Jane M. Brokel

Development of a measure of clinical information systems expectations and experiences.

Background/Objectives: The purpose of this study is to describe the development and initial psychometric properties of a measure of expectations and experiences regarding the impact of clinical information systems on work process and outcomes. Research Design: Basic item analysis, confirmatory factor analysis, cross-validation factor analyses, and reliability analysis were used to assess the psychometric properties of the scale. Subjects: The initial validation sample included registered nurses from a large Midwestern rural referral hospital that implemented electronic medical records and computerized provider order entry systems. Nurses from 3 other hospitals were used to cross-validate the factor structure of the scale. Measures: The scale assesses respondents’ perceptions related to communication changes, changes in selected work behaviors, perceptions of the implementation strategy, and the impact on quality of patient care. The instrument can be used to assess perceptions before and after implementation. Results: Confirmatory factor analysis generally supported the a priori factor structure for both expectations and experiences regarding the clinical information system. The consistency of the fit to the factor models was also high across the cross-validation samples. The scales demonstrated acceptable internal consistency in all the samples. Conclusions: Our findings suggest that the measure of clinical information systems expectations and experiences offers a valid and reliable tool for assessing the perceived impact of new clinical technology on work process and outcomes. This instrument can be useful before and after technology implementation by assisting in the identification of staff perceptions and concerns, thus allowing for targeted interventions to address these issues.

Impact of health information technology on detection of potential adverse drug events at the ordering stage

PURPOSE The impact of implementing commercially available health care information technologies at hospitals in a large health system on the identification of potential adverse drug events (ADEs) at the medication ordering stage was studied. METHODS All hospitals in the health system had implemented a clinical decision-support system (CDSS) consisting of a centralized clinical data repository, interfaces for reports, a results reviewer, and a package of ADE alert rules. Additional technology including computerized provider order entry (CPOE), an advanced CDSS, and evidence-based order sets was implemented in nine hospitals. ADE alerts at these hospitals were compared with alerts at nine hospitals without the advanced technology. A linear mixed-effects model was used in determining the mean response profile of six dependent variables over 28 total months for each experimental group. RESULTS Overall, hospitals with CPOE and an advanced CDSS captured significantly more ADE alerts for pharmacist review; an average of 336 additional potential ADEs per month per hospital were reviewed. Pharmacists identified some 94% of the alerts as false positives. Alerts identified as potentially true positives were reviewed with physicians, and order changes were recommended. The number of true-positive alerts per 1000 admissions increased. CONCLUSION The implementation of CPOE and advanced CDSS tools significantly increased the number of potential ADE alerts for pharmacist review and the number of true-positive ADE alerts identified per 1000 admissions.

Expert clinical rules automate steps in delivering evidence-based care in the electronic health record

A working framework is presented for interdisciplinary professionals for designing, building, and evaluating clinical decision support rules (expert rules) within the electronic health record. The working framework outlines the key workflow processes for eight health system organizations for selecting, designing, building, activating, and evaluating rules. In preparation, an interdisciplinary team selected expert rules for their organizations. A physician, a nurse, and/or pharmacy informatics specialists led the team for each organization. The team chose from a catalog of expert rules that were supported by regulatory or clinical evidence. The design process ensured that each expert rule followed evidence-based guidelines and was programmed to automate steps in planning and delivering patient care. Expert rules were prioritized when improving the safety and quality of care. Finally, clinical decision support rules were evaluated for abilities to improve the consistency and currency of assessments and follow-through on patient findings from these assessments. The informatics specialists from each of the health system organizations also participated in a health system oversight group to construct the key processes for this beginning framework. The group refined the processes for the selection, design, construction, activation, and evaluation of expert rules over the past 3 years. These steps offered direction to subsequent clinic and hospital organizations in a similar situation. This case study identified four key considerations when implementing and evaluating the clinical decision support expert rules within care delivery. In summary, the processes for decision support expert rules required rigorous development and change control processes to support operation.

Transformation of Emergency Department processes of care with EHR, CPOE, and ER event tracking systems

Mercy Medical Center – North Iowa implemented electronic health records (EHR), computerised provider order entry (CPOE) and event tracking systems in the emergency department (ED) as part of hospital-wide implementation of clinical information systems. This case study examines the changes in outcomes and processes in the ED following implementation. Although the system was designed to enhance efficiency, there was a significant increase in the mean length of stay (about 17 minutes, or 15%) in the ED after implementation. This surprising finding was examined in relationship to the multiple process-of-care changes in the ED.

Longitudinal study of symptom control and quality of life indicators with patients receiving community-based case management services

A longitudinal study examined seven outcomes of chronically ill patients receiving community-based case management services. A repeated-measures analysis showed that these patients reported greater satisfaction with quality of life and personal well-being and controlled their symptoms better, but declined in self-care activities of daily living and in self-care instrumental activities of daily living.

Complexity of medication-related verbal orders

Verbal orders are a common practice in hospitals but there has been little systematic study about them. Although the potential for harm arising from the miscommunication and misunderstanding of verbal orders has been recognized, there is very little research examining their complexity. This article provides a descriptive analysis of one hospitals medication-related verbal-order events for a 1-week period. Among other things, this analysis demonstrates the presence of great variability across different patient care units related to when and the way in which verbal orders are communicated and the numbers and types of individual medication-related orders communicated within a single verbal-order event. The discussion identifies 3 categories of factors potentially contributing to the complexity of verbal orders and the potential for miscommunication, misunderstanding, and patient harm: Verbal Ordering Process and Content, Verbal Order Makers, and Verbal Order Takers. (Am J Med Qual 2008;23:7-17)

Evaluating clinical decision support rules as an intervention in clinician workflows with technology.

The implementation of electronic health records in rural settings generated new challenges beyond those seen in urban hospitals. The preparation, implementation, and sustaining of clinical decision support rules require extensive attention to standards, content design, support resources, expert knowledge, and more. A formative evaluation was used to present progress and evolution of clinical decision support rule implementation and use within clinician workflows for application in an electronic health record. The rural hospital was able to use clinical decision support rules from five urban hospitals within its system to promote safety, prevent errors, establish evidence-based practices, and support communication. This article describes tools to validate initial 54 clinical decision support rules used in a rural referral hospital and 17 used in clinics. Since 2005, the study hospital has added specific system clinical decision support rules for catheter-acquired urinary tract infection, deep venous thrombosis, heart failure, and more. The findings validate the use of clinical decision support rules across sites and ability to use existing indicators to measure outcomes. Rural hospitals can rapidly overcome the barriers to prepare and implement as well as sustain use of clinical decision support rules with a systemized approach and support structures. A model for design and validation of clinical decision support rules into workflow processes is presented. The replication and reuse of clinical decision support rule templates with data specifications that follow data models can support reapplication of the rule intervention in subsequent rural and critical access hospitals through system support resources.

A Code of Professional Ethical Conduct for the American Medical Informatics Association: An AMIA Board of Directors Approved White Paper

The AMIA Board of Directors has decided to periodically publish AMIAs Code of Professional Ethical Conduct for its members in the Journal of the American Medical Informatics Association. The Code also will be available on the AMIA Web site at www.amia.org as it continues to evolve in response to feedback from the AMIA membership. The AMIA Board acknowledges the continuing work and dedication of the AMIA Ethics Committee. AMIA is the copyright holder of this work.

Changing Patient Care Orders From Paper to Computerized Provider Order Entry-Based Process

The purpose of this study was to describe the extent of change in patient care orders primarily for six diagnoses, procedures, or conditions in a not-for-profit Midwestern rural referral hospital. A descriptive method was used to analyze changes in the order sets over time for chest pain with acute myocardial infarction, degenerative osteoarthritis with hip joint replacement and degenerative osteoarthritis with knee joint replacement procedures, coronary artery bypass graft procedures, congestive heart failure, and pneumonia. Ten items about service-specific order sets were abstracted during pre– and post–EHR implementation and a year later. We then examined use 5 years later. The findings illustrate how the order sets evolved with multiple nested order sets to facilitate computerized provider order entry with a rate greater than 70% by physicians. The total number of available patient care orders within the order sets increased primarily because of linked nested order sets related to medications and diagnostic tests. Five years later, 50% of the orders were medication orders. In conclusion, this was important to deploy the order sets within smaller critical-access hospital facilities to train providers in adopting order sets internally.

Methodological Approaches to Measuring the Effects of Implementation of Health Information Technology (HIT)

The research design, evaluation methodology, and statistical analysis of the clinical efficacy of healthcare information technology (HIT) implementation can be a challenging task. Much of the research to date involves weakly designed studies. We discuss some of the more rudimentary experimental designs and analytical approaches that have typically been used. Approaches to strengthen a research design include: adding a matched control unit or hospital; using multiple observations before and after the HIT implementation; making observations across a number of hospitals that are implementing the same HIT application; comparing the changes in these hospitals to a matched set that have not yet implemented the HIT application; and applying statistical approaches that permit changes in trends over time to be examined. Here we report our use of linear piecewise spline mixed effects models and compare our models to other methodological approaches that could be used for this evaluation.