Raymond L. Woosley

Background: Torsade de pointes (TdP) is a potentially fatal cardiac arrhythmia that is often drug induced. Clinical decision support (CDS) may help minimize TdP risk by guiding decision making in patients at risk. CDS has been shown to decrease prescribing of high‐risk medications in patients at risk of TdP, but alerts are often ignored. Other risk‐management options can potentially be incorporated in TdP risk CDS. Our goal was to evaluate actions clinicians take in response to a CDS advisory that uses a modified Tisdale QT risk score and presents management options that are easily selected (eg, single click). Methods and Results: We implemented an inpatient TdP risk advisory systemwide across a large health care system comprising 30 hospitals. This CDS was programmed to appear when prescribers attempted ordering medications with a known risk of TdP in a patient with a QT risk score ≥12. The CDS displayed patient‐specific information and offered relevant management options including canceling offending medications and ordering electrolyte replacement protocols or ECGs. We retrospectively studied the actions clinicians took within the advisory and separated by drug class. During an 8‐month period, 7794 TdP risk advisories were issued. Antibiotics were the most frequent trigger of the advisory (n=2578, 33.1%). At least 1 action was taken within the advisory window for 2700 (34.6%) of the advisories. The most frequent action taken was ordering an ECG (n=1584, 20.3%). Incoming medication orders were canceled in 793 (10.2%) of the advisories. The frequency of each action taken varied by drug class ( P <0.05 for all actions). Conclusions: A modified Tisdale QT risk score–based CDS that offered relevant single‐click management options yielded a high action/response rate. Actions taken by clinicians varied depending on the class of the medication that evoked the TdP risk advisory, but the most frequent was ordering an ECG.

Objectives: Clinical decision support (CDS) can potentially help clinicians identify and manage patients who are at risk for torsades de pointes (TdP). However, computer alerts are often ignored and might contribute to alert fatigue. The goals of this project were to create an advanced TdP CDS advisory that presents patient-specific, relevant information, including 1-click management options, and to determine clinician satisfaction with the CDS. Methods: The advanced TdP CDS was developed and implemented across a health system comprising 29 hospitals. The advisory presents patient-specific information including relevant risk factors, laboratory values, and 1-click options to help manage the condition in high-risk patients. A short electronic survey was created to gather clinician feedback on the advisory. Results: After implementation, an email invitation to complete the anonymous advisory-related survey was sent to 442 clinicians who received the advisory. Among the 38 respondents, feedback was generally positive, with 79% of respondents reporting that the advisory helps them care for their patients and 87% responding that alternative actions for them to consider were clearly specified. However, 46% of respondents indicated the alert appeared too frequently. Conclusions: Advanced TdP risk CDS that provides relevant, patient-specific information and 1-click management options can be generally viewed favorably by clinicians who receive the advisory.

Objectives: Both social and medical factors can negatively affect health outcomes, especially in vulnerable populations. To address these 2 types of factors in a postdischarge population, 2 nonprofit organizations collaborated to combine their novel decision support programs and address the question: Could combined programs have greater potential for improved health outcomes? Methods: HomeMeds, a social health program in which trained social services staff make home visits to vulnerable clients, was combined with MedSafety Scan, a medical health, clinical decision support tool. Data captured in the home visits were entered into the HomeMeds and MedSafety Scan programs to detect those patients at the greatest risk of adverse health outcomes because of medications. Results: Patients (n = 108; mean age, 77 years; multiple comorbidities and LACE+ (length of stay, acuity, comorbidities, emergency department visits [hospital index]; score >29) received a postdischarge home visit by trained social services staff. The number of drugs reported as being taken was 10.4 ± 5.1 (range, 1–26), which was less than prescribed at discharge in 62% of patients (range, 1–8). Both programs detected a serious risk of medication-induced harm, mostly from different causes such as drug-drug interactions or for use not recommended in the elderly. Conclusions: Combined analysis of data from 2 novel decision support programs yielded complementary findings that together address both medical and social determinants of health. These have the potential to reduce medication-induced harm, costly rehospitalization, and/or emergency department visits and support the further evaluation of this combined approach in other vulnerable populations such as the seriously mentally ill, frail, those confined to home, opioid dependent, or otherwise impaired.

Background: Both medical and social issues can negatively affect health outcomes, especially for vulnerable populations. Assessing risk from these two perspectives independently, two non-profit organizations each developed a decision support program to identify medication-risk that could negatively impact health outcomes. This raises the question, could a combined program have greater potential for improved outcomes.Methods: HomeMeds℠ (HM), a social health program in which trained social services staff make home visits to vulnerable clients, was combined with MedSafety Scan (MSS), a medical health, clinical decision support tool. Data captured in the home visit were entered into the HM and MSS programs to analyze each patient’s drug risk profile and their list of medicines to detect those patients at greatest risk of adverse health outcomes caused by medications. Results: One hundred and eight patients (mean age 77 with multiple co-morbidities and LACE+ score >29) received a post-discharge home visit by trained social services staff. The number of drugs reported as being taken was 10.4 ± 5.1 (range 1-26) which was less than prescribed at discharge in 62% of patients (range 1-8). Both programs detected serious risk of medication-induced harm, mostly from different causes. HomeMeds detected medically relevant events such as unreported falls (24), dizziness (37), confusion (27) and uncontrolled pain (37). MedSafety Scan identified 22 patients with moderate to very high risk of cardiac arrhythmia due to their medicines. In 65% of patients, an average of 6.2 ± 5.6 potentially serious drug interactions were detected by MedSafety Scan but not HomeMeds.Conclusions: Combined analyses of data from these two decision support programs that approach health issues and patient needs from different perspectives yielded complementary findings that address both medical and social determinants of health after hospital discharge. These have the potential to reduce medication-induced harm, costly re-hospitalization and/or emergency room visits. The results of this quality improvement project are encouraging and support the further evaluation of this combined approach in other vulnerable populations such as the seriously mentally ill, frail, those confined to home, opiate-dependent or otherwise impaired.