Israeli Pilot Studies
Israeli Pilot Studies
Using MedAware’s technology, the goal of the studies was to assess the burden and impact of electronic prescription errors on healthcare outcome and cost. The studies were conducted in two large tertiary hospitals and one large HMO/ACO in Israel.
The inpatient cohort consisted of 23,092 patients with 33,342 hospitalizations and 476,155 prescriptions, which were admitted within a 6-8 month period. The outpatient cohort consisted of 409,546 patients with 43,647,747 prescriptions and with documented medical history of at least 5 years. All 3 medical institutions used full-fledged EMR systems with their own state-of-the-art rule-based decision support tools. Only prescriptions that passed the filtering of existing rule-based systems were analyzed.
Retrospective analysis of the electronic medical records was performed using MedAware’s proprietary algorithms. To simulate the real-time performance and accuracy of MedAware’s system, all events (prescriptions, blood tests, admissions/discharges, etc.) were fed into the system in a temporal order, and the prescriptions were analyzed according to the accumulated data available at the time of prescription.
Alerts were generated in more than 1% of outpatients and in more than 3% of inpatients. In the inpatient setting, 40% of the errors were correctly identified by the nurses, thus regarded as “near-misses.” The alert burden was 1/200 inpatient prescriptions and 1/1000 outpatient prescriptions.
Patients for whom alerts were generated had a significantly longer hospital length of stay (additional 2.4 to 4 days per admission) and more hospital admissions (additional 0.6 to 1.3 annual admissions) compared to patients with no alerts. Moreover, patients for whom alerts were generated had a significantly higher short-term mortality rate, as compared to patients for whom an alert was not generated (45% to 89% higher for inpatients and 20% higher for outpatient. The results were highly significant statistically, p<0.001).
The types of alerts detected by MedAware’s software were all beyond the standard drug interaction, dosage or allergy related errors. They included the following errors:
- Drug mix-up – prescribing the wrong drug
- Patient mix-up – assigning a drug to the wrong patient
- Physician unawareness of clinical data – prescribing a drug contraindicated for a patient’s status
- Outliers in monitored drugs – failure to discontinue/change dose of a drug on time
Samples of prescription errors that were identified by MedAware’s study, which were not identified by existing detection mechanisms:
- Aspirin prescribed to a bleeding patient with critically low platelet count
- Chlorambucil (chemotherapy) prescribed to a patient without cancer, instead of Chloramphenicol (antibiotic)
- Warfarin (anti-coagulant) prescribed without any indication
- Anti-diabetic (Glibenclamide) prescribed to a healthy 54-old female, without any indication of Diabetes