ENABLING DRUG-DRUG INTERACTIONS IN AN ELECTRONIC MEDICATION MANAGEMENT SYSTEM:
Anmol Sandhu – BPharm (Hons), MRes (Health Informatics) Health Data Analytics Conference, October 2019
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
Anmol Sandhu – BPharm (Hons), MRes (Health Informatics) Health Data Analytics Conference, October 2019
HEALTH DATA ANALYTICS - OCT 2019
Prescriber’s medication
Pharmacist’s review of medication
Nurse’s documentation administration
And all the processes in between…
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Drug-drug interaction Therapeutic Duplication Dose Range breach Drug-allergy interaction Local Restriction rules
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Prescribers can ‘accept’ or ‘override’ (bypass) alerts
Alert volume experienced by prescriber
HEALTH DATA ANALYTICS - OCT 2019
WHAT IS IT? HOW CAN IT BE MEASURED?
HIGH ALERT BURDEN
Ignore all alerts, including critical
User frustration High override rates Threshold for alert fatigue is unknown
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STUDY SITE
Bone Marrow Transplantation, AIDS/HIV, Cardiology, Cancer care
(MedChart – DXC Technology)
Medicines Management Pharmacist
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Drug-Allergy Therapeutic Duplication Dose Range (limited) Local restriction rules (limited)
Passive (order sentence, sets, on-demand look up)
Drug-drug interaction (DDI) alerts
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HEALTH DATA ANALYTICS - OCT 2019
PRESCRIBER VIEW
13 alerts for DDIs 17 instances where Dr must complete an action – override +/- make a comment or cancel order (remove)
in another
DDIs?3,4
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HEALTH DATA ANALYTICS - OCT 2019
HEALTH DATA ANALYTICS - OCT 2019
INFORMED DECISION
Allergy & Intolerance Dose Range Local rules Therapeutic Duplication DDI
Alert Condition 1 (Live system) Reference condition
Alert Condition 2 (Test system)
(All - unknown/ moderate/severe)
extracted and replicated in a ‘test’ EMM system
HEALTH DATA ANALYTICS - OCT 2019
HEALTH DATA ANALYTICS - OCT 2019
Live EMM system Extracted AC1 data Analyse
Input manually AC1 patient profiles
ALERT CONDITION 2
AC1 background medications
AC1 Results
Extract data using SQL queries Extract data using SQL queries
Test EMM system Extracted AC 2 data Analyse ALERT CONDITION 1
AC1 study medications
AC2 Results
EMM = electronic medication management; SQL = standard query language; AC1 = Alert Condition 1; AC2 = Alert Condition 2
HEALTH DATA ANALYTICS - OCT 2019
Alerts generated Medication orders with at least 1 alert (%) Alerts per medication
Alert Condition 1 (No DDI alerts)
209 145 (25%) 1.4 ( 0 - 4)
Alert Condition 2 (All DDI alerts)
1063 348 (60%) 3.1 (0 - 11)
Increase
+509%* +240%* +212%*
HEALTH DATA ANALYTICS - OCT 2019
*Statistically significant increase with DDI alerts (p<0.005)
Medication orders Background: 2728 Study date: 576 Patients 254 admitted inpatients
Alerted doctors (%) Alerts per doctor (range) Proportion of prescribed medicines that generated alerts Alert Condition 1 (No DDI alerts)
55 (71%) 3.8 (1-13) 38%
Alert Condition 2 (All DDI alerts)
71 (91%) 15 (1-85) 72%
% Increase
+121% +395% +188%
HEALTH DATA ANALYTICS - OCT 2019
576 medication orders prescribed by 78 unique doctors on the study date. Mean of 7.4 medication orders (range: 1 – 28) prescribed per doctor.
Statistically significant increase with DDI alerts (p<0.005)
Alerted doctors (%) Alerts per doctor (range) Alerted medication
Moderate DDIs
67 (86%) 7.8 (1- 38) 57%
% Increase +121% +205%* +145%* Severe DDIs
59 (76%) 4.7 (1-18) 32%
% Increase 107% 124% 113%
HEALTH DATA ANALYTICS - OCT 2019
*Statistically significant increase with DDI alerts (p<0.005)
Moderate 29% Severe 8% Unknown 63%
21
Why so high?
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28 medication
prescribed 72% of medication
an alert 20 medication
3 alerts per
60 alerts (vs 15 alerts)
alerts encountered alert fatigue
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Strengths:
Limitations:
(e.g. reduced ADEs) Challenges:
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HEALTH DATA ANALYTICS - OCT 2019
1.
(ACSQHC); Sydney, 2017. 2. Westbrook JI, Reckmann M, Li L, et al. Effects of two commercial electronic prescribing systems on prescribing error rates in hospital in-patients: a before and after study. PLoS Med 2012; 9(1): e1001164. 3. Wolfstadt JI, Gurwitz JH, Field TS, et al. The effect of computerized physician order entry with clinical decision support on the rates of adverse drug events: a systematic
4. Nabovati E, Vakili-Arki H, Taherzadeh Z, et al. Information Technology-Based Interventions to Improve Drug-Drug Interaction Outcomes: A Systematic Review on Features and Effects. Journal of Medical Systems 2017; 41(1): 1-17. 5. van der Sijs H, Mulder A, van Gelder T, Aarts J, Berg M, Vulto A. Drug safety alert generation and overriding in a large Dutch university medical centre. Pharmacoepidemiol Drug Saf 2009; 18(10): 941-7. 6. Kalmeijer MD, Holtzer W, van Dongen R, Guchelaar H-J. Implementation of a computerized physician medication order entry system at the Academic Medical Centre in
7. Zenziper Y, Kurnik D, Markovits N, et al. Implementation of a clinical decision support system for computerized drug prescription entries in a large tertiary care hospital. Isr Med Assoc J 2014; 16(5): 289-94. 8. Zenziper Straichman Y, Kurnik D, Matok I, et al. Prescriber response to computerized drug alerts for electronic prescriptions among hospitalized patients; 2017. 9. Jani YH, Barber N, Wong ICK. Characteristics of clinical decision support alert overrides in an electronic prescribing system at a tertiary care paediatric hospital. International Journal of Pharmacy Practice 2011; 19(5): 363-6. 10. Seidling HM, Storch CH, Bertsche T, et al. Successful strategy to improve the specificity of electronic statin-drug interaction alerts. Eur J Clin Pharmacol 2009; 65(11): 1149- 57. 11. Paterno MD, Maviglia SM, Gorman PN, et al. Tiering drug-drug interaction alerts by severity increases compliance rates. J Am Med Inform Assoc 2009; 16(1): 40-6. 12. Payne TH, Hines LE, Chan RC, et al. Recommendations to improve the usability of drug-drug interaction clinical decision support alerts. J Am Med Inform Assoc 2015. 13. Baysari MT, Tariq A, Day RO, Westbrook JI. Alert override as a habitual behavior - a new perspective on a persistent problem. J Am Med Inform Assoc 2017; 24(2): 409-12. 14. Phansalkar S, Desai AA, Bell D, et al. High-priority drug-drug interactions for use in electronic health records. J Am Med Inform Assoc 2012; 19(5): 735-43.
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HEALTH DATA ANALYTICS - OCT 2019
HEALTH DATA ANALYTICS - OCT 2019
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Moderate 29% Severe 8% Unknown 63%
DDI alert pair
Severity Opioid agonists + Benzodiazepines 40 Moderate Opioid agonists + Opioid antagonists 17 Severe Opioid agonists + Various general anaesthetics 10 Moderate Opioid agonists + Pregabalin 9 Moderate Benzodiazepines + Antipsychotics 9 Severe
triggered (N= 316)
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Alerts generated Medication orders with at least 1 alert Alerts per medication order (range)
Hospital 1 209 25% 1.4 ( 0 - 4) Hospital 2 (Moderate) 522 40% 2.3 (0 - 9) Increase
+250% +160% +164%
Hospital 2 (Severe) 277 28% 1.7 (0 - 6) Increase
+133% +113% +121%
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Statistically significant increase with DDI alerts (p<0.005)
Alerted doctors (%) Alerts per doctor (range) Alerted medication
Hospital 1
55 (71%) 3.8 (1-13) 27%
Hospital 3 (Mod/Severe)
67 (86%) 7.8 (1- 38) 57%
% Increase +121% +205% +145% Hospital 3 (Severe)
59 (76%) 4.7 (1-18) 32%
% Increase 107% 124% 113%
HEALTH DATA ANALYTICS - OCT 2019
576 medication orders prescribed by 78 unique doctors on the study date. Mean of 7.4 medication orders (range: 1 – 28) prescribed per doctor.
Statistically significant increase with DDI alerts (p<0.005)