Medication Safety in Two Intensive Care Units of a Community Teaching Hospital After Electronic Health Record Implementation: Sociotechnical and Human Factors Engineering Considerations

Evaluating health information systems-related errors using the human, organization, process, technology-fit (HOPT-fit) framework

Complex socio-technical health information systems (HIS) issues can create new error risks. Therefore, we evaluated the management of HIS-related errors using the proposed human, organization, process, and technology-fit framework to identify the lessons learned. Qualitative case study methodology through observation, interview, and document analysis was conducted at a 1000-bed Japanese specialist teaching hospital. Effective management of HIS-related errors was attributable to many socio-technical factors including continuous improvement, safety culture, strong management and leadership, effective communication, preventive and corrective mechanisms, an incident reporting system, and closed feedback loops. Enablers of medication errors include system sophistication and process factors like workarounds, variance, clinical workload, slips and mistakes, and miscommunication. The case management effectiveness in handling the HIS-related errors can guide other clinical settings. The potential of HIS to minimize errors can be achieved through continual, systematic, and structured evaluation. The case study validated the applicability of the proposed evaluation framework that can be applied flexibly according to study contexts to inform HIS stakeholders in decision-making. The comprehensive and specific measures of the proposed framework and approach can be a useful guide for evaluating complex HIS-related errors. Leaner and fitter socio-technical components of HIS can yield safer system use.

Methods for studying medication safety following electronic health record implementation in acute care: a scoping review

OBJECTIVES

The objective of this scoping review is to map methods used to study medication safety following electronic health record (EHR) implementation. Patterns and methodological gaps can provide insight for future research design.

MATERIALS AND METHODS

We used the Joanna Briggs Institute scoping review methodology and a custom data extraction table to summarize the following data: (1) study demographics (year, country, setting); (2) study design, study period, data sources, and measures; (3) analysis strategy; (4) identified limitations or recommendations; (5) quality appraisal; and (6) if a Safety-I or Safety-II perspective was employed.

RESULTS

We screened 5879 articles. One hundred and fifteen articles met our inclusion criteria and were assessed for eligibility by full-text review. Twenty-seven articles were eligible for extraction.

DISCUSSION AND CONCLUSION

We found little consistency in how medication safety following EHR implementation was studied. Three study designs, 7 study settings, and 10 data sources were used across 27 articles. None of the articles shared the same combination of design, data sources, study periods, and research settings. Outcome measures were neither defined nor measured consistently. It may be difficult for researchers to aggregate and synthesize medication safety findings following EHR implementation research. All studies but one used a Safety-I perspective to study medication safety. We offer a conceptual model to support a more consistent approach to studying medication safety following EHR implementation.

Barriers and enablers for safe medication administration in adult and neonatal intensive care units mapped to the behaviour change wheel

BACKGROUND

Intensive care settings have high rates of medication administration errors. Medications are often administered by nurses and midwives using a specified process (the '5 rights'). Understanding where medication errors occur, the contributing factors and how best practice is delivered may assist in developing interventions to improve medication safety.

AIMS

To identify medication administration errors and context specific barriers and enablers for best practice in an adult and a neonatal intensive care unit. Secondary aims were to identify intervention functions (through the Behaviour Change Wheel).

STUDY DESIGN

A dual methods exploratory descriptive study was conducted (May to June 2021) in a mixed 56-bedded adult intensive care unit and a 6-bedded neonatal intensive care unit in Sydney, Australia. Incident monitoring data were examined. Direct semi-covert observational medication administration audits using the 5 rights (n = 39) were conducted. Brief interviews with patients, parents and nurses were conducted. Data were mapped to the Behaviour Change Wheel.

RESULTS

No medication administration incidents were recorded. Audits (n = 3) for the neonatal intensive care unit revealed no areas for improvement. Adult intensive care unit nurses (n = 36) performed checks for the right medication 35 times (97%) and patient identity 25 times (69%). Sixteen administrations (44%) were interrupted. Four themes were synthesized from the interview data: Trust in the nursing profession; Availability of policies and procedures; Adherence to the '5 rights' and departmental culture; and Adequate staffing. The interventional functions most likely to bring about behaviour change were environmental restructuring, enablement, restrictions, education, persuasion and modelling.

CONCLUSIONS

This study reveals insights about the medication administration practices of nurses in intensive care. Although there were areas for improvement there was widespread awareness among nurses regarding their responsibilities to safely administer medications. Interview data indicated high levels of trust among patients and parents in the nurses.

RELEVANCE TO CLINICAL PRACTICE

This novel study indicated that nurses in intensive care are aware of their responsibilities to safely administer medications. Mapping of contextual data to the Behaviour Change Wheel resulted in the identification of Intervention functions most likely to change medication administration practices in the adult intensive care setting that is environmental restructuring, enablement, restrictions, education, persuasion and modelling.

Nurses' knowledge and implementation of Antimicrobial Stewardship and Infection Prevention strategies in acute paediatric settings

BACKGROUND

Nurses are the first point of contact for patients and are responsible for monitoring and reporting signs of infection. The COVID-19 pandemic cemented nurses' leadership role in infection prevention. Despite this, nurses' contribution to Antimicrobial Stewardship initiatives remains under-recognised. The aim in this study was to determine how paediatric nurses understood their role and contribution to antimicrobial stewardship and infection prevention and control practices in three different acute paediatric wards.

METHODS

Forty-three nurses were recruited from an adolescent ward, an oncology ward and a surgical ward in a metropolitan tertiary children's hospital for a qualitative exploratory descriptive study.

RESULTS

Thematic and content analysis derived three themes from the data: Understanding of preventable infections, Embracing evidence-based guidelines to protect the patient, and Roles in preventing and controlling infections and antimicrobial stewardship. Associated subthemes were: Desensitised to COVID-19, Understanding Infection Prevention and Control Precautions, Correct Use of Hospital Policy and Guidelines, Restrictions Associated with the Use of Electronic Medical Records, Understanding of Sepsis Management and the Importance of Timely Micro-Biological Testing, Ambivalence on Antimicrobial Stewardship roles, and High priority placed on consumer education.

CONCLUSION

Nurses' understanding of their role focused on practices such as performing hand hygiene, standard precautions and reporting the use of high-risk antimicrobials. A lack of understanding of paediatric COVID-19 transmission and presentations was also reported. Education on best practice in infection prevention and AMS was recognised as crucial for both nurses and parents.

Using human factors principles to redesign a 3D lab workflow during the COVID-19 pandemic

Background

Like most hospitals, our hospital experienced COVID-19 pandemic-related supply chain shortages. Our additive manufacturing lab’s capacity to offset these shortages was soon overwhelmed, leading to a need to improve the efficiency of our existing workflow. We undertook a work system analysis guided by the Systems Engineering Initiative for Patient Safety (SEIPS) construct which is based on human factors and quality improvement principles. Our objective was to understand the inefficiencies in project submission, review, and acceptance decisions, and make systematic improvements to optimize lab operations.

Methods

Contextual inquiry (interviews and workflow analysis) revealed suboptimal characteristics of the system, specifically, reliance on a single person to facilitate work and, at times, fractured communication with project sponsors, with root causes related to the project intake and evaluation process as identified through SEIPS tools. As interventions, the analysis led us to: 1) enhance an existing but underused project submission form, 2) design and implement an internal project scorecard to standardize evaluation of requests, and 3) distribute the responsibility of submission evaluation across lab members. We implemented these interventions in May 2021 for new projects and compare them to our baseline February 1, 2018 through – April 30, 2021 performance (1184 days).

Results

All project requests were submitted using the enhanced project submission form and all received a standardized evaluation with the project scorecard. Prior to interventions, we completed 35/79 (44%) of projects, compared to 12/20 (60%) of projects after interventions were implemented. Time to review new submissions was reduced from an average of 58 days to 4 days. A more distributed team responsibility structure permitted improved workflow with no increase in staffing, allowing the Lab Manager to devote more time to engineering rather than administrative/decision tasks.

Conclusions

By optimizing our workflows utilizing a human factors approach, we improved the work system of our additive manufacturing lab to be responsive to the urgent needs of the pandemic. The current workflow provides insights for labs aiming to meet the growing demand for point-of-care manufacturing.

Use of medication data alone to identify diagnoses and related contraindications: application of algorithms to close a common documentation gap

AIMS

Automated checks for medication-related problems have become a cornerstone of medication safety. In many clinical settings medication checks remain confined to drug-drug interactions because only medication data are available in an adequately coded form, leaving possible contraindicated drug-disease combinations unaccounted for. Therefore, we devised algorithms that identify frequently contraindicated diagnoses based on medication patterns related to these diagnoses.

METHODS

We identified drugs that can identify diseases constituting common contraindications based on their exclusive use for these conditions (such as allopurinol for gout or salbutamol for bronchial obstruction). Expert-based and machine learning algorithms were developed to identify diagnoses based on highly specific medication patterns. The applicability, sensitivity and specificity of the approach were assessed by using an anonymized real-life sample of medication and diagnosis data excerpts from 3,506 discharge records of geriatric patients.

RESULTS

Depending on the algorithm, the desired focus (i.e. sensitivity vs. specificity) and the disease, we were able to identify the diagnoses gout, epilepsy, coronary artery disease, congestive heart failure and bronchial obstruction with a specificity of 44.0%-99.8% (95% CI 41.7%-100.0%) and a sensitivity of 3.8%-83.1% (95% CI 1.0%-86.1%). Using only medication data we were able to identify 123 (51.3%) of 240 contraindications identified by experts with access to medication data and diagnoses.

CONCLUSION

This study provides a proof of principle that some key diagnosis-related contraindications can be identified based on a patient's medication data alone, while others cannot be identified. This approach offers new opportunities to analyse drug-disease contraindications in community pharmacy or clinical routine data.

Identifying Electronic Medication Administration Record (eMAR) Usability Issues from Patient Safety Event Reports

BACKGROUND

Improving our understanding of the association between medication errors and health information technology (health IT) usability has the potential to reduce errors and improve patient safety. This study used patient safety event reports (PSEs) to investigate the contribution of usability challenges associated with the electronic medication administration record (eMAR) to medication errors.

METHODS

Free-text descriptions of 849 medication-related PSEs selected from 2.3 million reports were analyzed. Coders identified the specific health IT components, usability challenge categories, and nuanced usability themes that contributed to each PSE. Thematic analysis was conducted to refine categorizations and identify emerging themes. Final analysis was limited to PSEs involving a contribution from eMAR, either as the point of origin or as a downstream contributor to error.

RESULTS

eMAR contributed to 473 PSEs. eMAR was the point of origin for 84 (17.8% of 473) PSEs. Usability challenge categories included Workflow support (n = 52, 11.0%) and Display/Visual clutter (n = 30, 6.3%). eMAR contributed downstream from the point of origin in 389 (82.2% of 473) PSEs, with errors stemming primarily from Pharmacy IT and computerized provider order entry (CPOE). Prominent secondary eMAR-associated usability challenges included Display/Visual clutter (n = 327, 69.1%) and Alerting (n = 32, 6.8%).

CONCLUSION

This study identified several eMAR usability challenges, through the analysis of PSEs, that contribute to medication errors. Findings highlight the critical need for improving the eMAR user interface. Improved interface design, better vendor usability testing, eMAR-focused certification testing, consideration of work system factors, and eMAR-focused usability and safety testing by health care facilities can improve eMAR technology and patient safety.

Automation failures and patient safety

PURPOSE OF REVIEW

The goal of automation is to decrease the anesthesiologist's workload and to decrease the possibility of human error. Automated systems introduce problems of its own, however, including loss of situation awareness, leaving the physician out of the loop, and training physicians how to monitor autonomous systems. This review will discuss the growing role of automated systems in healthcare and describe two types of automation failures.

RECENT FINDINGS

An automation surprise occurs when an automated system takes an action that is unexpected by the user. Mode confusion occurs when the operator does not understand what an automated system is programmed to do and may prevent the clinician from fully understanding what the device is doing during a critical event. Both types of automation failures can decrease a clinician's trust in the system. They may also prevent a clinician from regaining control of a failed system (e.g., a ventilator that is no longer working) during a critical event.

SUMMARY

Clinicians should receive generalized training on how to manage automation and should also be required to demonstrate competency before using medical equipment that employs automation, including electronic health records, infusion pumps, and ventilators.

Health information technology and hospital performance the role of health information quality in teaching hospitals

The research purposed in this paper is to investigate the impact of the health information technology on hospital performance through the health information quality as mediating variable, as new evidence from the teaching hospitals in the north of Jordan. Research design and methodology approach based on a survey that is conducted to collect the requested data to develop a model connect between the health information technologies, health information quality and hospital performance by using the Structural Equation Modeling approach. The research findings show that there is an intertwined and reciprocal relation between Health Information Technologies (HITs), hospital performance, and health information quality. HITs have direct positive impacts on both hospital performance and health information quality. Health information quality has also a direct impact on hospital performance. Besides, health information quality functioned as a partial mediator between HITs and hospital performance. The study did not examine the factors that influence the relationship between HITs, hospital performance and health information quality. This paper is evidence for the investor in the healthcare sector to invest more in HITs and health information quality, where the expected results are productivity improvement, performance leveraging and error reduction. The research originality is to introduce new evidence support literature form the Middle East countries is the main contribution of this paper.

Computerised clinical decision support systems and absolute improvements in care: meta-analysis of controlled clinical trials

OBJECTIVE

To report the improvements achieved with clinical decision support systems and examine the heterogeneity from pooling effects across diverse clinical settings and intervention targets.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

Medline up to August 2019.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES AND METHODS

Randomised or quasi-randomised controlled trials reporting absolute improvements in the percentage of patients receiving care recommended by clinical decision support systems. Multilevel meta-analysis accounted for within study clustering. Meta-regression was used to assess the degree to which the features of clinical decision support systems and study characteristics reduced heterogeneity in effect sizes. Where reported, clinical endpoints were also captured.

RESULTS

In 108 studies (94 randomised, 14 quasi-randomised), reporting 122 trials that provided analysable data from 1 203 053 patients and 10 790 providers, clinical decision support systems increased the proportion of patients receiving desired care by 5.8% (95% confidence interval 4.0% to 7.6%). This pooled effect exhibited substantial heterogeneity (I²=76%), with the top quartile of reported improvements ranging from 10% to 62%. In 30 trials reporting clinical endpoints, clinical decision support systems increased the proportion of patients achieving guideline based targets (eg, blood pressure or lipid control) by a median of 0.3% (interquartile range -0.7% to 1.9%). Two study characteristics (low baseline adherence and paediatric settings) were associated with significantly larger effects. Inclusion of these covariates in the multivariable meta-regression, however, did not reduce heterogeneity.

CONCLUSIONS

Most interventions with clinical decision support systems appear to achieve small to moderate improvements in targeted processes of care, a finding confirmed by the small changes in clinical endpoints found in studies that reported them. A minority of studies achieved substantial increases in the delivery of recommended care, but predictors of these more meaningful improvements remain undefined.

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.

Advocating for Greater Usability in Clinical Technologies: The Role of the Practicing Nurse

Health care, especially ICUs, rely on multiple types of technology to promote the best patient outcomes. Unfortunately, too often these technologies are poorly designed, causing errors, additional workload, and unnecessary frustration. The purpose of this article is to (1) empower nurses with the needed usability and usability testing vocabulary to identify and articulate clinical technology usability problems and (2) provide ideas on ways nurses can advocate to have an impact on positive change related to technology usability within a health care organization.

What to expect from electronic patient record system implementation: lessons learned from published evidence

Background

Numerous studies have examined factors related to success, failure and implications of electronic patient record (EPR) system implementations, but usually limited to specific aspects.

Objective

To review the published peer-reviewed literature and present findings regarding factors important in relation to successful EPR implementations and likely impact on subsequent clinical activity.

Method

Literature review.

Results

Three hundred and twelve potential articles were identified on initial search, of which 117 were relevant and included in the review. Several factors were related to implementation success, such as good leadership and management, infrastructure support, staff training and focus on workflows and usability. In general, EPR implementation is associated with improvements in documentation and screening performance and reduced prescribing errors, whereas there are minimal available data in other areas such as effects on clinical patient outcomes. The peer-reviewed literature appears to under-represent a range of technical factors important for EPR implementations, such as data migration from existing systems and impact of organisational readiness.

Conclusion

The findings presented here represent the synthesis of data from peer-reviewed literature in the field and should be of value to provide the evidence-base for organisations considering how best to implement an EPR system.