ENABLING DRUG-DRUG INTERACTIONS IN AN ELECTRONIC MEDICATION - PowerPoint PPT Presentation

ENABLING DRUG-DRUG INTERACTIONS IN AN ELECTRONIC MEDICATION MANAGEMENT SYSTEM: IMPACT ON PRESCRIBER ALERT BURDEN Anmol Sandhu – BPharm (Hons), MRes (Health Informatics) Health Data Analytics Conference, October 2019

Electronic medication management (EMM) systems  CPOE or ePS  Electronically support the medication management process Pharmacist’s Nurse’s Prescriber’s review of documentation medication medication administration order order & supply of the medicine of medicine And all the processes in between… HEALTH DATA ANALYTICS - OCT 2019

Clinical Decision Support (CDS)  Clinical decision support is an intelligent feature commonly integrated within EMM systems  Reduction in medication errors 1,2  Provides relevant clinical content and patient data to:  facilitate clinical decision making  notify of a potential adverse outcome  Alerts that trigger at the point of prescribing are a common form of CDS  Alerts can target different clinicians – nurses, pharmacists, doctors HEALTH DATA ANALYTICS - OCT 2019

Types of CDS alerts Drug-drug Therapeutic Dose Range interaction Duplication breach Local Drug-allergy Restriction interaction rules Prescribers can ‘accept’ or ‘override’ (bypass) alerts HEALTH DATA ANALYTICS - OCT 2019

Prescriber alert burden WHAT IS IT? HIGH ALERT BURDEN Ignore all alerts, including critical ones Alert volume User frustration experienced by Alert fatigue prescriber High override rates Threshold for alert fatigue is unknown HOW CAN IT BE MEASURED?  Rate of alerts encountered per prescriber  Proportion of a prescriber’s medication orders that generate alert - Prescriber-centric alert burden data lacking HEALTH DATA ANALYTICS - OCT 2019

St Vincent’s Hospital, Sydney (SVHS) STUDY SITE  379 bed tertiary referral hospital  Complex specialties including Heart, Lung, Bone Marrow Transplantation, AIDS/HIV, Cardiology, Cancer care  EMM system implemented in 2005 (MedChart – DXC Technology)  Used in all inpatient wards, including ICU  My role at SVHS: Specialist Electronic Medicines Management Pharmacist HEALTH DATA ANALYTICS - OCT 2019

CDS at St Vincent’s Hospital Drug-Allergy  Judicious approach to alert Drug-drug Therapeutic interaction (DDI) implementation since go-live Duplication alerts  DDI alerts NOT enabled Passive Dose Range (order sentence, (limited) sets, on-demand look up) Local restriction rules (limited) HEALTH DATA ANALYTICS - OCT 2019

Lung Transplant patient 39 medication orders HEALTH DATA ANALYTICS - OCT 2019

CDS alerts PRESCRIBER VIEW 13 alerts for DDIs 17 instances where Dr must complete an action – override +/- make a comment or cancel order (remove) HEALTH DATA ANALYTICS - OCT 2019

Challenges with DDI alerts 1. Standards  Currently no standards nationally or internationally 2. DDI knowledgebase  Large variability: same DDI may be contraindicated in one, but be listed at a different severity level, or not at all in another 3. Low alert specificity = high alert burden & potential for alert fatigue  Alerts are not ‘context - aware’ - Patient (lab results, co-morbidities), location, doctor, drug 4. Clinical outcomes of DDI alerts  Evaluations in clinical practice are lacking – do DDI alerts actually prevent adverse events associated with DDIs? 3,4 HEALTH DATA ANALYTICS - OCT 2019

Drug-Drug Interaction Alerts: Help or Hindrance? HEALTH DATA ANALYTICS - OCT 2019

DDI alerts – on or off? INFORMED DECISION  Improved functionality – enable alerts at a chosen severity level ― Limited initially – all ON or all OFF ― Moderate = 7702 pairs ― Severe = 3498 pairs ― Unknown  Renewed discussion ― Understand impact to prescribers ― Evidence-based data to inform decision HEALTH DATA ANALYTICS - OCT 2019

AIM To determine alert burden experienced by prescribers with existing CDS functionality, and then how this would change if DDI alerts were added to the EMM system

Alert Conditions  Prescribed medication orders for all admitted patients at SVHS on a single given day were extracted and replicated in a ‘test’ EMM system  The ‘Test’ system had DDI alerts enabled – unknown, moderate, severe Allergy & Dose Range Local rules Therapeutic DDI Intolerance Duplication Alert Condition 1      (Live system) Reference condition      Alert Condition 2 (All - unknown/ (Test system) moderate/severe) HEALTH DATA ANALYTICS - OCT 2019

DDI alerts – unknown HEALTH DATA ANALYTICS - OCT 2019

Data entry, extraction & analysis ALERT CONDITION 1 Extract data using SQL queries Live EMM AC1 Extracted Analyse system Results AC1 data Input manually AC1 patient profiles ALERT CONDITION 2 Extract data using SQL queries Test EMM AC1 background Analyse AC2 Extracted medications system Results AC 2 data AC1 study medications EMM = electronic medication management; SQL = standard query language; AC1 = Alert Condition 1; AC2 = Alert Condition 2 HEALTH DATA ANALYTICS - OCT 2019

RESULTS KEY FINDINGS

Finding 1 - Overall alert volume Patients Medication orders Background: 2728 254 admitted inpatients - 133 of these had study date medication orders Study date: 576 Alert Condition 2 Alert Condition 1 Increase (All DDI alerts) (No DDI alerts) Alerts generated 209 1063 +509%* Medication orders with at 145 (25%) 348 (60%) +240%* least 1 alert (%) Alerts per medication 1.4 ( 0 - 4) 3.1 (0 - 11) +212%* order (range) *Statistically significant increase with DDI alerts (p<0.005) HEALTH DATA ANALYTICS - OCT 2019

Finding 2 - Prescriber alert burden 576 medication orders prescribed by 78 unique doctors on the study date. Mean of 7.4 medication orders (range: 1 – 28) prescribed per doctor. Alert Condition 1 Alert Condition 2 % Increase (No DDI alerts) (All DDI alerts) Alerted doctors (%) 55 (71%) 71 (91%) +121% 3.8 15 +395% Alerts per doctor (range) (1-13) (1-85) Proportion of prescribed 38% 72% +188% medicines that generated alerts Statistically significant increase with DDI alerts (p<0.005) HEALTH DATA ANALYTICS - OCT 2019

Finding 2 - Prescriber alert burden Moderate DDIs % Increase Severe DDIs % Increase Alerted doctors (%) +121% 107% 67 (86%) 59 (76%) Alerts per doctor 7.8 +205%* 124% 4.7 (1-18) (range) (1- 38) +145%* 113% Alerted medication 57% 32% orders (%) *Statistically significant increase with DDI alerts (p<0.005) HEALTH DATA ANALYTICS - OCT 2019

Finding 3 - DDI alert profile Moderate 29% Severe 8% Unknown 63% 21

WHAT DOES IT ALL MEAN?

Finding 1 – Overall alert volume  With the addition of DDI alerts, almost two-thirds (60.4%) of medication orders generated an alert Inpatient studies: 6.6% - 37.1% 5-9 - Why so high?  Inclusion of the ‘unknown’ DDI alerts – not common practice, low risk medicines Exclusion of unknown DDI alerts  Moderate/Severe: 40%; Severe: 28% -  Poor alert specificity – inclusion of contextual factors could reduce alert burden 55% reduction in statin-drug interaction alerts if dose of statin considered the alert algorithm 10 -  Commercial database – overly inclusive, prone to excessive alert generation Change presentation of alerts in accordance with their severity (‘Tiering’) 11 -  Relative complexity and acuity of patients at SVHS – transplant patients HEALTH DATA ANALYTICS - OCT 2019

Finding 2 – Prescriber alert burden  For an individual prescriber, the addition of DDI alerts had a substantial impact on the number of alerts encountered  alert fatigue 72% of 28 medication 60 alerts medication 20 medication 3 alerts per orders orders generate orders order (vs 15 alerts) prescribed an alert  Unable to compare with other studies – override rates as a surrogate marker of alert fatigue 12,13 - Future work: prescriber-related outcome measures of alert burden  Important to consider cumulative alert burden  Alert volume will increase with inclusion of other CDS types in the future  Non-medication related alerts arising from Electronic Medical Records HEALTH DATA ANALYTICS - OCT 2019

Strengths, limitations & challenges Strengths:  Prescriber-focused outcome measures – burden to individual users  Assessed cumulative impact of enabling a specific type of CDS alert - Plan implementation of future CDS alert types  Insight into incidence of DDI alerts in an Australian hospital Limitations:  One day, one EMM system, one knowledgebase, one hospital (inpatient)  Did not examine alert design and utility or the changes in prescribing decisions, or clinical outcomes (e.g. reduced ADEs) Challenges:  Highly manual and laborious in nature – hampered scalability  Challenges with CDS alert data analysis +++ HEALTH DATA ANALYTICS - OCT 2019

Looking ahead – using data to make informed decisions  DDI alerts need to be refined and reviewed prior to their implementation - Remove unknown DDI alerts, assess clinical significance, incorporate contextual factors - Severe DDI alerts  Centralised body to develop and curate CDS content and drug knowledgebases for an Australian context would ensure standardisation across healthcare organisations HEALTH DATA ANALYTICS - OCT 2019