader Research Programs team at the Agency for reviewing parts of this manuscript and commenting on an earlier version. We are all employed by the Australian Digital Health Agency. Commissioned; externally peer reviewed.

Electronic Health Records: Promises and Realities: A 3-Part Series Part I: The Digital Sea Change, Ready or Not

IntroductionThe ability to utilize the electronic health record (EHR) without compromising the doctor-patient relationship (DPR) is an essential skill of all physicians and trainees, yet little time is spent on educating or assessing learners on needed techniques. To address this gap, we developed a conventional OSCE station coupled with a simulated patient chart within the Epic program in order to assess our students' skills utilizing the EHR during a patient encounter.MethodsOf third-year medical students, 119 were given full access to the patient's simulated chart 24 hours in advance of their OSCE to review clinical data. During an in-person OSCE with a standardized patient (SP), students performed a focused history and physical, using the EHR to verify allergies and medications. Students completed an electronic patient note graded by faculty. SPs evaluated the students on communication and interpersonal skills with specific rubric elements. Faculty graded the students' notes to evaluate their expression of clinical reasoning in the assessment and plan.ResultsTraining SPs and faculty to assess students on EHR skills was feasible. After implementation of a comprehensive curriculum focused on EHR and DPR, there was a significant difference on EHR-related communication skills (M = 76.4, SD = 17.6) versus (M = 37, SD = 28.9) before curriculum enhancement t (117.9) = −12.4, p <.001.DiscussionThe EHR OSCE station provided a standardized method of assessing students' EHR skills during a patient encounter. Challenges still exist in the technological requirements to develop and deliver cases in today's EHR platform.

Objective: Estimate the effects of electronic health records (EHRs) on physician productivity in day-to-day practice by including the combinations of EHRs with nonelectronic records that typify current practice. Study Design: Cross section of 7,725 physicians representing 70% of physicians who provided patient care in 2012-2014 in Arizona. The sample was divided into office based (OFB) and non-office based (NOFB) physicians. Methods: Information from licensing applications was combined with a survey of the uses of electronic and nonelectronic health records. Productivity was measured as patient visits per hour in a typical work week. The effects of EHRs on productivity were estimated from a regression model that compared users to non-users and an model that included combinations of electronic, paper and scanned records. The results are compared to the physicians’ rankings of the effect of each combination of records on staff and physician productivity. Results: The productivity of OFB physicians is increased by the use of e-prescribing (+17.4%, p<0.001) and intervention reminders (+5.7%, p=0.03). There are no significant effects of the different mixes of health records. Among NOFB physicians, e-prescribing is the only contributor to productivity (+7.7%, p=0.069). Physicians rank EHR-only as more productive than any combinations of EHRs with non-electronic records. Conclusion: The potential productivity of EHRs is severly limited by the typical practice of combining EHRs with non-electronic records. Reducing the number of different types of records used in a practice will increase the positive effects of EHRs on productivity. One requirement for such reductions is an increased availability of health information exchanges.

Electronic Health Records (EHR) is the electronic form of storing a patient's medical history. EHR contains patient’s data such as progress notes, medications, prescriptions, vital signs, scan reports and laboratory data. Transferring EHR over the internet improves the quality of health care and reduces medical costs. However, in the traditional system, the EHR are stored across different decentralised hospitals, making data sharing difficult and increasing the risk of patient privacy. A privacy-preserving framework for electronic health records using blockchain technology is implemented to address these issues. The patient has complete control over the EHR, and the patient can share their health records with doctors of various medical institutions. The privacy and security of the patient’s EHR are guaranteed by the verifiability and immutability property of the blockchain technology. The doctor upload the EHR, and it is encrypted using the SHA256 hashing algorithm and stored as a separate block. The patient shares the EHR with the doctor of any medical institution through the unique key shared via the doctor’s email. The doctor can access and update the EHR using the shared key. The block validation is done using Delegated Proof of Stake (DPoS) consensus algorithm, which guarantees the privacy of the patient’s data. The proposed system based on the DPoS algorithm has considerabe reduction in resource utilisation, computational capacity, time, and cost for EHR transactions.

An Electronic Health Record (EHR) system is a computerised medical information system that collects, displays, and stores a patient’s information. It is an evidence base that addresses issues associated with patients’ paper records. Implementing such a system will have a high positive impact on healthcare quality and healthcare services. For example, an EHR is an electronic record that sequentially stores any resident’s health data from nearly the first month of gestation until death, and can bring those records anytime and to any authorised physician. The objectives of this study are to investigate the present status of EHR implementations around the world, and to identify best practice solutions. Additionally, the study focuses on how to adopt best practices in Australia. The methodology of this paper involved an academic research consisting of 250 articles and over 100 websites. All of the information in this paper was obtained through a search strategy—using PubMed, Google Scholar, and Google—of the best practices applied in many countries, including the US, Canada, and several nations in Western Europe. With 30 references used that provided the recommendations to adopt the best practice solutions for Australian My Health Record system while implementation. This paper has further exposed the problems with EHR systems as implemented worldwide. The recommendations can be summarised as follows: improve the overall awareness of the stakeholders, conduct training sessions for stakeholders on My Health Record system, reward physicians for using the system, achieve ongoing technical and systems security integrity and compliance, implement a response plan in the event of a breach of the EHR system, and implement a simple graphical user interface to facilitate access to stakeholders. Further results as recommendations are provided in the results section. The research concluded that by adopting these best practice solutions, Australia will be able to achieve a stable healthcare system, which will ensure a higher level of healthcare quality to patients and healthcare alike. This paper will give stakeholders a clear vision to determine the original cause that hinders satisfying results while implementing My Health Record system in Australia.

We integrate literature- and data-driven task analysis methods to derive an initial task taxonomy for electronic health record (EHR) and electronic medical record (EMR) data analysis. An EHR (EMR) is a digital and longitudinal version of a patients health(medical) information and may include all key clinical events relevant to that persons health (medical) history, such as provider, demographics, progress notes, medicine, diagnosis, etc. Our goal is to arrive a task taxonomy for analyzing EHR (EMR) datasets because tasks play an important role in the design and evaluation of visualization techniques. Our method has three stages: data collection, task modelling, and task taxonomy summary. In data collection, we first survey related literature from the past two decades and extract typical tasks and corresponding data by extracting goals and scenarios of the particular work. We introduce multiple continuous relations to describe specific binary or multiple continuous relation-seeking tasks. Finally, we arrive an initial set of task types for EHR/EMR analysis that guide the design and evaluation of visualization techniques.

My Health Record system is the Australian Govern-ment‘s digital health record system that holds My Health Record. My Health Record is a secure online health record containing consumers' health information. The system aims to provide health care professionals with access to key health information (e.g. listing medicines, allergies and key diagnoses), radiology and pathology test results. The system (previously named Personally Controlled Electronic Health Record) enables consumers to decide how to share information with any of their health care providers who are registered and connected to the system. The My Health Record system operates under the Australian legislative framework My Health Records Act 2012. The Act establishes, inter alia, a privacy framework specifying which entities can collect, use and disclose certain information in the system and the penalties that can be imposed on improper collection, use and disclosure of this information. This paper presents the formal specification (from the legislation) and verification of the My Health Record regarding how consumers can control who access the information, and how the system adheres to such access. We rely on the correct-by-construction Event-B method to prove control and access properties of the system.

Switzerland's Electronic Patient Record (EPR) system faces a critical adoption crisis: less than 2% of the population has opened an account despite years of investment. To understand this failure from the citizen's perspective, we launched the first "expedition" in which 120 volunteers ("salutonauts") systematically tested Switzerland's current digital health infrastructure by opening EPRs, requesting their health data and documenting their experiences. This represents the first real-world, citizen-led assessment of Switzerland's health data infrastructure. Our findings reveal three fundamental insights. First, citizens appreciate the concept of personally controlled health records governed by a trustworthy national framework. Second, the current EPR implementation is both impractical and economically untenable under existing conditions: opening an EPR nationwide would cost CHF 400 million (calculated from required man-hours alone), while uploading historic documents would cost CHF 1 billion, and obtaining insurance claims data would cost over CHF 5 billion in lost productivity. Third, the recently announced draft of the new Electronic Health Dossier Law (E-GD) comes at a critical moment, since evidence-based redesign of the EPR system is essential. 95% of participants valued comprehensive medical checkups producing structured data, demonstrating citizens' appetite for high-quality digital health records when they provide tangible benefits. Our expedition methodology, which is agile, citizen-centred and involves real-world testing, offers a replicable model for evaluating digital health initiatives before massive implementation costs are incurred. With the E-GD revision underway, Switzerland has a unique opportunity to develop an EPR system that serves patients and physicians above all. Successful implementation requires EPR opening at birth by default (with the ability to opt-out), the AI-powered automation of data collection and uploading, seamless healthcare professional interfaces, and using the EPR as the billing data gateway. The alternative - i.e. continuing the current approach - virtually guarantees failure based on economic factors alone.

Behavioral Health Data in the Electronic Health Record: Privacy Concerns Slow Sharing

Barcode-assisted medication administration (BCMA) is technology with demonstrated benefit in reducing medication administration errors in hospitalized patients; however, it is not routinely used in emergency departments (EDs). EDs may benefit from BCMA, because ED medication administration is complex and error-prone. A naïve observational study was conducted at an academic medical center implementing BCMA in the ED. The rate of medication administration errors was measured before and after implementing an integrated electronic medical record (EMR) with BCMA capacity. Errors were classified as wrong drug, wrong dose, wrong route of administration, or a medication administration with no physician order. The error type, severity of error, and medications associated with errors were also quantified. A total of 1,978 medication administrations were observed (996 pre-BCMA and 982 post-BCMA). The baseline medication administration error rate was 6.3%, with wrong dose errors representing 66.7% of observed errors. BCMA was associated with a reduction in the medication administration error rate to 1.2%, a relative rate reduction of 80.7% (p < 0.0001). Wrong dose errors decreased by 90.4% (p < 0.0001), and medication administrations with no physician order decreased by 72.4% (p = 0.057). Most errors discovered were of minor severity. Antihistamine medications were associated with the highest error rate. Implementing BCMA in the ED was associated with significant reductions in the medication administration error rate and specifically wrong dose errors. The results of this study suggest a benefit of BCMA on reducing medication administration errors in the ED.

Using HL7 FHIR to achieve interoperability in patient health record.

As electronic health records (EHRs) became broadly available in medical practice, effective use of EHRs by medical students emerged as an essential aspect of medical education. While new federal clinical documentation guidelines have the potential to encourage greater medical student EHR use and enhance student learning experiences with respect to EHRs, little is known nationally about how students have engaged with EHRs in the past. This study examines medical student accounts of EHR use during their internal medicine (IM) clerkships and sub-internships during a 5-year time period prior to the new clinical documentation guidelines. An online survey about EHR use was administered to medical students immediately after they completed USMLE Step 2 CK. The sample included 16,602 medical students planning to graduate from US medical schools from 2012 to 2016. Descriptive statistics were computed to determine the average percentage of students engaged in various health record activities during their IM educational experiences by graduation year. The vast majority (99%) of medical students used EHRs during IM clerkships or sub-internships. Most students reported that they entered information into EHRs during the inpatient component of the IM clerkship (84%), outpatient component of the IM clerkship (70%), and the IM sub-internship (92%). Yet, 43% of the students who graduated in 2016 never entered admission orders and 35% of them never entered post-admission orders. Medical school graduates ought to be able to effectively document clinical encounters and enter orders into EHR systems. Although most students used and entered information into EHRs during their IM clinical training, many students appear to have received inadequate opportunities to enter notes or orders, in particular. Implications for graduate medical education preparedness are considered. Future research should address similar questions using comparable national data collected after the recent guideline changes.

Electronic Health Records (EHR) and Electronic Medical Records (EMR) are data repositories that contain medical and treatment history of patients. Data collected in EHR and EMR may present Data Quality (DQ) issues, which compromise their use to research due to the validity and the reproducibility of the results. The present paper is a Literature Review (LR) that summarizes how DQ of healthcare data has been addressed and which methodologies were proposed. Initially 698 papers were identified, searching in five databases with two search strings. After three paper-filtering stages, 44 papers remained for full quality assessment and results extraction. The findings show that many DQ dimensions have been used for assessment, but there is no consensus in the definitions of the dimensions. For DQ improvement, the activities performed were categorized into two groups, one related with Data Governance and the other related to Data Cleaning.