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May 18th, 2010
Measuring the Severity of Medication Discrepancies:
A Community Pharmacy Perspective
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Overview Background
Introduction
Medication Discrepancies
Potential-to-harm scale
Data
Limitations
Conclusion
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Background
The Journal of the American Medical Association recently said that if adverse reactions to medications were classified as a distinct disease, it would rank as the 5th leading cause of death in the USA.
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Introduction
Modern Medicine = More Diagnoses
= More Treatment Options
= More Drugs Dispensed
However,
Increased Potential for Medication Discrepancies
Increased Risk of Medication Errors
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Introduction
What does this mean to pharmacists?
= The integrated management of medication regimes to decrease the number of medication discrepancies
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Introduction
Our study sought to investigate the prevalence of medication discrepancies in two population cohorts leaving hospital care for either a:
Outpatient Renal Ward
Long Term Care Facility
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Medication Discrepancy
Medication discrepancies, for our purposes, were taken to be any discontinuity between the pharmacy database and any other listing of the patients' medications, e.g. hospital records.
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Methods Each patient was interviewed about
his/her medication regimen.
Discrepancies were rated for potential short and long term risks based upon a novel potential-to-harm (PTH) scale
The PTH scale was devised to gauge the severity of each discrepancy
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Potential-to-Harm Scale
Long Term Risk
L1 – Low risk of discomfort or harm
L2 – Intermediate risk of discomfort or harm
L3 – High risk of discomfort or harm
Short Term Risk
S1 – Low risk of discomfort or harm
S2 – Intermediate risk of discomfort or harm
S3 – High risk of discomfort or harm
Categorical assessments were carried out by pharmacists
Potential risks in both short and long term were considered
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Examples
Example:
Short Term Risk, Low Risk of Discomfort or Harm (S1):
Patient's community pharmacy list did not include docusate sodium for prevention of constipation secondary to chronic narcotic use but patient is using regularly.
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Examples
Example:
Long Term Risk, High Risk of Discomfort or Harm (L3):
Patient's community pharmacy list included Warfarin 1mg OD but the current dose was for 2.5mg OD.
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Med Rev Form
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Results – Analysis
Table 1. Demographic Data
Longterm CareCohort(n = 29)
Renal WardCohort(n = 19)
Total(N = 48)
Mean age (±SD), yMin age, yMax age, y
82 (±9)5596
66 (±16)2181
76 (±14)2196
Male, No. (%) 14 (48.3) 11 (57.9) 25 (52.1)
Female, No. (%) 15 (51.7) 8 (42.1) 23 (47.9)
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Results – No. of Meds
10 20 30 40 50 60 70 80 90 100 1100
5
10
15
20
25
30
35
Age at Assessment
# of Meds
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Results – Analysis
Table 3. Potential-to-Harm Scale Observations
Short Term Long Term
S1 S2 S3 L1 L2 L3
Longterm Care Cohort(n = 29)No. discrepanc ies byseverity c lass per 10patients, Mean (±SD)
3 (±6) 5 (±12) 3 (±10) 5 (±7) 3 (±7) 3 (±10)
Renal Ward Cohort (n = 19)No. discrepanc ies byseverity c lass per 10patients, Mean (±SD)
5 (±7) 15 (±27) 7 (±8) 16 (±17) 0 0
Total (N = 48)No. discrepanc ies byseverity c lass per 10patients, Mean (±SD)
4(±7) 9 (±15) 5 (±10) 10 (±11) 2 (±6) 2 (±8)
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Results – Analysis
Table 2. Observed Discrepanc ies
Longterm CareCohort(n = 29)
Renal WardCohort(n = 19)
Total(N = 48)
No. Patients withdiscrepanc ies (%)
15 (51.7) 19 (100.0) 34 (70.8)
No. recorded medications,mean (±SD)
12 (±6) 15 (±4) 13 (±5)
No. medicationdiscrepanc ies, mean(±SD)
3 (±4) 5 (±3) 3 (±4)
Relative No. ofdiscrepanc ies, mean %(±SD%)
23 (35) 30 (20) 26 (29)
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Limitations
The sample size for this study was small, 48 patients, and therefore may not be a true representation of the population.
There is a degree of interviewer subjectivity in performing the medication reconciliations which may influence the results.
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Conclusion
Extrapolating from the data, we can make the following conclusions and observation:
Both populations displayed severe risks resulting from medication discrepancies.
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Conclusion
Both populations displayed severe risks resulting from medication discrepancies.
Renal patients had more discrepancies than long term care patients. Possibly the more the patient controls their own medication the more problems that can arise.
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Conclusion
Regular medication reconciliations decrease the number of medication discrepancies.
Medication reconciliations are an important tool available to community pharmacists and can be used to improve the delivery of seamless patient care.
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Conclusion
By doing medication reconciliation we have shown that it can improve patient outcomes.
The data and results of this study provide a stepping stone to further study in regards to medication related problems.
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