Use of a Proven Framework for Computer Decision Support within the Intermountain Healthcare Network.

Hospitalized patients in the U.S. do not always receive optimal care. In light of this, Computerized Decision Support (CDS) has been recommended to for the improvement of patient care. A number of methodologies, standards, and frameworks have been developed to facilitate the development and interoperability of computerized clinical guidelines and CDS logic. In addition, Health Information Exchange using Service-Oriented Architecture holds some promise to help realize that goal. We have used a framework at Intermountain Healthcare that employs familiar programming languages and technology to develop over 40 CDS applications during the past 13 years, which clinicians are dependent on each day. This paper describes the framework, technology, and CDS application development methods, while providing three distinct examples of applications that illustrate the need and use of the framework for patient care improvement. The main limitation of this framework is its dependence on point-to-point interfaces to access patient data. We look forward to the use of validated and accessible Service-Oriented Architecture to facilitate patient data access across diverse databases.

Automated detection of physiologic deterioration in hospitalized patients.

OBJECTIVE: Develop and evaluate an automated case detection and response triggering system to monitor patients every 5 min and identify early signs of physiologic deterioration. MATERIALS AND METHODS: A 2-year prospective, observational study at a large level 1 trauma center. All patients admitted to a 33-bed medical and oncology floor (A) and a 33-bed non-intensive care unit (ICU) surgical trauma floor (B) were monitored. During the intervention year, pager alerts of early physiologic deterioration were automatically sent to charge nurses along with access to a graphical point-of-care web page to facilitate patient evaluation. RESULTS: Nurses reported the positive predictive value of alerts was 91-100% depending on erroneous data presence. Unit A patients were significantly older and had significantly more comorbidities than unit B patients. During the intervention year, unit A patients had a significant increase in length of stay, more transfers to ICU (p = 0.23), and significantly more medical emergency team (MET) calls (p = 0.0008), and significantly fewer died (p = 0.044) compared to the pre-intervention year. No significant differences were found on unit B. CONCLUSIONS: We monitored patients every 5 min and provided automated pages of early physiologic deterioration. This before-after study found a significant increase in MET calls and a significant decrease in mortality only in the unit with older patients with multiple comorbidities, and thus further study is warranted to detect potential confounding. Moreover, nurses reported the graphical alerts provided information needed to quickly evaluate patients, and they felt more confident about their assessment and more comfortable requesting help.

Enhanced notification of infusion pump programming errors.

Hospitalized patients receive countless doses of medications through manually programmed infusion pumps. Many medication errors are the result of programming incorrect pump settings. When used appropriately, smart pumps have the potential to detect some programming errors. However, based on the current use of smart pumps, there are conflicting reports on their ability to prevent patient harm without additional capabilities and interfaces to electronic medical records (EMR). We developed a smart system that is connected to the EMR including medication charting that can detect and alert on potential pump programming errors. Acceptable programming limits of dose rate increases in addition to initial drug doses for 23 high-risk medications are monitored. During 22.5 months in a 24 bed ICU, 970 alerts (4% of 25,040 doses, 1.4 alerts per day) were generated for pump settings programmed outside acceptable limits of which 137 (14%) were found to have prevented potential harm. Monitoring pump programming at the system level rather than the pump provides access to additional patient data in the EMR including previous dosage levels, other concurrent medications and caloric intake, age, gender, vitals and laboratory results.

Rapid identification of hospitalized patients at high risk for MRSA carriage.

Patients who are asymptomatic carriers of methicillin-resistant Staphylococcus aureus (MRSA) are major reservoirs for transmission of MRSA to other patients. Medical personnel are usually not aware when these high-risk patients are hospitalized. We developed and tested an enterprise-wide electronic surveillance system to identify patients at high risk for MRSA carriage at hospital admission and during hospitalization. During a two-month study, nasal swabs from 153 high-risk patients were tested for MRSA carriage using polymerase chain reaction (PCR) of which 31 (20.3%) were positive compared to 12 of 293 (4.1%, p < 0.001) low-risk patients. The mean interval from admission to availability of PCR test results was 19.2 hours. Computer alerts for patients at high-risk of MRSA carriage were found to be reliable, timely and offer the potential to replace testing all patients. Previous MRSA colonization was the best predictor but other risk factors were needed to increase the sensitivity of the algorithm.

Enhanced notification of critical ventilator events.

Mechanical ventilators are designed to generate alarms when patients become disconnected or experience other critical ventilator events. However, these alarms can blend in with other accustomed sounds of the intensive care unit. Ventilator alarms that go unnoticed for extended periods of time often result in permanent patient harm or death. We developed a system to monitor critical ventilator events through our existing hospital network. Whenever an event is identified, the new system takes control of every computer in the patient's intensive care unit and generates an enhanced audio and visual alert indicating that there is a critical ventilator event and identifies the room number. Once the alert is acknowledged or the event is corrected, all the computers are restored back to the pre-alert status and/or application. This paper describes the development and implementation of this system and reports the initial results, user acceptance, and the increase in valuable information and patient safety.

Unit-wide notification of ventilator disconnections.

The alarms generated by mechanical ventilators when patients become disconnected can blend in with other typical sounds of the intensive care unit. Ventilator alarms that go unnoticed for extended periods of time often result in permanent patient harm or death. We developed a unit-wide system to monitor ventilator disconnection alarms. When a disconnection is identified, the system takes control of every computer in the patient's intensive care unit and generates an enhanced audio and visual alert. This system was tested in four ICUs at LDS Hospital. Acceptance by medical personnel was very high and patient safety was improved through early intervention that avoided prolonged hypoxia. In addition, the system facilitated root cause analyses and new safety strategies.