Dataset Information

Implementation and adoption of nationwide electronic health records in secondary care in England: qualitative analysis of interim results from a prospective national evaluation.

ABSTRACT:

Objectives

To describe and evaluate the implementation and adoption of detailed electronic health records in secondary care in England and thereby provide early feedback for the ongoing local and national rollout of the NHS Care Records Service.

Design

A mixed methods, longitudinal, multisite, socio-technical case study.

Setting

Five NHS acute hospital and mental health trusts that have been the focus of early implementation efforts and at which interim data collection and analysis are complete. Data sources and analysis Dataset for the evaluation consists of semi-structured interviews, documents and field notes, observations, and quantitative data. Qualitative data were analysed thematically with a socio-technical coding matrix, combined with additional themes that emerged from the data. Main results Hospital electronic health record applications are being developed and implemented far more slowly than was originally envisioned; the top-down, standardised approach has needed to evolve to admit more variation and greater local choice, which hospital trusts want in order to support local activity. Despite considerable delays and frustrations, support for electronic health records remains strong, including from NHS clinicians. Political and financial factors are now perceived to threaten nationwide implementation of electronic health records. Interviewees identified a range of consequences of long term, centrally negotiated contracts to deliver the NHS Care Records Service in secondary care, particularly as NHS trusts themselves are not party to these contracts. These include convoluted communication channels between different stakeholders, unrealistic deployment timelines, delays, and applications that could not quickly respond to changing national and local NHS priorities. Our data suggest support for a "middle-out" approach to implementing hospital electronic health records, combining government direction with increased local autonomy, and for restricting detailed electronic health record sharing to local health communities.

Conclusions

Experiences from the early implementation sites, which have received considerable attention, financial investment and support, indicate that delivering improved healthcare through nationwide electronic health records will be a long, complex, and iterative process requiring flexibility and local adaptability both with respect to the systems and the implementation strategy. The more tailored, responsive approach that is emerging is becoming better aligned with NHS organisations' perceived needs and is, if pursued, likely to deliver clinically useful electronic health record systems.

SUBMITTER: Robertson A

PROVIDER: S-EPMC2933355 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:AIMS: To investigate the perceptions and reported practices of mental health hospital staff using national hospital electronic health records (EHRs) in order to inform future implementations, particularly in acute mental health settings. METHODS: Thematic analysis of interviews with a wide range of clinical, information technology (IT), managerial and other staff at two early adopter mental health National Health Service (NHS) hospitals in London, UK, implementing national EHRs. RESULTS: We analysed 33 interviews. We first sought out examples of workarounds, such as delayed data entry, entering data in wrong places and individuals using the EHR while logged in as a colleague, then identified possible reasons for the reported workarounds. Our analysis identified four main categories of factors contributing to workarounds (i.e., operational, cultural, organisational and technical). Operational factors included poor system integration with existing workflows and the system not meeting users' perceived needs. Cultural factors involved users' competence with IT and resistance to change. Organisational factors referred to insufficient organisational resources and training, while technical factors included inadequate local technical infrastructure. Many of these factors, such as integrating the EHR system with day-to-day operational processes, staff training and adequate local IT infrastructure, were likely to apply to system implementations in various settings, but we also identified factors that related particularly to implementing EHRs in mental health hospitals, for example: EHR system incompatibility with IT systems used by mental health-related sectors, notably social services; the EHR system lacking specific, mental health functionalities and options; and clinicians feeling unable to use computers while attending to distressed psychiatric patients. CONCLUSIONS: A better conceptual model of reasons for workarounds should help with designing, and supporting the implementation and adoption of, EHRs for use in hospital mental health settings.

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Project description:BackgroundThe rate of adoption of electronic medical record (EMR) systems has increased internationally, and new EMR adoption is currently a major topic in Japan. However, no study has performed a detailed analysis of longitudinal data to evaluate the changes in the EMR adoption status over time.ObjectiveThis study aimed to evaluate the changes in the EMR adoption status over time in hospitals and clinics in Japan and to examine the facility and regional factors associated with these changes.MethodsSecondary longitudinal data were created by matching data in fiscal year (FY) 2011 and FY 2014 using reference numbers. EMR adoption status was defined as "EMR adoption," "specified adoption schedule," or "no adoption schedule." Data were obtained for hospitals (n=4410) and clinics (n=67,329) that had no adoption schedule in FY 2011 and for hospitals (n=1068) and clinics (n=3132) with a specified adoption schedule in FY 2011. The EMR adoption statuses of medical institutions in FY 2014 were also examined. A multinomial logistic model was used to investigate the associations between EMR adoption status in FY 2014 and facility and regional factors in FY 2011. Considering the regional variations of these models, multilevel analyses with second levels were conducted. These models were constructed separately for hospitals and clinics, resulting in four multinomial logistic models. The odds ratio (OR) and 95% Bayesian credible interval (CI) were estimated for each variable.ResultsA total of 6.9% of hospitals and 14.82% of clinics with no EMR adoption schedules in FY 2011 had adopted EMR by FY 2014, while 10.49% of hospitals and 33.65% of clinics with specified adoption schedules in FY 2011 had cancelled the scheduled adoption by FY 2014. For hospitals with no adoption schedules in FY 2011, EMR adoption/scheduled adoption was associated with practice size characteristics, such as number of outpatients (from quantile 4 to quantile 1: OR 1.67, 95% CI 1.005-2.84 and OR 2.40, 95% CI 1.80-3.21, respectively), and number of doctors (from quantile 4 to quantile 1: OR 4.20, 95% CI 2.39-7.31 and OR 2.02, 95% CI 1.52-2.64, respectively). For clinics with specified EMR adoption schedules in FY 2011, the factors negatively associated with EMR adoption/cancellation of scheduled EMR adoption were the presence of beds (quantile 4 to quantile 1: OR 0.57, 95% CI 0.45-0.72 and OR 0.74, 95% CI 0.58-0.96, respectively) and having a private establisher (quantile 4 to quantile 1: OR 0.27, 95% CI 0.13-0.55 and OR 0.43, 95% CI 0.19-0.91, respectively). No regional factors were significantly associated with the EMR adoption status of hospitals with no EMR adoption schedules; population density was positively associated with EMR adoption in clinics with no EMR adoption schedule (quantile 4 to quantile 1: OR 1.49, 95% CI 1.32-1.69).ConclusionsDifferent approaches are needed to promote new adoption of EMR systems in hospitals as compared to clinics. It is important to induce decision making in small- and medium-sized hospitals, and regional postdecision technical support is important to avoid cancellation of scheduled EMR adoption in clinics.

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