PDMP Track: Combining PDMP and Other Data to Combat Rx Drug Abuse

1. PDMP Track:
Combining PDMP and Other Data
to Combat Rx Drug Abuse
Presenters:
• Richard Stripp, PhD, Chief Scientific and Technical Officer, Cordant
Health Solutions
• Roneet Lev, MD, Director of Operations, Scripps Mercy Hospital
Emergency Department, and Chair, San Diego County (CA) Rx Drug
Abuse Medical Task Force
• Jonathan Lucas, MD, Chief Deputy Medical Examiner, San Diego
County (CA)
Moderator: Connie M. Payne, Executive Officer, Statewide Services,
Administrative Office of the Courts, and Member, Operation UNITE Board
of Directors

2. Disclosures
• Richard Stripp, PhD – Employment: Cordant Health
Solutions (formerly Sterling Healthcare Services)
• Roneet Lev, MD; Jonathan Lucas, MD; and Connie M.
Payne have disclosed no relevant, real or apparent
personal or professional financial relationships with
proprietary entities that produce health care goods
and services.

3. Disclosures
• All planners/managers hereby state that they or their
spouse/life partner do not have any financial
relationships or relationships to products or devices
with any commercial interest related to the content of
this activity of any amount during the past 12 months.
• The following planners/managers have the following to
disclose:
– Kelly Clark – Employment: Publicis Touchpoint Solutions;
Consultant: Grunenthal US
– Robert DuPont – Employment: Bensinger, DuPont &
Associates-Prescription Drug Research Center
– Carla Saunders – Speaker’s bureau: Abbott Nutrition

4. Learning Objectives
1. Identify reasons and methods for combining
PDMP data with other data.
2. Evaluate the effectiveness of combining
laboratory and PDMP data to identify
patients who are not taking Rx medication
responsibly, based on a nationwide study.
3. Describe a San Diego collaborative project
that combines PDMP and medical examiner
data.

5. Combining PDMP and Other Data
to Combat Rx Drug Abuse

6. Disclosure
• Richard Stripp, Chief Scientific & Technical
Officer, wishes to disclose he is an employee
of Cordant Health Solutions. He will present
this content in a fair and balanced manner.

7. Learning Objectives
1. Identify reasons and methods for combining
PDMP data with other data.
2. Evaluate the effectiveness of combining
laboratory and PDMP data to identify
patients who are not taking Rx medication
responsibly, based on a nationwide study.

8. The Problem: Isolated Pharmacy and
Laboratory Databases
• Tens of millions of urine drug tests are performed
annually in the United States, but when and if drug
tests are performed often is based on little data
beyond the prescribers "feel" for the patient.
Kelly, S. (2014, October 8). Survey: 50% of Physicians Never Check the PDMP when Prescribing Controlled Substances. Retrieved from Imprivata:
http://www.imprivata.com/blog/survey-50-physicians-never-check-pdmp-when-prescribing-controlled-substances
A recent survey shows
50% of physicians
never check the PDMP
when prescribing
controlled substances

9. The Problem: Not all patients are
created equal
• Without guidance based on patient-specific
data on who and when to test, physicians
struggle with drug testing.
The downstream effects
of this are:
• Underutilization
• Overutilization
• Inflated drug testing
costs

10. The Problem: Isolated Pharmacy and
Laboratory Databases
Laboratory Pharmacy
Lacks visibility of all
medications prescribed to
the patient
Cannot
communicate if
medications came
from multiple
sources
Cannot always differentiate
if medication was
prescribed or illegally
acquired

11. The Problem: Isolated Pharmacy and
Laboratory Databases
Laboratory Pharmacy
Cannot determine
adherence to drug
therapy
Lacks visibility of illicit
drug use
Does not identify use of
non-prescribed
medications

12. Patient Case Study #1
• Prescription
– Oxycodone
– Alprazolam
• Prescriptions were filled per PDMP
– Report shows 30 day supplies of both oxycodone and alprazolam filled
every month at one local pharmacy
• Oxycodone – 30 MG, TAB (filled 11/04) 30 day supply
• Oxycodone – 15 MG, TAB (filled 11/04) 30 day supply
• Alprazolam – 1 MG, TAB (filled 11/04) 30 day supply
• PDMP results: High Risk
– Combining prescriptions totaled 180 MEDs
– Prescribed benzodiazepines in combination with an opioid
PDMP data tells one story while the lab results
tell another

13. Patient Case Study #1
• Combined Report Review:
– By combining both PDMP data and toxicology results, we
know the prescriptions were filled and have been filled
consistently over the past year, but are not being taken.
– Additionally, the other non-disclosed medications were
not in the PDMP report. Meaning the patient is most likely
getting them from an illegal source.
What PDMP Identified What Drug Test Identified
Oxycodone
Alprazolam
Lorazepam
Buprenorphine

14. Patient Case Study #2
• Prescription
– Oxycodone
• Prescriptions were filled per PDMP
– The patient is taking more than one opioid from more than
one prescriber
• Oxycodone – 10 MG, TAB (filled 11/18) 28 day supply
• Oxycodone – 15 MG, TAB (filled 11/04) 15 day supply
• PDMP results: High Risk
– >1 opioid filled within 4 weeks from more than one
prescriber
PDMP review helps complete the story of the
lab test

15. Patient Case Study #2
• Combined Report Review:
– With the combination of drug test results and PDMP data, the
prescriber can see that the unexpected medication (alprazolam) is not
being prescribed and can discuss with the patient about the source
and need for the medication.
– A drug test alone would not have supplied enough information for the
physician.
– Having combined access to PDMP data and lab data allows the
provider to have a more informed conversation with the patient.
What PDMP Identified What Drug Test Identified
Oxycodone
Alprazolam

16. Of course the results of one or two case studies cannot
predict the behavior of an entire population.
How does combining pharmacy & laboratory data work
to identify high risk patients everywhere?

17. The Study
• Scope
– In April through June 2014,
Cordant conducted
257 toxicology screens on
237 injured workers across
48 states.
– Test subjects were selected
from the pool of patients
meeting key risk identifiers
according to PBM data.

18. The Study
• The Process
– Cordant applied the several parameters to the study
group using their pharmacy data to identify potential
risk.
– Risk criteria identified about 1/4th of the injured
worker population as potentially “high risk”.
– Once patients were identified as potentially high risk,
urine samples were collected at the doctor’s office
during their next visit and sent to one of Cordant’s five
laboratories for testing.

19. The Results
70.9% of patients tested in the study produced
inconsistent results

20. The Results
• Of patients tested, 45.6% were deemed
high risk due to meeting one or more of
the following criteria:
– Prescribed medication not detected
– Detection of an illicit drug or alcohol
– Exhibiting other aberrant results or
behavior
• Patients were deemed medium risk if
non-reported prescription medications
were detected
• Low risk if tests showed the expected
results.

21. The Results
• Toxicology testing revealed high degrees of
inconsistent test results among the tested
claimants who fit these parameters:
– Prescribed a high medication dosage: 70.9%
inconsistent test results.
– On the prescribed opioid for more than two
months: 73.8% inconsistent test results.
(Claimants in this group were often prescribed higher doses
and for a longer duration than claimants in other groups.)

22. Conclusion
• Combining pharmacy and laboratory data is extremely
effective in identifying high-risk opioid users.
– Reduces inflated drug testing costs by identifying those most
likely to abuse or misuse medications.
– Improves patient outcomes by giving the physician a more
complete picture
• Getting the right medication to the Patient at the right
time, in the right dose, and with the right monitoring
requires a combination of:
– care coordination
– lab and prescription data integration
– and clinical expertise

23. Thank You

24. Medical Examiner and CURES
Correlations
San Diego 2013
Roneet Lev, MD Scripps Mercy Hospital
Sean Petro
Oren Lee
Jonathan Lucas, MD San Diego Medical Examiner
24

25. Disclosures
• Roneet Lev, MD, has disclosed no relevant, real or
apparent personal or professional financial
relationships with proprietary entities that
produce health care goods and services.
• Jonathan Lucas, MD, has disclosed no relevant,
real or apparent personal or professional financial
relationships with proprietary entities that
produce health care goods and services.

26. About the Data
• ME Data
– 254 deaths with
prescriptions as cause
of death
– Could be with alcohol,
illicit, over the counter
• CURES Data (aka PDMP)
– Outpatient pharmacies
– Does Not Include
• VA
• Balboa Naval Hospital
• Methadone Clinics
• Inpatient hospitals
186
68
254 Prescription Related Deaths
in San Diego 2013
CURES Data
No CURES
26

27. Medical Examiner Data
Prescription 154
Prescription+Illicit 44
Prescription+Alcohol 39
Prescription+Illicit+Alcohol 10
Prescription+OvertheCounter(OTC) 4
Prescription+Alcohol+OTC 2
Prescription+Illicit+OTC 1
Prescription+Illicit+Alcohol+OTC 1
Total 254
27

28. “Death Diaries”
CURES Report
28

29. Death Diary: 56 Year Old Female
23 Scripts
10 Providers
29
February, March No Meds
April ER#1: Hydrocodone #10
Dr. R: Codeine#40, Lorazepam #42
May Dr. P: Hydrocodone #15, Lorazepam #20
June ER#2: Hydrocodone #20, Lorazepam #20
August ER#3: Oxycodone #20, Lorazepam #21
ER#4: Oxycodone #21, Lorazepam #20
September ER#5: Oxycodone #20, Lorazepam #6
Dr. L: Methadone #120
October Dr. L: Methadone #120
ER #6: Hydrocodone #15
Dr. W: Lorazepam #8
November ER #3: Oxycodone #5, Lorazepam #4
Dr. L: Methadone #120
December Dr. L: Methadone #120
January ER #7: Lorazepam #4
February 1, 2013 Dr. L: Methadone #30
Death: February 7, 2013
Methadone, Clonazepam, Phenytoin,
Carbamazepine, Gabapentin

30. Population Demographics
• Age Range 15 – 73
• Male predominance, especially for No CURES
• Average Age 46.5
254
64
46.6
186
60 46.468
76.1
46.6
Total %Male Average Age
ME Total
CURES
No CURES
30

31. 275 Pharmacies
2013 By the Numbers
Average Scripts Per Patient
- PDMP Match, No Illicit, Doctor Shopper
23.5
Highest Number of Scripts for 1 patient 123
Average Number of Pharmacies 3.12
Highest Number of Pharmacies for 1 Patient 21
Percent of Pharmacies With a Single Death
- 1 Pharmacy had 12 deaths
54.2%
31

32. Specific Medications on CURES
(Number of Patients)
• 33 Medications; 4366 Rx
Opioids Benzodiazepines Sleep Aid Stimulants Other
Hydrocodone (123) Clonazepam (44) Zolpidem (43) Amphetamine (7) Carisoprodol(30)
Oxycodone (84) Alprazolam (39) Lunesta (4) Phentermine (4) Testosterone(6)
Morphine (32) Lorazepam (37) Chloral Hydrate (2) Lisdexamfetamine (1) Lyrica (4)
Codeine (27) Diazepam (26) Zaleplon (2) Methylphenidate (1) Phenobarbital(3)
Oxymorphone (3) Chlordiazepoxide (17) Dronabinol (2)
Hydromorphone (20) Temazepam (17) Estrogen (1)
Methadone (14) Triazolam (6)
Fentanyl (13) Oxazepam (3)
Buprenorphine (11) Clorazepate (1)
Naloxone (9)
32

33. Types of Rx
190
93
17
3 10
157
122
49
9
30
Number of Patients with Medications
ME vs PDMP
ME
PDMP
33

34. Specific Medications
34

35. Number of Medication on ME Report
35
20%
80%
Single Medication
(51)
Multiple Medication
(203)

36. Single Medications on ME Report
36
3
4 4
5
7
12
OTHER SINGLE
MEDICATIONS
Diazepam (1)
Fluoxetine (1)
Ketamine (1)
Opioid (1)
Quetiapine (1)
Acetaminophen (2)
Clonazepam (2)
Hydrocodone (2)
Tramadol (2)
Venlafaxine (2)

37. PDMP Match
Rx 2 months before death matches ME report
• PDMP Match, No
Alcohol, No Illicit, No
Doc Shop (42 patients)
– 64% Female
– 51 years Ave (older)
– More Rx
– Less Providers
– Less Pharmacies
– More Single Rx
– More Opioids, Sleep
Aids, High Morphine
Equivalents, Long Acting
254
Total Deaths
100 (40%)
PDMP Match
68
(27%)
Match + No
Alcohol or Illicit
42 (16.5%)
Match +
No Illicit +
No Shoppers
37

38. Rx Types and PDMP Match
By Number of Patients
75
77
38
Opioids - 190
PDMP Match
PDMP No Match
No PDMP
32
38
23
Benzodiazepines - 93
1
2
Stimulants - 3
56
32
24
Other - 112
No one died
of Rx for
ADD/ADHD
38

39. Opioids + Benzodiazepines
• All PDMP Reports – 54% (100 patients)
• ME Deaths – 21% (55)
• ME/PDMP Match – 71% (39)
39
16
ME Reports – 254 patients
21% = Opioids + Benzodiazepines
Combination
PDMP Match
No Match
100
86
PDMP Reports - 186 patients
55% = Opioid + Benzodiazepine
Combination
Opioid + Benzo
No Combination
39

40. Chronic Use
3 or More Consecutive Months For Same Rx
Total
Population
Patients with
PDMP reports
Patients with
Chronic Use
Chronic
Use % of
Patients
San Diego Data 2013 3.2 million 816,372 13,567 1.6%
California Data 2013 38.3 million 7,057,000 200,080 2.8%
San Diego
Prescription Deaths in
2013 (ME/PDMP data)
254 deaths 186 128 68.8% of
patients
95.81%
of Rx
 69% of Deaths were Chronic Users; 96% of all Rx
2.8 1.6
68.8
California San Diego San Diego Deaths
Chronic Users
40

41. Methadone Deaths
46 total; 7 from CURES; 39 or 85% outside CURES
41
PDMP
Match (3)
6%
PDMP Match +
Doctor Shopper (3)
7% PMDP Match + Doctor
Shopper + Illicit (1)
2%
No Recent
Methadone Rx
(3)
7%
No Methadone on PDMP
(24)
52%
No PDMP Data (12)
26%
PDMP Match (3)
PDMP Match + Doctor Shopper (3)
PMDP Match + Doctor Shopper +
Illicit (1)
No Recent Methadone Rx (3)
No Methadone on PDMP (24)
No PDMP Data (12)

42. Methadone Deaths:
Many Chemical Combinations
42
4 4 4 4 4 4 4 5 5 5 6
9
12
46
Drugs included with
Methadone Deaths
OTHER SINGLE
MEDICATIONS
Lamotrigine (1)
Amitriptyline (1)
Benzodiazepine (1)
Carbamazepine (1)
Codeine (1)
Doxepin (1)
Nortriptyline (1)
Phenytoin (1)
Sertraline (1)
Cocaine (2)
Fluoxetine (2)
Hydromorphone (2)
Trazodone (2)
Alprazolam (3)
Venlafaxine (3)

43. Doctor Shopping
4 providers + 4 pharmacies in 12 months
• 52 Patients (28% of all PDMP Reports)were Doctor
Shoppers
• “The Heavy Half” = Received 51% of all Rx
• 50/50 Male/Female
28%
72%
% Doctor Shoppers
Doctor Shopper
Regular Patient
43

44. Doctor Shopper v PDMP Deaths
61.5
50
41.5
9.1 6.8
17.3
96.2
54
37
23.5
4.5 3.1
27.4
69
% PDMP
Match
% PDMP
Match, No
Illicit, No
Alcohol
Ave Number
Rx
Average
Number
Providers
Average
Number
Pharmacies
% Single Rx
Death
% Chronic
Use
Doctor Shoppers vs. PDMP Deaths
Doctor Shoppers (52)
Total PDMP Deaths (186)
44

45. Providers at-a-Glance
Total Number of Providers 713
Average # of Providers per patient 4.5
Maximum providers per patient
-PDMP Match, No Illicit, Doctor Shopper
36
Percentage of Providers With Single Death
- 3 Providers had 4 Deaths
85%
45

46. Who are the Prescribers?
• 713 total
Pain
3%
Dentistry
4% Surgery
8%
Psychiatry
11%
Emergency/U
rgent Care
20%
Primary Care
54%
PRIMARY CARE
Cardiology
Endocrinology
Family Practice (17%)
General Practice
Gastroenterology
GYN
Infection Disease
Internal Medicine (22%)
Nephrology
Neurology
Nurse Practitioner (2.6%)
Oncology
Physician Assistant (6.3%)
PM&R
Rheumatology
PAIN
Anesthesia
Pain
SURGERY
ENT
General
Neurosurgery
Ophthalmology
Orthopedics
Plastics
Podiatry
Radiology
Urology
Vascular
46

47. Where do Doctor Shoppers Go?
• Neurology 17.1 Rx per Doctor Shopper v 3.1
• Urology 100% to Doctor Shoppers
0 0 0
7
20 23
35
43 44 44 47 48 50 50 51 53 56 56
62 64 64 67 67 69 71 73 76 81
100 100 100 100
Endocrinology(1)
GI(1)
Ophthalmology(1)
GYN(6)
ID(3)
Anesthesia(7)
Surgery(15)
FamilyPractice(118)
GeneralPractice(3)
Pain(13)
PA(59)
InternalMedicine(141)
Plastics(1)
Radiology(2)
AVERAGE(713)
PM&R(11)
Dentistry(33)
Psychiatry(77)
Neurology(8)
Emergency(140)
NursePractitioner(22)
ENT(2)
Vascular(1)
Rheumatology(5)
Neurosurgery(3)
Oncology(4)
Orthopedics(22)
Podiatry(4)
Cardiology(1)
Nephrology(1)
Urology(5)
Unknown(3)
47

48. Who is Prescribing the Most?
62
65
18
14
7
11.5
6 7.6
5
1.51.8 0.45
%Total Rx %Total Pills
Rx by Specialty
Primary Care
Psychiatry
Surgery
Pain
Emergency
Dentistry

49. How many pills do you need?
19
23
58
75
79
97
123
Dentistry Emergency Psychiatry Average Primary Care Pain Surgery
Pills/Rx

50. Specialists and Medication Types
Emergency
6%
Primary
Care
69%
Surgery
11%
Dentistry
3%
Psychiatry
3%
Pain
8%
Opioids (63%) Emergency
Primary Care
Surgery
Dentistry
Psychaitry
Pain
Emergency
3%
Primary
Care
47%
Surgery
2%
Dentistry
1%
Psychiatry
47%
Benzodiazepines (25%)
Emergency
0%
Primary
Care
62%
Surgery
0%
Dentistry
1%
Psychiatry
35%
Pain
2%
Sleep Aids (3%)
Primary
Care
34%
Psychiatry
66%
Stimulants (1%)
50

51. Who is Prescribing Hydrocodone?
• 95,821 pills
• 990 Rx
• 123 Patients
Emergency
2%
Primary
Care
63%
Surgery
28%
Dentistry
1%
Psychiatry
3%
Pain
4%
51

52. Take Home Messages
“Death Dairy” to Safe Prescribing
1. Providers – Check CURES
2. Pharmacies – Check CURES
3. Don’t Mix Opioids and Benzos
4. Use Medication Agreement
5. Subtract ER Doses From Regular Rx
6. Functional Assessment verses Pain
Scale
7. Escalating dosages? Methadone? or
Addiction Referral
8. Insurance Paying?
9. DEA Watching?
10. ME office giving feedback
52

53. PDMP Track:
Combining PDMP and Other Data
to Combat Rx Drug Abuse
Presenters:
• Richard Stripp, PhD, Chief Scientific and Technical Officer, Cordant
Health Solutions
• Roneet Lev, MD, Director of Operations, Scripps Mercy Hospital
Emergency Department, and Chair, San Diego County (CA) Rx Drug
Abuse Medical Task Force
• Jonathan Lucas, MD, Chief Deputy Medical Examiner, San Diego
County (CA)
Moderator: Connie M. Payne, Executive Officer, Statewide Services,
Administrative Office of the Courts, and Member, Operation UNITE Board
of Directors