The Value of Monitoring Clinical Decision Support Interventions.

BACKGROUND: Well-functioning clinical decision support (CDS) can facilitate provider workflow, improve patient care, promote better outcomes, and reduce costs. However, poorly functioning CDS may lead to alert fatigue, cause providers to ignore important CDS interventions, and increase provider dissatisfaction. OBJECTIVE: The purpose of this article is to describe one institution's experience in implementing a program to create and maintain properly functioning CDS by systematically monitoring CDS firing rates and patterns. METHODS: Four types of CDS monitoring activities were implemented as part of the CDS lifecycle. One type of monitoring occurs prior to releasing active CDS, while the other types occur at different points after CDS activation. RESULTS: Two hundred and forty-eight CDS interventions were monitored over a 2-year period. The rate of detecting a malfunction or significant opportunity for improvement was 37% during preactivation and 18% during immediate postactivation monitoring. Monitoring also informed the process of responding to user feedback about alerts. Finally, an automated alert detection tool identified 128 instances of alert pattern change over the same period. A subset of cases was evaluated by knowledge engineers to identify true and false positives, the results of which were used to optimize the tool's pattern detection algorithms. CONCLUSION: CDS monitoring can identify malfunctions and/or significant improvement opportunities even after careful design and robust testing. CDS monitoring provides information when responding to user feedback. Ongoing, continuous, and automated monitoring can detect malfunctions in real time, before users report problems. Therefore, CDS monitoring should be part of any systematic program of implementing and maintaining CDS.

Economic Modeling of Heart Failure Telehealth Programs: When Do They Become Cost Saving?

Telehealth programs for congestive heart failure have been shown to be clinically effective. This study assesses clinical and economic consequences of providing telehealth programs for CHF patients. A Markov model was developed and presented in the context of a home-based telehealth program on CHF. Incremental life expectancy, hospital admissions, and total healthcare costs were examined at periods ranging up to five years. One-way and two-way sensitivity analyses were also conducted on clinical performance parameters. The base case analysis yielded cost savings ranging from $2832 to $5499 and 0.03 to 0.04 life year gain per patient over a 1-year period. Applying telehealth solution to a low-risk cohort with no prior admission history would result in $2502 cost increase per person over the 1-year time frame with 0.01 life year gain. Sensitivity analyses demonstrated that the cost savings were most sensitive to patient risk, baseline cost of hospital admission, and the length-of-stay reduction ratio affected by the telehealth programs. In sum, telehealth programs can be cost saving for intermediate and high risk patients over a 1- to 5-year window. The results suggested the economic viability of telehealth programs for managing CHF patients and illustrated the importance of risk stratification in such programs.

Analysis of Nursing Clinical Decision Support Requests and Strategic Plan in a Large Academic Health System.

OBJECTIVES: To understand requests for nursing Clinical Decision Support (CDS) interventions at a large integrated health system undergoing vendor-based EHR implementation. In addition, to establish a process to guide both short-term implementation and long-term strategic goals to meet nursing CDS needs. MATERIALS AND METHODS: We conducted an environmental scan to understand current state of nursing CDS over three months. The environmental scan consisted of a literature review and an analysis of CDS requests received from across our health system. We identified existing high priority CDS and paper-based tools used in nursing practice at our health system that guide decision-making. RESULTS: A total of 46 nursing CDS requests were received. Fifty-six percent (n=26) were specific to a clinical specialty; 22 percent (n=10) were focused on facilitating clinical consults in the inpatient setting. "Risk Assessments/Risk Reduction/Promotion of Healthy Habits" (n=23) was the most requested High Priority Category received for nursing CDS. A continuum of types of nursing CDS needs emerged using the Data-Information-Knowledge-Wisdom Conceptual Framework: 1) facilitating data capture, 2) meeting information needs, 3) guiding knowledge-based decision making, and 4) exposing analytics for wisdom-based clinical interpretation by the nurse. CONCLUSION: Identifying and prioritizing paper-based tools that can be modified into electronic CDS is a challenge. CDS strategy is an evolving process that relies on close collaboration and engagement with clinical sites for short-term implementation and should be incorporated into a long-term strategic plan that can be optimized and achieved overtime. The Data-Information-Knowledge-Wisdom Conceptual Framework in conjunction with the High Priority Categories established may be a useful tool to guide a strategic approach for meeting short-term nursing CDS needs and aligning with the organizational strategic plan.

Stroke navigator--a clinical decision support system for acute stroke.

The Stroke Navigator is a clinical decision support system aimed at improving the diagnosis and treatment of acute stroke. It combines an audit trail, a differential diagnosis window, an interactive stroke protocol map, and a list of recommendations for hospital staff. It provides a patient-specific overview of the workflow status and of the available clinical findings, with the goal of improving the continuity of care. For this purpose, it uses a workflow engine that was specifically designed to meet the demands of clinical practice. The Stroke Navigator furthermore calculates and displays the probabilities of various stroke differential diagnoses. The demonstration will introduce these and other features by means of a hypothetical patient case. It will also summarize the status of alpha-testing the first prototype.

Weaknesses in the classification criteria for antithrombotic-related major bleeding events.

We asked two physicians to review the medical records (electronic and paper) of 100 patients on antithrombotics. The physicians used published classification criteria to identify all of the bleeding events that the patients experienced. The goal of the review was to investigate whether the physicians would identify the same antithrombotic related major bleeding events (ARMBEs) for each patient. The correct identification and classification of multiple bleeding events is a prerequisite for studies of antithrombotic treatment practices during hospitalization that predispose patients to ARMBEs. In addition, we were interested in the reasons for disagreement between the physicians, so that we could find ways of improving their agreement. The reviewers identified 299 bleeding events for the 100 patients. They disagreed on whether 29 of the events represented an ARMBE occurring during hospitalization. With a kappa statistic of 0.49 (95% confidence interval, 0.31 to 0.66) the agreement was moderate. The reviewers most often disagreed either because they misinterpreted the data (12 events) or because the classification criteria for ARMBEs were not explicit enough (9 events). Disagreement took two main forms: either the reviewers disagreed on ARMBEs by not identifying the same bleeds (11 events) or by not applying the severity criteria appropriately (7 events). Because the main type of disagreement was not identifying the same bleeds, a study investigating the antithrombotic treatment practices that predispose patients to ARMBEs would be threatened. We therefore proposed supplementing the existing classification criteria with additional rules to avoid ambiguities in patients with multiple events.

Accuracy of administrative data for identifying patients with pneumonia.

The goal of this study was to determine the accuracy and the impact of 5 different claims-based pneumonia definitions. Three International Classification of Diseases, Version 9, (ICD-9), and 2 diagnosis-related group (DRG)-based case identification algorithms were compared against an independent, clinical pneumonia reference standard. Among 10748 patients, 272 (2.5%) had pneumonia verified by the reference standard. The sensitivity of claims-based algorithms ranged from 47.8% to 66.2%. The positive predictive values ranged from 72.6% to 80.8%. Patient-related variables were not significantly different from the reference standard among the 3 ICD-9-based algorithms. DRG-based algorithms had significantly lower hospital admission rates (57% and 65% vs 73.2%), lower 30-day mortality (5.0% and 5.8% vs 10.7%), shorter length of stay (3.9 and 4.1 days vs 5.6 days), and lower costs (USD $4543 and USD $5159 vs USD $8585). Claims-based identification algorithms for defining pneumonia in administrative databases are imprecise. ICD-9-based algorithms did not influence patient variables in our population. Identifying pneumonia patients with DRG codes is significantly less precise.