Ten key considerations for the successful optimization of large-scale health information technology

From use, value and user-centered design to context: A mixed methods analysis of a hospital electronic medical record enhancement

Objective

This study sought to determine the use and perceived value of a user-centered electronic medical record (EMR) enhancement for stroke care and understand if its value was in alignment with its intended design. The EMR enhancement was introduced into Queensland public hospitals in Australia and included a summary page for enhanced interprofessional collaboration and data collection forms for efficient data extraction.

Methods

A mixed methods design was adopted and data collected from four hospital sites. We conducted 15 semistructured interviews with multidisciplinary end-users across participating sites and analyzed this data using inductive thematic techniques. Usage log data was extracted from the EMR to determine its use.

Results

Relative use of the summary page showed moderate use, varying from 66 ± 22.5 uses for each stroke patient admission per month (Site 1) to 26.7 ± 9.1 (Site 2). Interviews identified key themes of "visibility" and providing a "quick snapshot" of patient data as the main positive attributes. Technology "functionality" was perceived negatively. Use of the data collection forms was minimal, with inconsistency across sites: (Site 3, 0% to Site 2, 47%). Negative themes of "inefficiency," poor "functionality" and the "trust" required in data entry practices were found.

Conclusions

Despite its user-centered design, clinicians did not always use the enhancement in line with its intended design, or grasp its intended value. Our findings highlight the challenges of user-centered design to accurately reflect clinical workflows within different contexts. A greater understanding is required of how to optimize user-centered EMR design for specific hospital contexts.

The Implementation of Pharmacogenetics in the United Kingdom

There is considerable inter-individual variability in the effectiveness and safety of pharmaceutical interventions. This phenomenon can be attributed to a multitude of factors; however, it is widely acknowledged that common genetic variation affecting drug absorption or metabolism play a substantial contributory role. This is a concept known as pharmacogenetics. Understanding how common genetic variants influence responses to medications, and using this knowledge to inform prescribing practice, could yield significant advantages for both patients and healthcare systems. Some health services around the world have introduced pharmacogenetics into routine practice, whereas others are less advanced along the implementation pathway. This chapter introduces the field of pharmacogenetics, the existing body of evidence, and discusses barriers to implementation. The chapter will specifically focus on efforts to introduce pharmacogenetics in the NHS, highlighting key challenges related to scale, informatics, and education.

Management Opportunities and Challenges After Achieving Widespread Health System Digitization

The adoption of electronic health records (EHRs) and digitization of health data over the past decade is ushering in the next generation of digital health tools that leverage artificial intelligence (AI) to improve varied aspects of health system performance. The decade ahead is therefore shaping up to be one in which digital health becomes even more at the forefront of health care delivery - demanding the time, attention, and resources of health care leaders and frontline staff, and becoming inextricably linked with all dimensions of health care delivery. In this chapter, we look back and look ahead. There are substantive lessons learned from the first era of large-scale adoption of enterprise EHRs and ongoing challenges that organizations are wrestling with - particularly related to the tension between standardization and flexibility/customization of EHR systems and the processes they support. Managing this tension during efforts to implement and optimize enterprise systems is perhaps the core challenge of the past decade, and one that has impeded consistent realization of value from initial EHR investments. We describe these challenges, how they manifest, and organizational strategies to address them, with a specific focus on alignment with broader value-based care transformation. We then look ahead to the AI wave - the massive number of applications of AI to health care delivery, the expected benefits, the risks and challenges, and approaches that health systems can consider to realize the benefits while avoiding the risks.

Policy parameters for optimising hospital ePrescribing: An exploratory literature review of selected countries of the Organisation for Economic Co-operation and Development

Objective

Electronic prescribing systems offer considerable opportunities to enhance the safety, effectiveness and efficiency of prescribing and medicines management decisions but, despite considerable investments in health IT infrastructure and healthcare professional training, realising these benefits continues to prove challenging. How systems are customised and configured to achieve optimal functionality is an increasing focus for policymakers. We sought to develop an overview of the policy landscape currently supporting optimisation of hospital ePrescribing systems in economically developed countries with a view to deriving lessons for the United Kingdom (UK).

Methods

We conducted a review of research literature and policy documents pertaining to optimisation of ePrescribing within hospitals across Organisation for Economic Co-operation and Development (OECD) countries on Embase, Medline, National Institute for Health (NIH), Google Scholar databases from 2010 to 2020 and the websites of organisations with international and national health policy interests in digital health and ePrescribing. We designed a typology of policies targeting optimisation of ePrescribing systems that provides an overview of evidence relating to the level at which policy is set, the aims and the barriers encountered in enacting these policies.

Results

Our database searches retrieved 11 relevant articles and other web resources mainly from North America and Western Europe. We identified very few countries with a national level strategy for optimisation of ePrescribing in hospitals. There were hotspots of digital maturity in relation to ePrescribing at institutional, specialisation, regional and national levels in the US and Europe. We noted that such countries with digital maturity fostered innovations such as patient involvement.

Conclusions

We found that, whilst helpful to achieve certain aims, coordinated strategies within and across countries for optimisation of ePrescribing systems are rare, even in countries with well-established ePrescribing and digital health infrastructures. There is at present little policy focus on maximising the utility of ePrescribing systems.

Interorganizational Knowledge Sharing to Establish Digital Health Learning Ecosystems: Qualitative Evaluation of a National Digital Health Transformation Program in England

Background

The English Global Digital Exemplar (GDE) program is one of the first concerted efforts to create a digital health learning ecosystem across a national health service.

Objective

This study aims to explore mechanisms that support or inhibit the exchange of interorganizational digital transformation knowledge.

Methods

We conducted a formative qualitative evaluation of the GDE program. We used semistructured interviews with clinical, technical, and managerial staff; national program managers and network leaders; nonparticipant observations of knowledge transfer activities through attending meetings, workshops, and conferences; and documentary analysis of policy documents. The data were thematically analyzed by drawing on a theory-informed sociotechnical coding framework. We used a mixture of deductive and inductive methods, supported by NVivo software, to facilitate coding.

Results

We conducted 341 one-on-one and 116 group interviews, observed 86 meetings, and analyzed 245 documents from 36 participating provider organizations. We also conducted 51 high-level interviews with policy makers and vendors; performed 77 observations of national meetings, workshops, and conferences; and analyzed 80 national documents. Formal processes put in place by the GDE program to initiate and reinforce knowledge transfer and learning have accelerated the growth of informal knowledge networking and helped establish the foundations of a learning ecosystem. However, formal networks were most effective when supported by informal networking. The benefits of networking were enhanced (and costs reduced) by geographical proximity, shared culture and context, common technological functionality, regional and strategic alignments, and professional agendas.

Conclusions

Knowledge exchange is most effective when sustained through informal networking driven by the mutual benefits of sharing knowledge and convergence between group members in their organizational and technological setting and goals. Policy interventions need to enhance incentives and reduce barriers to sharing across the ecosystem, be flexible in tailoring formal interventions to emerging needs, and promote informal knowledge sharing.

Acceptance of Technologies for Aging in Place: A Conceptual Model

BACKGROUND

Older adults want to preserve their health and autonomy and stay in their own home environment for as long as possible. This is also of interest to policy makers who try to cope with growing staff shortages and increasing health care expenses. Ambient assisted living (AAL) technologies can support the desire for independence and aging in place. However, the implementation of these technologies is much slower than expected. This has been attributed to the lack of focus on user acceptance and user needs.

OBJECTIVE

The aim of this study is to develop a theoretically grounded understanding of the acceptance of AAL technologies among older adults and to compare the relative importance of different acceptance factors.

METHODS

A conceptual model of AAL acceptance was developed using the theory of planned behavior as a theoretical starting point. A web-based survey of 1296 older adults was conducted in the Netherlands to validate the theoretical model. Structural equation modeling was used to analyze the hypothesized relationships.

RESULTS

Our conceptual model showed a good fit with the observed data (root mean square error of approximation 0.04; standardized root mean square residual 0.06; comparative fit index 0.93; Tucker-Lewis index 0.92) and explained 69% of the variance in intention to use. All but 2 of the hypothesized paths were significant at the P<.001 level. Overall, older adults were relatively open to the idea of using AAL technologies in the future (mean 3.34, SD 0.73).

CONCLUSIONS

This study contributes to a more user-centered and theoretically grounded discourse in AAL research. Understanding the underlying behavioral, normative, and control beliefs that contribute to the decision to use or reject AAL technologies helps developers to make informed design decisions based on users' needs and concerns. These insights on acceptance factors can be valuable for the broader field of eHealth development and implementation.

Protocol for a qualitative study to identify strategies to optimise hospital ePrescribing systems

INTRODUCTION

Electronic prescribing (ePrescribing) is a key area of development and investment in the UK and across the developed world. ePrescribing is widely understood as a vehicle for tackling medication-related safety concerns, improving care quality and making more efficient use of health resources. Nevertheless, implementation of an electronic health record does not itself ensure benefits for prescribing are maximised. We examine the process of optimisation of ePrescribing systems using case studies to provide policy recommendations based on the experiences of digitally mature hospital sites.

METHODS AND ANALYSIS

Qualitative interviews within six digitally mature sites will be carried out. The aim is to capture successful optimisation of electronic prescribing (ePrescribing) in particular health systems and hospitals. We have identified hospital sites in the UK and in three other developed countries. We used a combination of literature reviews and advice from experts at Optimising ePrescribing in Hospitals (eP Opt) Project round-table events. Sites were purposively selected based on geographical area, innovative work in ePrescribing/electronic health (eHealth) and potential transferability of practices to the UK setting. Interviews will be recorded and transcribed and transcripts coded thematically using NVivo software. Relevant policy and governance documents will be analysed, where available. Planned site visits were suspended due to the COVID-19 pandemic.

ETHICS AND DISSEMINATION

The Usher Research Ethics Group granted approval for this study. Results will be disseminated via peer-reviewed journals in medical informatics and expert round-table events, lay member meetings and the ePrescribing Toolkit (http://www.eprescribingtoolkit.com/)-an online resource supporting National Health Service (NHS) hospitals through the ePrescribing process.

Optimising electronic prescribing in hospitals: a scoping review protocol

Introduction

Electronic prescribing (ePrescribing) systems can improve the quality of prescribing decisions and substantially reduce the risk of serious medication errors in hospitals. However, realising these benefits depends on ensuring that relevant sociotechnical considerations are addressed. Optimising ePrescribing systems is essential to maximise the associated benefits and minimise the accompanying risks of these large-scale and expensive health informatics infrastructures.

Methods

We will undertake a systematic scoping review of the literature to identify strategies to achieve optimisation of ePrescribing systems. We will search Medline, Embase and CINAHL for the period 1 January 2010 to 1 June 2019 and the grey literature by using Google Scholar. Independent reviewers will screen the results using predefined inclusion and exclusion criteria and will extract data for narrative and thematic synthesis.

Discussion

This work will be published in a peer-reviewed journal and we will ensure that the findings are both accessible and interpretable to the public, academics, policymakers and National Health Service leaders.

Definitions, components and processes of data harmonisation in healthcare: a scoping review

BACKGROUND

Data harmonisation (DH) has emerged amongst health managers, information technology specialists and researchers as an important intervention for routine health information systems (RHISs). It is important to understand what DH is, how it is defined and conceptualised, and how it can lead to better health management decision-making. This scoping review identifies a range of definitions for DH, its characteristics (in terms of key components and processes), and common explanations of the relationship between DH and health management decision-making.

METHODS

This scoping review identified relevant studies from 2000 onwards (date filter), written in English and published in PubMed, Web of Science and CINAHL. Two reviewers independently screened records for potential inclusion for the abstract and full-text screening stages. One reviewer did the data extraction, analysis and synthesis, with built-in reliability checks from the rest of the team. We developed a narrative synthesis of definitions and explanations of the relationship between DH and health management decision-making.

RESULTS

We sampled 61 of 181 included to synthesis definitions and concepts of DH in detail. We identified six common terms for data harmonisation: record linkage, data linkage, data warehousing, data sharing, data interoperability and health information exchange. We also identified nine key components of data harmonisation: DH involves (a) a process of multiple steps; (b) integrating, harmonising and bringing together different databases (c) two or more databases; (d) electronic data; (e) pooling data using unique patient identifiers; and (f) different types of data; (g) data found within and across different departments and institutions at facility, district, regional and national levels; (h) different types of technical activities; (i) has a specific scope. The relationship between DH and health management decision-making is not well-described in the literature. Several studies mentioned health providers' concerns about data completeness, data quality, terminology and coding of data elements as barriers to data utilisation for clinical decision-making.

CONCLUSION

To our knowledge, this scoping review was the first to synthesise definitions and concepts of DH and address the causal relationship between DH and health management decision-making. Future research is required to assess the effectiveness of data harmonisation on health management decision-making.

A Multisite Case Study of Caregiver Advise, Record, Enable Act Implementation

BACKGROUND AND OBJECTIVES

The Commonwealth of Pennsylvania passed the Caregiver Advise, Record, Enable (CARE) Act on April 20, 2016. We designed a study to explore early implementation at a large, integrated delivery financing system. Our goal was to assess the effects of system-level decisions on unit implementation and the incorporation of the CARE Act's three components into routine care delivery.

RESEARCH DESIGN AND METHODS

We conducted a multisite, ethnographic case study at three different hospitals' medical-surgical units. We conducted observations and semi-structured interview to understand the implementation process and the approach to caregiver identification, notification, and education. We used thematic analysis to code interviews and observations and linked findings to the Promoting Action on Research Implementation in Health Services framework.

RESULTS

Organizational context and electronic health record capability were instrumental to the CARE Act implementation and integration into workflow. The implementation team used a decentralized strategy and a variety of communication modes, relying on local hospital units to train staff and make the changes. We found that the system facilitated the CARE Act implementation by placing emphasis on the documentation and charting to demonstrate compliance with the legal requirements.

DISCUSSION AND IMPLICATIONS

General acute hospitals will be making or have made similar decisions on how to operationalize the regulatory components and demonstrate compliance with the CARE Act. This study can help to inform others as they design and improve their compliance and implementation strategies.

A qualitative study of physician perspectives on adaptation to electronic health records

Background

Electronic Health Records (EHRs) have the potential to improve many aspects of care and their use has increased in the last decade. Because of this, acceptance and adoption of EHRs is less of a concern than adaptation to use. To understand this issue more deeply, we conducted a qualitative study of physician perspectives on EHR use to identify factors that facilitate adaptation.

Methods

We conducted semi-structured interviews with 9 physicians across a range of inpatient disciplines at a large Academic Medical Center. Interviews were conducted by phone, lasting approximately 30 min, and were transcribed verbatim for analysis. We utilized inductive and deductive methods in our analysis.

Results

We identified 4 major themes related to EHR adaptation: impact of EHR changes on physicians, how physicians managed these changes, factors that facilitated adaptation to using the EHR and adapting to using the EHR in the patient encounter. Within these themes, physicians felt that a positive mindset toward change, providing upgrade training that was tailored to their role, and the opportunity to learn from colleagues were important facilitators of adaptation.

Conclusions

As EHR use moves beyond implementation, physicians continue to be required to adapt to the technology and to its frequent changes. Our study provides actionable findings that allow healthcare systems to focus on factors that facilitate the adaptation process for physicians.

Optimisation: defining and exploring a concept to enhance the impact of public health initiatives

BACKGROUND

Repeated, data-driven optimisation processes have been applied in many fields to rapidly transform the performance of products, processes and interventions. While such processes may similarly be employed to enhance the impact of public health initiatives, optimisation has not been defined in the context of public health and there has been little exploration of its key concepts.

METHODS

We used a modified, three-round Delphi study with an international group of researchers, public health policy-makers and practitioners to (1) generate a consensus-based definition of optimisation in the context of public health and (2i) describe key considerations for optimisation in that context. A pre-workshop literature review and elicitation of participant views regarding optimisation in public health (round 1) were followed by a daylong workshop and facilitated face-to-face group discussions to refine the definition and generate key considerations (round 2); finally, post-workshop discussions were undertaken to refine and finalise the findings (round 3). A thematic analysis was performed at each round. Study findings reflect an iterative consultation process with study participants.

RESULTS

Thirty of 33 invited individuals (91%) participated in the study. Participants reached consensus on the following definition of optimisation in public health: "A deliberate, iterative and data-driven process to improve a health intervention and/or its implementation to meet stakeholder-defined public health impacts within resource constraints". A range of optimisation considerations were explored. Optimisation was considered most suitable when existing public health initiatives are not sufficiently effective, meaningful improvements from an optimisation process are anticipated, quality data to assess impacts are routinely available, and there are stable and ongoing resources to support it. Participants believed optimisation could be applied to improve the impacts of an intervention, an implementation strategy or both, on outcomes valued by stakeholders or end users. While optimisation processes were thought to be facilitated by an understanding of the mechanisms of an intervention or implementation strategy, no agreement was reached regarding the best approach to inform decisions about modifications to improve impact.

CONCLUSIONS

The study findings provide a strong basis for future research to explore the potential impact of optimisation in the field of public health.

A systematic analysis of the optimization of computerized physician order entry and clinical decision support systems: A qualitative study in English hospitals

This article analyzes the range of system optimization activities taking place over an extended period following the implementation of computerized physician order entry and clinical decision support systems. We undertook 207 qualitative semi-structured interviews, 24 rounds of non-participant observations of meetings and system use, and collected 17 organizational documents in five hospitals over three time periods between 2011 and 2016. We developed a systematic analysis of system optimization activities with eight sub-categories grouped into three main categories. This delineates the range of system optimization activities including resolving misalignments between technology and clinical practices, enhancing the adopted system, and improving user capabilities to utilize/further optimize systems. This study highlights the optimization efforts by user organizations adopting multi-user, organization-spanning information technologies. Hospitals must continue to attend to change management for an extended period (up to 5 years post-implementation) and develop a strategy for long-term system optimization including sustained user engagement, training, and broader capability development to ensure smoother and quicker realization of benefits.

Capturing implementation knowledge: applying focused ethnography to study how implementers generate and manage knowledge in the scale-up of obesity prevention programs

Background

Bespoke electronic information management systems are being used for large-scale implementation delivery of population health programs. They record sites reached, coordinate activity, and track target achievement. However, many systems have been abandoned or failed to integrate into practice. We investigated the unusual endurance of an electronic information management system that has supported the successful statewide implementation of two evidence-based childhood obesity prevention programs for over 5 years. Upwards of 80% of implementation targets are being achieved.

Methods

We undertook co-designed partnership research with policymakers, practitioners, and IT designers. Our working hypothesis was that the science of getting evidence-based programs into practice rests on an in-depth understanding of the role programs play in the ongoing system of local relationships and multiple accountabilities. We conducted a 12-month multisite ethnography of 14 implementation teams, including their use of an electronic information management system, the Population Health Information Management System (PHIMS).

Results

All teams used PHIMS, but also drew on additional informal tools and technologies to manage, curate, and store critical information for implementation. We identified six functions these tools performed: (1) relationship management, (2) monitoring progress towards target achievement, (3) guiding and troubleshooting PHIMS use, (4) supporting teamwork, (5) evaluation, and (6) recording extra work at sites not related to program implementation. Informal tools enabled practitioners to create locally derived implementation knowledge and provided a conduit between knowledge generation and entry into PHIMS.

Conclusions

Implementation involves knowing and formalizing what to do, as well as how to do it. Our ethnography revealed the importance of hitherto uncharted knowledge about how practitioners develop implementation knowledge about how to do implementation locally, within the context of scaling up. Harnessing this knowledge for local use required adaptive and flexible systems which were enabled by informal tools and technologies. The use of informal tools also complemented and supported PHIMS use suggesting that both informal and standardized systems are required to support coordinated, large-scale implementation. While the content of the supplementary knowledge required to deliver the program was specific to context, functions like managing relationships with sites and helping others in the team may be applicable elsewhere.

Linking Quality Improvement and Health Information Technology through the QI-HIT Figure 8

The implementation of health information technology (HIT) is complex. A method for mitigating complexity is incrementalism. Incrementalism forms the foundation of both incremental software development models, like agile, and the Plan-Do-Study-Act cycles (PDSAs) of quality improvement (QI), yet we often fail to be incremental at the union of the disciplines. We propose a new model for HIT implementation that explicitly links incremental software development cycles with PDSAs, the QI-HIT Figure 8 (QIHIT-F8). We then detail a subsequent local HIT implementation where we demonstrated its use. The QIHIT-F8 requires a reprioritization of project management activities around tests of change, strong QI principles to detect these changes, and the presence of both baseline and prospective data about the chosen indicators. These conditions are most likely to be present when applied to indicators of high strategic importance to an organization.

Diabetes Care Editors' Expert Forum 2018: Managing Big Data for Diabetes Research and Care

Technological progress in the past half century has greatly increased our ability to collect, store, and transmit vast quantities of information, giving rise to the term "big data." This term refers to very large data sets that can be analyzed to identify patterns, trends, and associations. In medicine-including diabetes care and research-big data come from three main sources: electronic medical records (EMRs), surveys and registries, and randomized controlled trials (RCTs). These systems have evolved in different ways, each with strengths and limitations. EMRs continuously accumulate information about patients and make it readily accessible but are limited by missing data or data that are not quality assured. Because EMRs vary in structure and management, comparisons of data between health systems may be difficult. Registries and surveys provide data that are consistently collected and representative of broad populations but are limited in scope and may be updated only intermittently. RCT databases excel in the specificity, completeness, and accuracy of their data, but rarely include a fully representative sample of the general population. Also, they are costly to build and seldom maintained after a trial's end. To consider these issues, and the challenges and opportunities they present, the editors of Diabetes Care convened a group of experts in management of diabetes-related data on 21 June 2018, in conjunction with the American Diabetes Association's 78th Scientific Sessions in Orlando, FL. This article summarizes the discussion and conclusions of that forum, offering a vision of benefits that might be realized from prospectively designed and unified data-management systems to support the collective needs of clinical, surveillance, and research activities related to diabetes.

Systems engineering and human factors support of a system of novel EHR-integrated tools to prevent harm in the hospital

We established a Patient Safety Learning Laboratory comprising 2 core and 3 individual project teams to introduce a suite of digital health tools integrated with our electronic health record to identify, assess, and mitigate threats to patient safety in real time. One of the core teams employed systems engineering (SE) and human factors (HF) methods to analyze problems, design and develop improvements to intervention components, support implementation, and evaluate the system of systems as an integrated whole. Of the 29 participants, 19 and 16 participated in surveys and focus groups, respectively, about their perception of SE and HF. We identified 7 themes regarding use of the 12 SE and HF methods over the 4-year project. Qualitative methods (interviews, focus, groups, observations, usability testing) were most frequently used, typically by individual project teams, and generated the most insight. Quantitative methods (failure mode and effects analysis, simulation modeling) typically were used by the SE and HF core team but generated variable insight. A decentralized project structure led to challenges using these SE and HF methods at the project and systems level. We offer recommendations and insights for using SE and HF to support digital health patient safety initiatives.

Factors influencing the selection of a picture archiving and communication system: A qualitative study

BACKGROUND

Picture Archiving and Communication System (PACS) is an evolving technology in health care domains that is used for storage, management, retrieval, transfer, and delivery of medical images. Some medical centers in Iran have installed the PACS in recent years but have not used it appropriately. One of the problems in implementing this system is inability to select appropriate PACS. Several factors are involved in the selection process. The objective of this study was to determine the factors that influence PACS selection.

METHODS

This qualitative study aimed to identify factors influencing the PACS selection. Data were collected through semistructured interviews with 10 experts in three educational hospitals and in the position to make decision for the purchase of PACS. Data were analyzed by the conventional qualitative content analysis method proposed by Lundman and Graneheim.

RESULTS

Analyses achieved 11 subcategories in two specific and general categories that influence PACS selection. The specific category of this study included six subcategories, and the general category included five subcategories.

CONCLUSION

The results of this study determined that usability was the most important factor from the perspective of participants. Since the main users of a system have a critical role in adoption or rejection of a system, ease of use (usability) is significant and must be considered in system selection as a significant factor.

Secondary use of routine data in hospitals: description of a scalable analytical platform based on a business intelligence system

We describe a scalable platform for research-oriented analyses of routine data in hospitals, which evolved from a state-of-the-art business intelligence architecture for enterprise resource planning. This platform involves an in-memory database management system for data modeling and analytics and a high-performance cluster for more computing-intensive analytical tasks. Setting up platforms for research-oriented analyses is a highly dynamic, time-consuming, and costly process. In some health care institutions, effective research platforms may be derived from existing business intelligence systems.

The Heimdall Framework for Supporting Characterisation of Learning Health Systems

BACKGROUND

Learning Health Systems (LHS) can focus population medicine and Evidence Based Practice; smart technology delivering the next generation of improved healthcare described as Precision Medicine, and yet researchers in the LHS domain presently lack the ability to recognise their relevant works as falling within this domain.

OBJECTIVE

To review LHS literature and develop a framework describing the domain that can be used as a tool to analyse the literature and support researchers to identify health informatics investigations as falling with the domain of LHS.

METHOD

A scoping review is used to identify literature on which analysis was performed. This resolved the ontology and framework. The ontology was applied to quantify the distribution of classifications of LHS solutions. The framework was used to analyse and characterise the various works within the body of LHS literature.

RESULTS

The ontology and framework developed was shown to be easily applicable to the literature, consistently describing and representing the goals, intentions and solutions of each LHS investigation in the literature. More proposed or potential solutions are described in the literature than implemented LHS. This suggests immaturity in the domain and points to the existence of barriers preventing LHS realisation.

CONCLUSION

The lack of an ontology and framework may have been one of the causes for the failure to describe research works as falling within the LHS domain. Using our ontology and framework, LHS research works could be easily classified, demonstrating the comprehensiveness of our approach in contrast to earlier efforts.

Are all certified EHRs created equal? Assessing the relationship between EHR vendor and hospital meaningful use performance

Objective

The federal electronic health record (EHR) certification process was intended to ensure a baseline level of system quality and the ability to support meaningful use criteria. We sought to assess whether there was variation across EHR vendors in the degree to which hospitals using products from those vendors were able to achieve high levels of performance on meaningful use criteria.

Materials and Methods

We created a cross-sectional national hospital sample from the Office of the National Coordinator for Health Information Technology EHR Products Used for Meaningful Use Attestation public use file and the Centers for Medicare & Medicaid Services Medicare EHR Incentive Program Eligible Hospitals public use file. We used regression models to assess the relationship between vendor and hospital performance on 6 Stage 2 Meaningful Use criteria, controlling for hospital characteristics. We also calculated how much variation in performance is explained by vendor choice.

Results

We found significant associations between specific vendor and level of hospital performance for all 6 meaningful use criteria. Epic was associated with significantly higher performance on 5 of the 6 criteria; relationships for other vendors were mixed, with some associated with significantly worse performance on multiple criteria. EHR vendor choice accounted for between 7% and 34% of performance variation across the 6 criteria.

Discussion

A nontrivial proportion of variation in hospital meaningful use performance is explained by vendor choice, and certain vendors are more often associated with better meaningful use performance than others. Our results suggest that policy-makers should improve the certification process by including more "real-world" scenario testing and provider feedback or ratings to reduce this variation. Hospitals can use these results to guide interactions with vendors.

Conclusion

Vendor choice accounts for a meaningful proportion of variation in hospital meaningful use performance, and specific vendors are consistently associated with higher or lower performance across criteria.

SemEHR: A general-purpose semantic search system to surface semantic data from clinical notes for tailored care, trial recruitment, and clinical research

Objective

Unlocking the data contained within both structured and unstructured components of electronic health records (EHRs) has the potential to provide a step change in data available for secondary research use, generation of actionable medical insights, hospital management, and trial recruitment. To achieve this, we implemented SemEHR, an open source semantic search and analytics tool for EHRs.

Methods

SemEHR implements a generic information extraction (IE) and retrieval infrastructure by identifying contextualized mentions of a wide range of biomedical concepts within EHRs. Natural language processing annotations are further assembled at the patient level and extended with EHR-specific knowledge to generate a timeline for each patient. The semantic data are serviced via ontology-based search and analytics interfaces.

Results

SemEHR has been deployed at a number of UK hospitals, including the Clinical Record Interactive Search, an anonymized replica of the EHR of the UK South London and Maudsley National Health Service Foundation Trust, one of Europe's largest providers of mental health services. In 2 Clinical Record Interactive Search-based studies, SemEHR achieved 93% (hepatitis C) and 99% (HIV) F-measure results in identifying true positive patients. At King's College Hospital in London, as part of the CogStack program (github.com/cogstack), SemEHR is being used to recruit patients into the UK Department of Health 100 000 Genomes Project (genomicsengland.co.uk). The validation study suggests that the tool can validate previously recruited cases and is very fast at searching phenotypes; time for recruitment criteria checking was reduced from days to minutes. Validated on open intensive care EHR data, Medical Information Mart for Intensive Care III, the vital signs extracted by SemEHR can achieve around 97% accuracy.

Conclusion

Results from the multiple case studies demonstrate SemEHR's efficiency: weeks or months of work can be done within hours or minutes in some cases. SemEHR provides a more comprehensive view of patients, bringing in more and unexpected insight compared to study-oriented bespoke IE systems. SemEHR is open source, available at https://github.com/CogStack/SemEHR.

Implementation of a Medication Reconciliation Assistive Technology: A Qualitative Analysis

Objective: To aid the implementation of a medication reconciliation process within a hybrid primary-specialty care setting by using qualitative techniques to describe the climate of implementation and provide guidance for future projects. Methods: Guided by McMullen et al's Rapid Assessment Process¹, we performed semi-structured interviews prior to and iteratively throughout the implementation. Interviews were coded and analyzed using grounded theory² and cross-examined for validity. Results: We identified five barriers and five facilitators that impacted the implementation. Facilitators identified were process alignment with user values, and motivation and clinical champions fostered by the implementation team rather than the administration. Barriers included a perceived limited capacity for change, diverging priorities, and inconsistencies in process standards and role definitions. Discussion: A more complete, qualitative understanding of existing barriers and facilitators helps to guide critical decisions on the design and implementation of a successful medication reconciliation process.

Population-based analysis of patients with COPD in Catalonia: a cohort study with implications for clinical management

BACKGROUND

Clinical management of patients with chronic obstructive pulmonary disease (COPD) shows potential for improvement provided that patients' heterogeneities are better understood. The study addresses the impact of comorbidities and its role in health risk assessment.

OBJECTIVE

To explore the potential of health registry information to enhance clinical risk assessment and stratification.

DESIGN

Fixed cohort study including all registered patients with COPD in Catalonia (Spain) (7.5 million citizens) at 31 December 2014 with 1-year (2015) follow-up.

METHODS

A total of 264 830 patients with COPD diagnosis, based on the International Classification of Diseases (Ninth Revision) coding, were assessed. Performance of multiple logistic regression models for the six main dependent variables of the study: mortality, hospitalisations (patients with one or more admissions; all cases and COPD-related), multiple hospitalisations (patients with at least two admissions; all causes and COPD-related) and users with high healthcare costs. Neither clinical nor forced spirometry data were available.

RESULTS

Multimorbidity, assessed with the adjusted morbidity grouper, was the covariate with the highest impact in the predictive models, which in turn showed high performance measured by the C-statistics: (1) mortality (0.83), (2 and 3) hospitalisations (all causes: 0.77; COPD-related: 0.81), (4 and 5) multiple hospitalisations (all causes: 0.80; COPD-related: 0.87) and (6) users with high healthcare costs (0.76). Fifteen per cent of individuals with highest healthcare costs to year ratio represented 59% of the overall costs of patients with COPD.

CONCLUSIONS

The results stress the impact of assessing multimorbidity with the adjusted morbidity grouper on considered health indicators, which has implications for enhanced COPD staging and clinical management.

TRIAL REGISTRATION NUMBER

NCT02956395.

A systematic review of the types and causes of prescribing errors generated from using computerized provider order entry systems in primary and secondary care

Objective

To understand the different types and causes of prescribing errors associated with computerized provider order entry (CPOE) systems, and recommend improvements in these systems.

Materials and Methods

We conducted a systematic review of the literature published between January 2004 and June 2015 using three large databases: the Cumulative Index to Nursing and Allied Health Literature, Embase, and Medline. Studies that reported qualitative data about the types and causes of these errors were included. A narrative synthesis of all eligible studies was undertaken.

Results

A total of 1185 publications were identified, of which 34 were included in the review. We identified 8 key themes associated with CPOE-related prescribing errors: computer screen display, drop-down menus and auto-population, wording, default settings, nonintuitive or inflexible ordering, repeat prescriptions and automated processes, users' work processes, and clinical decision support systems. Displaying an incomplete list of a patient's medications on the computer screen often contributed to prescribing errors. Lack of system flexibility resulted in users employing error-prone workarounds, such as the addition of contradictory free-text comments. Users' misinterpretations of how text was presented in CPOE systems were also linked with the occurrence of prescribing errors.

Discussion and Conclusions

Human factors design is important to reduce error rates. Drop-down menus should be designed with safeguards to decrease the likelihood of selection errors. Development of more sophisticated clinical decision support, which can perform checks on free-text, may also prevent errors. Further research is needed to ensure that systems minimize error likelihood and meet users' workflow expectations.

Prototype Development: Context-Driven Dynamic XML Ophthalmologic Data Capture Application

Background

The capture and integration of structured ophthalmologic data into electronic health records (EHRs) has historically been a challenge. However, the importance of this activity for patient care and research is critical.

Objective

The purpose of this study was to develop a prototype of a context-driven dynamic extensible markup language (XML) ophthalmologic data capture application for research and clinical care that could be easily integrated into an EHR system.

Methods

Stakeholders in the medical, research, and informatics fields were interviewed and surveyed to determine data and system requirements for ophthalmologic data capture. On the basis of these requirements, an ophthalmology data capture application was developed to collect and store discrete data elements with important graphical information.

Results

The context-driven data entry application supports several features, including ink-over drawing capability for documenting eye abnormalities, context-based Web controls that guide data entry based on preestablished dependencies, and an adaptable database or XML schema that stores Web form specifications and allows for immediate changes in form layout or content. The application utilizes Web services to enable data integration with a variety of EHRs for retrieval and storage of patient data.

Conclusions

This paper describes the development process used to create a context-driven dynamic XML data capture application for optometry and ophthalmology. The list of ophthalmologic data elements identified as important for care and research can be used as a baseline list for future ophthalmologic data collection activities.

An Evaluation of Understandability of Patient Journey Models in Mental Health

BACKGROUND

There is a significant trend toward implementing health information technology to reduce administrative costs and improve patient care. Unfortunately, little awareness exists of the challenges of integrating information systems with existing clinical practice. The systematic integration of clinical processes with information system and health information technology can benefit the patients, staff, and the delivery of care.

OBJECTIVES

This paper presents a comparison of the degree of understandability of patient journey models. In particular, the authors demonstrate the value of a relatively new patient journey modeling technique called the Patient Journey Modeling Architecture (PaJMa) when compared with traditional manufacturing based process modeling tools. The paper also presents results from a small pilot case study that compared the usability of 5 modeling approaches in a mental health care environment.

METHOD

Five business process modeling techniques were used to represent a selected patient journey. A mix of both qualitative and quantitative methods was used to evaluate these models. Techniques included a focus group and survey to measure usability of the various models.

RESULTS

The preliminary evaluation of the usability of the 5 modeling techniques has shown increased staff understanding of the representation of their processes and activities when presented with the models. Improved individual role identification throughout the models was also observed. The extended version of the PaJMa methodology provided the most clarity of information flows for clinicians.

CONCLUSIONS

The extended version of PaJMa provided a significant improvement in the ease of interpretation for clinicians and increased the engagement with the modeling process. The use of color and its effectiveness in distinguishing the representation of roles was a key feature of the framework not present in other modeling approaches. Future research should focus on extending the pilot case study to a more diversified group of clinicians and health care support workers.