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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