Safety Assurance Factors for Electronic Health Record Resilience (SAFER): study protocol

What Goes Up, Must Come Down: A State-of-the-Art Electronic Health Record Downtime and Uptime Procedure in a Metropolitan Health Setting

BACKGROUND

Electronic health records (EHRs) are used at most hospitals around the world, and downtime events are inevitable and common. Downtime represents a risky time for patients because patient information and critical EHR functionality are unavailable. Many institutions have used EHRs for years, with health professionals less likely to be familiar or comfortable with paper-based processes, resulting in an increased risk of errors during downtimes. There is currently limited guidance available on how to develop and operationalize downtime procedure at a local level. In this paper, we fill this gap by describing our state-of-the-art downtime and uptime procedure and its evaluation.

METHOD

A district-wide downtime and uptime procedure was revised and standardized based on lessons learned from other health care organizations. The procedure outlines downtime and uptime preparations including downtime drills, downtime viewer auditing, and downtime education; downtime response including activating downtime and tracking patient changes; and uptime recovery including medication reconciliation and uptime documentation.

IMPLEMENTATION

We implemented our new procedure across the district during an 8-hour planned downtime. A district downtime planning committee was formed, and a virtual command center was established to coordinate the downtime and uptime events. During downtime and uptime, onsite support was provided by the district's health informatics teams and clinicians. Data recovery was completed safely and efficiently with the revised uptime process. Following the event, we gathered staff feedback and reflections on implementing the procedure which highlighted its success but also revealed some areas for further improvement.

CONCLUSION

In this paper, we describe a state-of-the-art EHR downtime and uptime procedure and lessons learned from its implementation. The implementation was successful with staff well prepared and information reconciled efficiently ensuring safe continuity of care. It was only through extensive planning, significant commitment, and engagement of all stakeholders that this outcome was possible.

Developing an Ophthalmology Clinical Decision Support System to Identify Patients for Low Vision Rehabilitation

Purpose

The purpose of this study was to develop and evaluate an electronic health record (EHR) clinical decision support system to identify patients meeting criteria for low vision rehabilitation (LVR) referral.

Methods

In this quality improvement project, we applied a user-centered design approach to develop an interactive electronic alert for LVR referral within the Johns Hopkins Wilmer Eye Institute. We invited 15 ophthalmology physicians from 8 subspecialties to participate in the design and implementation, and to provide user experience feedback. The three project phases incorporated development evaluation, feedback analysis, and system refinement. We report on the final alert design, firing accuracy, and user experiences.

Results

The alert was designed as physician-centered and patient-specific. Alert firing relied on visual acuity and International Classification of Diseases (ICD)-10 diagnosis (hemianopia/quadrantanopia) criteria. The alert suppression considerations included age < 5 years, recent surgeries, prior LVR visit, and related alert actions. False positive rate (firing when alert should have been suppressed or when firing criteria not met) was 0.2%. The overall false negative rate (alert not firing when visual acuity or encounter diagnosis criteria met) was 5.6%. Of the 13 physicians who completed the survey, 8 agreed that the alert is easy to use, and 12 would consider ongoing usage.

Conclusions

This EHR-based clinical decision support system shows reliable firing metrics in identifying patients with vision impairment and promising acceptance by ophthalmologist users to facilitate care and LVR referral.

Translational Relevance

The use of real-time data offers an opportunity to translate ophthalmic guidelines and best practices into systematic action for clinical care and research purposes across subspecialties.

Identification of Design Criteria to Improve Patient Care in Electronic Health Record Downtime

OBJECTIVE

Design criteria specifications (needs, obstacles, and context-of-use considerations) for continuing safe and efficient patient care activities during downtime were identified by using phenomenological analysis.

METHODS

Interview transcripts from medical personnel who had experience with downtime incidents were examined using a phenomenological approach. This process allowed for the identification of design criteria for performing downtime patient care activities.

RESULTS

A substantial variation in criteria was found from participants in different roles. The differences suggest opportunities to address downtime that may require attention to individual roles.

CONCLUSIONS

Workload distribution and communication are significant issues in patient care during downtime. There may not be an equal work distribution, leading to an increased workload for some personnel during downtime. Phenomenological analysis was completed after participants were interviewed, indicating it is a viable post hoc approach. Some downtime criteria were identified as potential guidelines for the development of better downtime contingency plans.

How can we partner with electronic health record vendors on the complex journey to safer health care?

The Office of the National Coordinator for Health Information Technology released the Safety Assurance Factors for EHR Resilience (SAFER) guides in 2014. Our group developed these guides covering key facets of both electronic health record (EHR) infrastructure (eg, system configuration, contingency planning for downtime, and system-to-system interfaces) and clinical processes (eg, computer-based provider order entry with clinical decision support, test result reporting, patient identification, and clinician-to-clinician communication). The SAFER guides encourage healthy relationships between EHR vendors and users. We conducted a qualitative study over 12 months. We visited 9 health care organizations ranging in size from 1-doctor outpatient clinics to large, multisite, multihospital integrated delivery networks. We interviewed and observed clinicians, IT professionals, and administrators. From the interview transcripts and observation field notes, we identified overarching themes: technical functionality, usability, standards, testing, workflow processes, personnel to support implementation and use, infrastructure, and clinical content. In addition, we identified health care organization-EHR vendor working relationships: marine drill sergeant, mentor, development partner, seller, and parasite. We encourage health care organizations and EHR vendors to develop healthy working relationships to help address the tasks required to design, develop, implement, and maintain EHRs required to achieve safer and higher quality health care.

The effects and preventability of 2627 patient safety incidents related to health information technology failures: a retrospective analysis of 10 years of incident reporting in England and Wales

BACKGROUND

The use of health information technology (IT) is rapidly increasing to support improvements in the delivery of care. Although health IT is delivering huge benefits, new technology can also introduce unique risks. Despite these risks, evidence on the preventability and effects of health IT failures on patients is scarce. In our study we therefore sought to evaluate the preventability and effects of health IT failures by examining patient safety incidents in England and Wales.

METHODS

We designed our study as a retrospective analysis of 10 years of incident reporting in England and Wales. We used text mining with the words "computer", "system", "workstation", and "network" to explore free-text incident descriptors to identify incidents related to health IT failures following a previously described approach. We then applied an n-gram model of searching to identify contiguous sequences of words and provide spatial context. We examined incident details, recorded harm, and preventability. Standard descriptive statistics were applied. Degree of harm was identified according to standardised definitions and preventability was assessed by two independent reviewers.

FINDINGS

We identified 2627 incidents related to health IT failures. 2557 (97%) of 2627 incidents were assessed for harm (70 incidents were excluded). 2106 (82%) of 2557 health IT failures caused no harm to patients, 331 (13%) caused low harm, 102 (4%) caused moderate harm, 14 (1%) caused severe harm, and four (<1%) contributed to the death of a patient. 1964 (75%) of 2627 incidents were deemed to be preventable.

INTERPRETATION

Health IT is fundamental to the delivery of high-quality care, yet there is a poor understanding of the effects of IT failures on patient safety and whether they can be prevented. Failures are complex and involve interlinked aspects of technology, people, and the environment. Health IT failures are undoubtedly a potential source of substantial harm, but they are likely to be under-reported. Worryingly, three-quarters of IT failures are potentially preventable. There is a need to see health IT as a fundamental tenet of patient safety, develop better methods for capturing the effects of IT failures on patients, and adopt simple measures to reduce their probability and mitigate their risk.

FUNDING

The National Institutes of Health Research Imperial Patient Safety Translational Research Centre at Imperial College London.

A regulatory perspective on the influence of health information technology on organisational quality and safety in England

Health information technology can transform and enhance the quality and safety of care, but it may also introduce new risks. This study analysed 130 healthcare regulator inspection reports and organisational digital maturity scores in order to characterise the impact of health information technology on quality and safety from a regulatory perspective. Although digital maturity and the positive use of health information technology are significantly associated with overall organisational quality, the negative effects of health information technology are frequently and more commonly identified by regulators. The poor usability of technology, lack of easy access to systems and data and the incorrect use of health information technology are the most commonly identified areas adversely affecting quality and safety. There is a need to understand the full risks and benefits of health information technology from the perspective of all stakeholders, including patients, end-users, providers and regulators in order to best inform future practice and regulation.

Healthcare delivery for paediatric and adolescent diabetes in low resource settings: Type 1 diabetes clinics in Uganda

The management of type 1 diabetes (T1DM) includes setting up organised follow-up clinics. A programme for establishing such clinics in Uganda commenced in 2009. The clinics were established along the chronic care model and were integrated into the health structure of other chronic diseases. Web-based electronic medical records were utilised to establish a centralised registry. All children with diabetes below 18 years of age were encouraged to enrol into the programme by attending the nearest established T1DM clinic. At the commencement of the programme, there were 178 patients with T1DM receiving care in various health facilities but without organised follow-up T1DM clinics. These patients were subsequently enrolled into the programme and as of June 30, 2018, the programme had a total of 32 clinics with 1187 children; 3 with neonatal diabetes. Challenges encountered included difficulties in timely diagnosis, failure to provide adequate care in the remote rural areas and failure to achieve pre-defined glycated haemoglobin (HbA1c) goals. Despite these challenges, this observational study demonstrates that healthcare delivery for T1DM organised along the chronic care model and supported by web-based electronic medical records is achievable and provides care that is sustainable. Addressing the encountered challenges should result in improved outcomes for T1DM.

Implications of electronic health record downtime: an analysis of patient safety event reports

Objective

We sought to understand the types of clinical processes, such as image and medication ordering, that are disrupted during electronic health record (EHR) downtime periods by analyzing the narratives of patient safety event report data.

Materials and Methods

From a database of 80 381 event reports, 76 reports were identified as explicitly describing a safety event associated with an EHR downtime period. These reports were analyzed and categorized based on a developed code book to identify the clinical processes that were impacted by downtime. We also examined whether downtime procedures were in place and followed.

Results

The reports were coded into categories related to their reported clinical process: Laboratory, Medication, Imaging, Registration, Patient Handoff, Documentation, History Viewing, Delay of Procedure, and General. A majority of reports (48.7%, n = 37) were associated with lab orders and results, followed by medication ordering and administration (14.5%, n = 11). Incidents commonly involved patient identification and communication of clinical information. A majority of reports (46%, n = 35) indicated that downtime procedures either were not followed or were not in place. Only 27.6% of incidents (n = 21) indicated that downtime procedures were successfully executed.

Discussion

Patient safety report data offer a lens into EHR downtime-related safety hazards. Important areas of risk during EHR downtime periods were patient identification and communication of clinical information; these should be a focus of downtime procedure planning to reduce safety hazards.

Conclusion

EHR downtime events pose patient safety hazards, and we highlight critical areas for downtime procedure improvement.

Essential activities for electronic health record safety: A qualitative study

Electronic health record-caused safety risks are an unintended consequence of the implementation of clinical systems. To identify activities essential to assuring that the electronic health record is managed and used safely, we used the Rapid Assessment Process, a collection of qualitative methods. A multidisciplinary team conducted visits to five healthcare sites to learn about best practices. Although titles and roles were very different across sites, certain tasks considered necessary by our subjects were remarkably similar. We identified 10 groups of activities/tasks in three major areas. Area A, decision-making activities, included overseeing, planning, and reviewing to assure electronic health record safety. Area B, organizational learning activities, involved monitoring, testing, analyzing, and reporting. Finally, Area C, user-related activities, included training, communication, and building clinical decision support. To minimize electronic health record-related patient safety risks, leaders in healthcare organizations should ensure that these essential activities are performed.

Adherence to recommended electronic health record safety practices across eight health care organizations

Objective

The Safety Assurance Factors for EHR Resilience (SAFER) guides were released in 2014 to help health systems conduct proactive risk assessment of electronic health record (EHR)- safety related policies, processes, procedures, and configurations. The extent to which SAFER recommendations are followed is unknown.

Methods

We conducted risk assessments of 8 organizations of varying size, complexity, EHR, and EHR adoption maturity. Each organization self-assessed adherence to all 140 unique SAFER recommendations contained within 9 guides (range 10-29 recommendations per guide). In each guide, recommendations were organized into 3 broad domains: "safe health IT" (total 45 recommendations); "using health IT safely" (total 80 recommendations); and "monitoring health IT" (total 15 recommendations).

Results

The 8 sites fully implemented 25 of 140 (18%) SAFER recommendations. Mean number of "fully implemented" recommendations per guide ranged from 94% (System Interfaces-18 recommendations) to 63% (Clinical Communication-12 recommendations). Adherence was higher for "safe health IT" domain (82.1%) vs "using health IT safely" (72.5%) and "monitoring health IT" (67.3%).

Conclusions

Despite availability of recommendations on how to improve use of EHRs, most recommendations were not fully implemented. New national policy initiatives are needed to stimulate implementation of these best practices.

Evaluation of Electronic Medical Records on Nurses' Time Allocation During Cesarean Delivery

BACKGROUND

The impact of the electronic medical record (EMR) on nursing workload is not well understood. The objective of this descriptive study was to measure the actual and perceived time that nurses spend on the EMR in the operating room during cesarean births.

METHODS

Twenty scheduled cesarean births were observed. An observer timed the circulating nurse's EMR use during each case. Immediately after each case, the nurse completed a questionnaire to estimate EMR time allocation during the case and their desired time allocation for a typical case. They were also asked about time allotted to various activities preoperatively, intraoperatively, and postoperatively for a typical cesarean birth.

RESULTS

Mean observed nurse EMR time was 36 ± 12 minutes per case, 40% ± 10% of the duration of the cesarean delivery. Nurses tended to estimate greater time spent on the EMR; the perceived mean proportion of time spent on the EMR (55%) was greater than the actual timed value of 40% (P = 0.020). Nurse's desired amount of time spent on the EMR was 22% ± 15% of the case duration, significantly less than actual time spent on the EMR (P = 0.007).

CONCLUSIONS

On average, nurses spent 40% of their intraoperative time on the EMR during cesarean births, and this time burden was distributed across the perioperative period. These findings highlight the time burden of EMRs and suggest that EMR functionality should be better aligned with end-user needs. Future studies are needed to better understand the impacts of intraoperative EMR use on patient safety and patient/nursing/clinician communication.

Electronic Health Record-Related Safety Concerns: A Cross-Sectional Survey of Electronic Health Record Users

Background

The rapid expansion in the use of electronic health records (EHR) has increased the number of medical errors originating in health information systems (HIS). The sociotechnical approach helps in understanding risks in the development, implementation, and use of EHR and health information technology (HIT) while accounting for complex interactions of technology within the health care system.

Objective

This study addresses two important questions: (1) “which of the common EHR error types are associated with perceived high- and extreme-risk severity ratings among EHR users?”, and (2) “which variables are associated with high- and extreme-risk severity ratings?”

Methods

This study was a quantitative, non-experimental, descriptive study of EHR users. We conducted a cross-sectional web-based questionnaire study at the largest hospital district in Finland. Statistical tests included the reliability of the summative scales tested with Cronbach’s alpha. Logistic regression served to assess the association of the independent variables to each of the eight risk factors examined.

Results

A total of 2864 eligible respondents provided the final data. Almost half of the respondents reported a high level of risk related to the error type “extended EHR unavailability”. The lowest overall risk level was associated with “selecting incorrectly from a list of items”. In multivariate analyses, profession and clinical unit proved to be the strongest predictors for high perceived risk. Physicians perceived risk levels to be the highest (P<.001 in six of eight error types), while emergency departments, operating rooms, and procedure units were associated with higher perceived risk levels (P<.001 in four of eight error types). Previous participation in eLearning courses on EHR-use was associated with lower risk for some of the risk factors.

Conclusions

Based on a large number of Finnish EHR users in hospitals, this study indicates that HIT safety hazards should be taken very seriously, particularly in operating rooms, procedure units, emergency departments, and intensive care units/critical care units. Health care organizations should use proactive and systematic assessments of EHR risks before harmful events occur. An EHR training program should be compulsory for all EHR users in order to address EHR safety concerns resulting from the failure to use HIT appropriately.

Advancing the science of measurement of diagnostic errors in healthcare: the Safer Dx framework

Diagnostic errors are major contributors to harmful patient outcomes, yet they remain a relatively understudied and unmeasured area of patient safety. Although they are estimated to affect about 12 million Americans each year in ambulatory care settings alone, both the conceptual and pragmatic scientific foundation for their measurement is under-developed. Health care organizations do not have the tools and strategies to measure diagnostic safety and most have not integrated diagnostic error into their existing patient safety programs. Further progress toward reducing diagnostic errors will hinge on our ability to overcome measurement-related challenges. In order to lay a robust groundwork for measurement and monitoring techniques to ensure diagnostic safety, we recently developed a multifaceted framework to advance the science of measuring diagnostic errors (The Safer Dx framework). In this paper, we describe how the framework serves as a conceptual foundation for system-wide safety measurement, monitoring and improvement of diagnostic error. The framework accounts for the complex adaptive sociotechnical system in which diagnosis takes place (the structure), the distributed process dimensions in which diagnoses evolve beyond the doctor's visit (the process) and the outcomes of a correct and timely "safe diagnosis" as well as patient and health care outcomes (the outcomes). We posit that the Safer Dx framework can be used by a variety of stakeholders including researchers, clinicians, health care organizations and policymakers, to stimulate both retrospective and more proactive measurement of diagnostic errors. The feedback and learning that would result will help develop subsequent interventions that lead to safer diagnosis, improved value of health care delivery and improved patient outcomes.

An analysis of electronic health record-related patient safety concerns

OBJECTIVE

A recent Institute of Medicine report called for attention to safety issues related to electronic health records (EHRs). We analyzed EHR-related safety concerns reported within a large, integrated healthcare system.

METHODS

The Informatics Patient Safety Office of the Veterans Health Administration (VA) maintains a non-punitive, voluntary reporting system to collect and investigate EHR-related safety concerns (ie, adverse events, potential events, and near misses). We analyzed completed investigations using an eight-dimension sociotechnical conceptual model that accounted for both technical and non-technical dimensions of safety. Using the framework analysis approach to qualitative data, we identified emergent and recurring safety concerns common to multiple reports.

RESULTS

We extracted 100 consecutive, unique, closed investigations between August 2009 and May 2013 from 344 reported incidents. Seventy-four involved unsafe technology and 25 involved unsafe use of technology. A majority (70%) involved two or more model dimensions. Most often, non-technical dimensions such as workflow, policies, and personnel interacted in a complex fashion with technical dimensions such as software/hardware, content, and user interface to produce safety concerns. Most (94%) safety concerns related to either unmet data-display needs in the EHR (ie, displayed information available to the end user failed to reduce uncertainty or led to increased potential for patient harm), software upgrades or modifications, data transmission between components of the EHR, or 'hidden dependencies' within the EHR.

DISCUSSION

EHR-related safety concerns involving both unsafe technology and unsafe use of technology persist long after 'go-live' and despite the sophisticated EHR infrastructure represented in our data source. Currently, few healthcare institutions have reporting and analysis capabilities similar to the VA.

CONCLUSIONS

Because EHR-related safety concerns have complex sociotechnical origins, institutions with long-standing as well as recent EHR implementations should build a robust infrastructure to monitor and learn from them.

Resilient Practices in Maintaining Safety of Health Information Technologies

Electronic health record systems (EHRs) can improve safety and reliability of health care, but they can also introduce new vulnerabilities by failing to accommodate changes within a dynamic EHR-enabled health care system. Continuous assessment and improvement is thus essential for achieving resilience in EHR-enabled health care systems. Given the rapid adoption of EHRs by many organizations that are still early in their experiences with EHR safety, it is important to understand practices for maintaining resilience used by organizations with a track record of success in EHR use. We conducted interviews about safety practices with 56 key informants (including information technology managers, chief medical information officers, physicians, and patient safety officers) at two large health care systems recognized as leaders in EHR use. We identified 156 references to resilience-related practices from 41 informants. Framework analysis generated five categories of resilient practices: (a) sensitivity to dynamics and interdependencies affecting risks, (b) basic monitoring and responding practices, (c) management of practices and resources for monitoring and responding, (d) sensitivity to risks beyond the horizon, and (e) reflecting on risks with the safety and quality control process itself. The categories reflect three functions that facilitate resilience: reflection, transcending boundaries, and involving sharp-end practitioners in safety management.

Contingency planning for electronic health record-based care continuity: a survey of recommended practices

BACKGROUND

Reliable health information technology (HIT) in general, and electronic health record systems (EHRs) in particular are essential to a high-performing healthcare system. When the availability of EHRs are disrupted, alternative methods must be used to maintain the continuity of healthcare.

METHODS

We developed a survey to assess institutional practices to handle situations when EHRs were unavailable for use (downtime preparedness). We used literature reviews and expert opinion to develop items that assessed the implementation of potentially useful practices. We administered the survey to U.S.-based healthcare institutions that were members of a professional organization that focused on collaboration and sharing of HIT-related best practices among its members. All members were large integrated health systems.

RESULTS

We received responses from 50 of the 59 (84%) member institutions. Nearly all (96%) institutions reported at least one unplanned downtime (of any length) in the last 3 years and 70% had at least one unplanned downtime greater than 8h in the last 3 years. Three institutions reported that one or more patients were injured as a result of either a planned or unplanned downtime. The majority of institutions (70-85%) had implemented a portion of the useful practices we identified, but very few practices were followed by all organizations.

CONCLUSIONS

Unexpected downtimes related to EHRs appear to be fairly common among institutions in our survey. Most institutions had only partially implemented comprehensive contingency plans to maintain safe and effective healthcare during unexpected EHRs downtimes.

A qualitative study identifying the cost categories associated with electronic health record implementation in the UK

OBJECTIVE

We conducted a prospective evaluation of different forms of electronic health record (EHR) systems to better understand the costs incurred during implementation and the factors that can influence these costs.

METHODS

We selected a range of diverse organizations across three different geographical areas in England that were at different stages of implementing three centrally procured applications, that is, iSOFT's Lorenzo Regional Care, Cerner's Millennium, and CSE's RiO. 41 semi-structured interviews were conducted with hospital staff, members of the implementation team, and those involved in the implementation at a national level.

RESULTS

Four main overarching cost categories were identified: infrastructure (eg, hardware and software), personnel (eg, training team), estates/facilities (eg, space), and other (eg, training materials). Many factors were felt to impact on these costs, with different hospitals choosing varying amounts and types of infrastructure, diverse training approaches for staff, and different software applications to integrate with the new system.

CONCLUSIONS

Improving the quality and safety of patient care through EHR adoption is a priority area for UK and US governments and policy makers worldwide. With cost considered one of the most significant barriers, it is important for hospitals and governments to be clear from the outset of the major cost categories involved and the factors that may impact on these costs. Failure to adequately train staff or to follow key steps in implementation has preceded many of the failures in this domain, which can create new safety hazards.

Enabling large-scale biomedical analysis in the cloud

Recent progress in high-throughput instrumentations has led to an astonishing growth in both volume and complexity of biomedical data collected from various sources. The planet-size data brings serious challenges to the storage and computing technologies. Cloud computing is an alternative to crack the nut because it gives concurrent consideration to enable storage and high-performance computing on large-scale data. This work briefly introduces the data intensive computing system and summarizes existing cloud-based resources in bioinformatics. These developments and applications would facilitate biomedical research to make the vast amount of diversification data meaningful and usable.

Trends in health information technology safety: from technology-induced errors to current approaches for ensuring technology safety

OBJECTIVES

Health information technology (HIT) research findings suggested that new healthcare technologies could reduce some types of medical errors while at the same time introducing classes of medical errors (i.e., technology-induced errors). Technology-induced errors have their origins in HIT, and/or HIT contribute to their occurrence. The objective of this paper is to review current trends in the published literature on HIT safety.

METHODS

A review and synthesis of the medical and life sciences literature focusing on the area of technology-induced error was conducted.

RESULTS

There were four main trends in the literature on technology-induced error. The following areas were addressed in the literature: definitions of technology-induced errors; models, frameworks and evidence for understanding how technology-induced errors occur; a discussion of monitoring; and methods for preventing and learning about technology-induced errors.

CONCLUSIONS

The literature focusing on technology-induced errors continues to grow. Research has focused on the defining what an error is, models and frameworks used to understand these new types of errors, monitoring of such errors and methods that can be used to prevent these errors. More research will be needed to better understand and mitigate these types of errors.