Improving Situation Awareness to Advance Patient Outcomes: A Systematic Literature Review.

Improving nurses' situation awareness skills would likely improve patient status recognition and prevent adverse events. Technologies such as electronic health record dashboards can be a promising approach to support nurses' situation awareness. However, the effect of these dashboards on this skill is unknown. This systematic literature review explores the evidence around interventions to improve nurses' situation awareness at the point of care. Current research on this subject is limited. Studies that examined the use of electronic health record dashboards as an intervention had weak evidence to support their effectiveness. Other interventions, including communication interventions and structured nursing assessments, may also improve situation awareness, but more research is needed to confirm this. It is important to carefully consider the design and content of situation awareness interventions, as well as the specific outcomes being measured, when designing situation awareness interventions. Overall, there is a need for higher-quality research in this area to determine the most effective interventions for improving nurse situation awareness. Future studies should focus on developing dashboards that follow a theoretical situation awareness model information and represent all situation awareness levels.

Axe the Fax: What Users Think of Electronic Referral.

Long wait times for elective services are seen as one of the major challenges for Canadian healthcare. Canadians report that they wait longer for specialists than citizens in other countries. The main reason for this is that the referral process is poorly coordinated and leads to delays in care. Electronic referral (eReferral) is seen as a potential means of improving the referral process and enabling faster access to care. There is the potential for national implementation of eReferral in Canada to help achieve this aim. However, existing initiatives have encountered challenges with user adoption and users have continued to use fax. A validated tool was used to survey both users of fax as well as users of eReferral. These two groups of users were then compared. Most family physicians using fax were satisfied overall with the process. This highlighted how challenging any change of this engrained technology will be. There were, however, some significant areas were eReferral was superior to fax. This included response time, the overall quality of referral information, completeness of the information, the timeliness of the information, and the format and layout. There is an opportunity to leverage these findings to support the adoption of eReferral and help reduce wait times.

System Dynamics in Remote Monitoring Service for Cardiovascular Implantable Electronic Devices.

This methodological paper describes how system dynamics was applied in evaluating the effect of remote monitoring (RM) of cardiovascular implantable electronic device (CIED) workload on clinical resource utilization. The development of a causal loop diagram and a stock and flow diagram and the construction of the simulation model for comparison of an in-person clinic group and RM clinic group are described.

Shared Decision Making via Personal Health Record Technology for Routine Use of Diabetic Youth: A Study Protocol.

Engaging patients in the self-management decision-making provides opportunities for positive health outcomes. The process of shared decision making (SDM) is touted as the pinnacle of patient-centred care, yet it has been difficult to implement in practice. Access to tools resulting from the integration of all health data and clinical evidence, and an ease of communications with care providers are needed to engage patients in decision making. Personal health record (PHR) technology is a promising approach for overcoming such barriers. Yet there is a scarcity of studies on system design for SDM via PHR. This paper describes a study protocol to identify functional requirements of PHR for facilitating SDM and factors that would influence the embedding of the proposed system in clinical practice.

Evidence for Busines Intelligence in Health Care: A Literature Review.

This paper outlines a systematic literature review undertaken to establish current evidence regarding the impact of Business Intelligence (BI) on health system decision making and organizational performance. The review also examined BI implementation factors contributing to these constructs. Following the systematic review, inductive content analysis was used to categorize themes within the eight articles identified. This study demonstrated there is little evidence based literature focused on BI impact on organizational decision making and performance within health care. There was evidence found that BI does improve decision making. Implementation success was found to be dependent on several factors, many of which relate to broader organizational culture and readiness.

Shared decision-making using personal health record technology: a scoping review at the crossroads.

OBJECTIVE: This scoping review aims to determine the size and scope of the published literature on shared decision-making (SDM) using personal health record (PHR) technology and to map the literature in terms of system design and outcomes. MATERIALS AND METHODS: Literature from Medline, Google Scholar, Cumulative Index to Nursing and Allied Health Literature, Engineering Village, and Web of Science (2005-2015) using the search terms "personal health records," "shared decision making," "patient-provider communication," "decision aid," and "decision support" was included. Articles ( n = 38) addressed the efficacy or effectiveness of PHRs for SDM in engaging patients in self-care and decision-making or ways patients can be supported in SDM via PHR. RESULTS: Analysis resulted in an integrated SDM-PHR conceptual framework. An increased interest in SDM via PHR is apparent, with 55% of articles published within last 3 years. Sixty percent of the literature originates from the United States. Twenty-six articles address a particular clinical condition, with 10 focused on diabetes, and one-third offer empirical evidence of patient outcomes. The tethered and standalone PHR architectural types were most studied, while the interconnected PHR type was the focus of more recently published methodological approaches and discussion articles. DISCUSSION: The study reveals a scarcity of rigorous research on SDM via PHR. Research has focused on one or a few of the SDM elements and not on the intended complete process. CONCLUSION: Just as PHR technology designed on an interconnected architecture has the potential to facilitate SDM, integrating the SDM process into PHR technology has the potential to drive PHR value.

Designing Personal Health Record Technology for Shared Decision Making.

Engaging patients in the self-management decision-making provides opportunities for positive health outcomes. The process of shared decision-making (SDM) is touted as the pinnacle of patient-centred care, yet it has been difficult to implement in practice. Access to tools resulting from the integration of all health data and clinical evidence, and an ease of communications with care providers are needed to engage patients in self management decision-making. Personal health record (PHR) technology is a promising approach for overcoming such barriers. Yet there is a scarcity of studies on system design for SDM via PHR. This paper describes the design and implications of a system for SDM via PHR.

A Survey of Standard Information Models for Clinical Decision Support Systems.

HL7 CDA, vMR, and openEHR archetypes have been utilized as standard information models for clinical decision support systems. Compared to openEHR archetypes, vMR typically requires less time to develop and extend which makes it a good fit for rapid prototyping and pilot projects, while openEHR archetypes handle the data and semantic specification better. Using CDA for clinical decision support systems is discouraged due to its complexity, steep learning curve, and potential safety issues.

A Pilot Ontology for Healthcare Quality Indicators.

Computerisation of quality indicators for the English National Health Service currently relies primarily on queries and clinical coding, with little use of ontologies. We created a searchable ontology for a diverse set of healthcare quality indicators. We investigated attributes and relationships in a set of 222 quality indicators, categorised by clinical pathway, inclusion and exclusion criteria and US Institute of Medicine purpose. Our pilot ontology could reduce duplication of effort in healthcare quality monitoring.

A system dynamics approach to analyze laboratory test errors.

Although many researches have been carried out to analyze laboratory test errors during the last decade, it still lacks a systemic view of study, especially to trace errors during test process and evaluate potential interventions. This study implements system dynamics modeling into laboratory errors to trace the laboratory error flows and to simulate the system behaviors while changing internal variable values. The change of the variables may reflect a change in demand or a proposed intervention. A review of literature on laboratory test errors was given and provided as the main data source for the system dynamics model. Three "what if" scenarios were selected for testing the model. System behaviors were observed and compared under different scenarios over a period of time. The results suggest system dynamics modeling has potential effectiveness of helping to understand laboratory errors, observe model behaviours, and provide a risk-free simulation experiments for possible strategies.

An ontology for healthcare quality indicators: challenges for semantic interoperability.

Semantic interoperability, a popular research area for electronic health records, can also be a challenge for quality indicators. We analysed attributes and relationships in a diverse set of over 200 healthcare quality indicators and created a searchable ontology. The ontology is intended to help reduce duplication of effort in healthcare quality monitoring. We describe issues with coding the indicators and specifying inclusion and exclusion criteria and propose some solutions.

The importance of telehealth for directors and other decision makers.

Innovation in healthcare services and clinical service redesign is a must because currently gaps exist between the care that people should receive for disease management and the actual care they receive. Directors and other decision makers should be aware of key challenges in the design, implementation and deployment of telehealth. In this paper, we discuss how telehealth enable chronic disease management, population health management and patient engagement, and what are the biggest challenges with getting value out of telehealth.

Modelling clinical diagnostic errors: a system dynamics approach.

The diagnostic process involves a series of stages in the patient pathway. Any errors or misleading information from any stage could lead to errors in the final decision-making. System dynamics modeling maps the diagnostic process as a whole and seeks to provide a quantitative way of analyzing different errors at each stage as well as relevant key factors. This paper provides a framework based on system dynamics for modeling the tracing of errors inside of the system from where errors initially occur, the routes of errors inside of the system, and how errors are delivered out of the system. Also, a detailed illustration of the phase history and physical examinations is provided as an example to explain how relevant factors can be interpreted and how this affects errors according to the framework.

Nursing Handover using an Electronic Application for Community Nurses.

Handover from one healthcare professional is an essential component of patient care. This can be a challenge in the community setting where care occurs in the patient's home and clinicians do not have the benefit of face-to-face interactions with colleagues. The purpose of this study was to explore the nurses' perceptions of handover and their views on using an electronic application as opposed to their current email system. Nurses completed two surveys, one prior to viewing the application and the second after reviewing the application. Both surveys were analyzed by educational preparation and age to explore whether either factor influenced opinions of handover or the electronic application. Nurses reported that handover was important and standardization of reporting improved their knowledge of their patients' care plan.

Conceptual analysis of a diverse set of healthcare quality indicators.

Computerisation of quality indicators for the English National Health Service currently relies primarily on queries and clinical coding, with little use of ontologies. We investigated attributes and relationships in a diverse set of over 200 healthcare quality indicators, categorising by clinical pathway, inclusion and exclusion criteria and Institute of Medicine purpose. Our results, some of which are described in this paper, were used to create an ontology that could reduce duplication of effort in healthcare quality monitoring.

A causal loop approach to the study of diagnostic errors.

Missed, wrong or delayed diagnosis has a direct effect on patient safety. Diagnostic errors have been discussed at length, however it still lacks a systematic approach. This study proposed a more systematic way of studying diagnostic errors by using a causal loop diagram. A systematic review was used to find the key factors which may cause diagnostic errors and their interrelationships. A causal loop diagram, as a qualitative model at the first stage of system dynamics modeling, was produced to map all the factor and interrelationships. The diagram provides not only the direct and indirect factors affecting correct diagnosis, but also a clear view of how the change of one factor in the model triggers changes of other factors and then the change of the number of final diagnostic errors.

From Computer-interpretable Guidelines to Computer-interpretable Quality Indicators: A Case for an Ontology.

In the United Kingdom's National Health Service, quality indicators are generally measured electronically by using queries and data extraction, resulting in overlap and duplication of query components. Electronic measurement of health care quality indicators could be improved through an ontology intended to reduce duplication of effort during healthcare quality monitoring. While much research has been published on ontologies for computer-interpretable guidelines, quality indicators have lagged behind. We aimed to determine progress on the use of ontologies to facilitate computer-interpretable healthcare quality indicators. We assessed potential for improvements to computer-interpretable healthcare quality indicators in England. We concluded that an ontology for a large, diverse set of healthcare quality indicators could benefit the NHS and reduce workload, with potential lessons for other countries.

Automation bias: empirical results assessing influencing factors.

OBJECTIVE: To investigate the rate of automation bias - the propensity of people to over rely on automated advice and the factors associated with it. Tested factors were attitudinal - trust and confidence, non-attitudinal - decision support experience and clinical experience, and environmental - task difficulty. The paradigm of simulated decision support advice within a prescribing context was used. DESIGN: The study employed within participant before-after design, whereby 26 UK NHS General Practitioners were shown 20 hypothetical prescribing scenarios with prevalidated correct and incorrect answers - advice was incorrect in 6 scenarios. They were asked to prescribe for each case, followed by being shown simulated advice. Participants were then asked whether they wished to change their prescription, and the post-advice prescription was recorded. MEASUREMENTS: Rate of overall decision switching was captured. Automation bias was measured by negative consultations - correct to incorrect prescription switching. RESULTS: Participants changed prescriptions in 22.5% of scenarios. The pre-advice accuracy rate of the clinicians was 50.38%, which improved to 58.27% post-advice. The CDSS improved the decision accuracy in 13.1% of prescribing cases. The rate of automation bias, as measured by decision switches from correct pre-advice, to incorrect post-advice was 5.2% of all cases - a net improvement of 8%. More immediate factors such as trust in the specific CDSS, decision confidence, and task difficulty influenced rate of decision switching. Lower clinical experience was associated with more decision switching. Age, DSS experience and trust in CDSS generally were not significantly associated with decision switching. CONCLUSIONS: This study adds to the literature surrounding automation bias in terms of its potential frequency and influencing factors.

Development of a public health reporting data warehouse: lessons learned.

Data warehouse projects are perceived to be risky and prone to failure due to many organizational and technical challenges. However, often iterative and lengthy processes of implementation of data warehouses at an enterprise level provide an opportunity for formative evaluation of these solutions. This paper describes lessons learned from successful development and implementation of the first phase of an enterprise data warehouse to support public health surveillance at British Columbia Centre for Disease Control. Iterative and prototyping approach to development, overcoming technical challenges of extraction and integration of data from large scale clinical and ancillary systems, a novel approach to record linkage, flexible and reusable modeling of clinical data, and securing senior management support at the right time were the main factors that contributed to the success of the data warehousing project.

Challenges in data quality assurance for electronic health records.

Data quality is an integral part of EHR systems. Quality assurance for these systems not only identifies the current defects in the data but also aims for minimizing the risk of their future occurrence. Previous studies for secondary use of data in research projects presented several dimensions for such defects and proposed few methods for identifying them. Although those methods were successful in small scale research studies, their application to large scale day-to-day flow of information in EHR systems involves many challenges. In this paper, we highlighted those challenges for each method and each dimension and proposed a framework for using existing technologies to address those challenges.