Prescription Reporting with Immediate Medication Utilization Mapping (PRIMUM)

Joseph R. Hsu, MDProfessor, Orthopaedic Trauma

Director, Limb Lengthening and Deformity Service

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More people die in USA per year of poisoning than MVC.

90%ofallpoisoningsarenaracotics related

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Is this a problem for US? NC?

Prescribingratesper100citizens,2012U.S.healthcareproviderswrote259millionprescriptionsforopioidpainkillersin2012.

PrescriptionratesarehighestintheSoutheast(Photo:SOURCECDC(MorbidityandMortalityWeeklyReport);Credit:JulieSnider,USATODAY)

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OpportunityforSystem-WideIntervention

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Prescription Reporting with Immediate Medication Utilization Mapping (PRIMUM)

Principal Investigators: Rachel Seymour, PhD, and Joseph Hsu, MD Co-investigators: Michael Beuhler, MD; Michael Bosse, MD; Stephen Colucciello, MD; Michael Gibbs, MD; Steven Jarrett, PharmD; Michael Runyon, MD; Animita Saha, MD; Brad Watling, MD; Christopher Griggs, MD; Stephen Wyatt, DO; Daniel Leas, MD; Sharon Schiro, PhD; Meghan Wally, MSPH

• Goals: 1) To identify patients at risk for misuse, abuse, and diversion of prescription opioids

and benzodiazepines. 2) To provide critical information to the prescriber at the point of care in order to inform

clinical decision-making

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Carolinas HealthCare System

• 40+ hospitals• 900+ care locations• 15,000+ clinicians• 10m+ annual encounters

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PRIMUM: Rule logic and alert system

• Prescriber selects controlled substance

• EMR searches patient chart for defined risk factors for abuse/misuse/diversion

• If risk factors identified, provides prescriber with alert

• Prescriber can continue or discontinue script.

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Identifying and Selecting the Triggers• Literature Review

– Risk factors for misuse, abuse, diversion, or overdose – Included demographic characteristics, medical conditions, prescription history, and high

risk behaviors• Expert Panel• Select triggers that are:

– Consistently and accurately documented in EMR– Searchable in the EMR– Objective!

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Intervention Phases• April-May 2015: 30-day baseline data gathered silently• May 26, 2015: Pilot test sites went live with alert

– CMC Orthopaedics – MMP and MP– CMC-Main ED– CMC-Lincoln and CMC-Pineville EDs– Internal Medicine clinic at MP– Elizabeth Family Medicine– CHC Urgent Care Morrocroft

• July 21, 2015: Alert went live across all Cerner-driven CHS facilities (outpatient)

• October 27, 2015: Alert live in ALL Cerner facilities (inpatient and outpatient)

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Initial Triggers• Early refill/Current prescription with >30% remaining expected • 3+ visits to ED or Urgent Care with onsite treatment with opioids (not

including visits leading to admission) within previous 30 days • 3+ prescriptions for opioids or benzodiazepines within previous 30 days • Previous presentation for overdose within the EMR• Positive screen for blood alcohol, cocaine, or marijuana within the EMR

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“Silent” Surveillance Phase 1: Tuning the Logic

• Made decision to run the rule when prescriber clicks to prescribe narcotic

• Programmed initial triggers into the rule• Ran the rule silently (no alert shown to prescribers) to test and

collect baseline data• Modified triggers to appropriate rates

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Final PRIMUM Logic

• Early refill/Current prescription with >50% remaining expected• 2+ visits to ED or Urgent Care with onsite treatment with opioids

(not including visits leading to admission) within previous 30 days • 3+ prescriptions for opioids or benzodiazepines within previous 30

days • Previous presentation for overdose within the EMR• Positive screen for blood alcohol, cocaine, or marijuana within the

EMR

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Baseline DataOverallEncounters*

N=770,431

OverallEncounterswithNarcotic/Benzo Prescription

N=45,9615.97%ofOverall

PrescribingEncountersw/AlertN=9,998

21.8%ofPrescribingEncounters

1.30%ofTotal *Excludingphonecallprescriptions

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Baseline DataED/UCEncounters

N=61,984OutpatientEncounters

N=595,479

ED/UCEncounterswithNarcotic/Benzo Prescription

N=18,26629.47%ofED/UC

OutpatientEncounterswithNarcotic/Benzo Prescription

N=22,5243.78%ofOutpatient

PrescribingEncountersw/AlertN=3,265

17.87%ofPrescribingEncounters

5.27%ofTotal

PrescribingEncountersw/AlertN=5.537

24.58%ofPrescribingEncounters

0.93%ofTotal

InpatientDischargesN=12,386

IPDischargeswithNarcotic/Benzo Prescription

N=4,65637.59%ofIPDischarges

CSPrescribingDischargesw/AlertN=1,057

22.70%ofPrescribingDischarges

8.53%ofTotal

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“Silent” Surveillance Phase 2: Collecting Baseline Data• 81,841 prescriptions • 2,640

prescriptions/day• 1.33 prescriptions

per prescribing encounter

Characteristic N % of Prescribing Encounters

Age<1818-64>65

1,55245,57114,624

2.5%73.8%23.7%

Facility TypeED/Urgent CareInpatient DischargeOutpatientOther

18,2674,65638,310

514

29.6%7.5%62.0%0.83%

Class of DrugOpiateBenzodiazepineBoth

45,16514,2682,314

73.2%23.1%3.8%

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Baseline DataCharacteristic N % of Prescribing

EncountersNumber of Criteria Met012345

48,16410,5172,654369430

78.0%17.0%4.3%

0.60%0.07%0.00%

Criteria MetPrescription with 50% remaining2+ visits with onsite administration3+ prescriptions Positive BAC or tox screenPrevious presentation for overdose

8,3581,2082,8734,165500

13.5%2.0%4.7%6.8%

0.81%

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Pilot Phase• One month live in select pilot sites

– CMC Orthopaedics – 2 clinics– CMC-Main/Trauma Center ED– Two regional EDs– Internal Medicine clinic– Family Medicine– Suburban Urgent Care

• Conducted debriefing interviews with prescribers in these sites to gain feedback

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Live Intervention• Alert appears to prescribers• Live in all Cerner-driven

CHS facilities (outpatient) July 21, 2015

• Live in ALL Cerner-driven CHS facilities (including inpatient discharge) October 27, 2015

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Live Data: Opioids in

Outpatient Setting

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Discussion

• Development and tuning in virtual environment vs. live “silent”

Boussadi A, Caruba T, Zapletal E, Sabatier B, Durieux P, Degoulet P. A clinical data warehouse-based process for refining medication orders alerts. J Am Med Inform Assoc. 2012;19(5):782-5.

Oppenheim M, Mintz R, Boyer A, Frayer W. Design of a clinical alert system to facilitate development, testing, maintenance, and user-specific notification. Proc AMIA Symp. 2000:630-4.

Russ AL, Chen S, Melton BL, Saleem JJ, Weiner M, Spina JR et al. Design and evaluation of an electronic override mechanism for medication alerts to facilitate communication between prescribers and pharmacists.

Ann Pharmacother. 2015;49(7):761-9.

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Discussion

• Change behavior?

Smith DH, Perrin N, Feldstein A, Yang X, Kuang D, Simon SR et al. The impact of prescribing safety alerts for elderly persons in an electronic medical record: An interrupted time series evaluation. Arch Intern Med.

2006;166:1098-104.

Loo TS, Davis RB, Lipsitz LA, Irish J, Bates CK, Agarwal K et al. Electronic medical record reminders and panel management to improve primary care of elderly patients. Arch Intern Med. 2011;171(17):1552-8.

Kucher N, Koo S, Quiroz R, Cooper JM, Paterno MD, Soukonnikov B et al. Electronic alerts to prevent venous thromboembolism among hospitalized patients. New England Journal of Medicine. 2005;352(10):969-77.

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Discussion

• Depends on Alert Fatigue

Feldstein AC, Smith DH, Robertson NR, Kovach CA, Soumerai SB, Simon SR et al. Decision Support System Design and Implementation for Outpatient Prescribing: The Safety in Prescribing Study. In: Henriksen K, Battles JB, Marks ES, editors. Advances in Patient Safety: From Research to Implementation. Rockville, MD: Agency for

Healthcare Research and Quality; 2005. p. 35-50.

Shah NR, Seger AC, Seger DL, Fiskio JM, Kuperman GJ, Blumenfeld B et al. Improving acceptance of computerized prescribing alerts in ambulatory care. J Am Med Inform Assoc. 2006;13(1):5-11.

van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of drug safety alerts in computerized physician order entry. J Am Med Inform Assoc. 2006;13(2):138-47.

Ulrich B. Alarm fatigue: A growing problem. Nephrology Nursing Journal. 2013;40(4):293

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Next Steps• Evaluate Results• Inpatient order intervention• Uniform pain agreement• MME calculator• Dissemination to other sites/EMRs• Collaboration with NCCSRS

Questions/Discussion