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Treatment Track

Preventing Opioid Overdose Deaths: Practical Skills for Clinicians

Presenters:

• Brian Manns, PharmD, Health Policy Fellow, Policy Research Analysis and Development Office, CDC

• Roger Weiss, MD, Professor of Psychiatry, Harvard Medical School, and Chief, Division of Alcohol and Drug Abuse, McLean Hospital

• Udi E. Ghitza, PhD, Health Science Administrator, NIDA

• Cynthia Campbell, PhD, Research Scientist, Division of Research, Kaiser Permanente, Northern California

Moderator: CDR Christopher M. Jones, PharmD, MPH, Senior Advisor, Office of Public Health Strategy and Analysis, Office of the Commissioner, FDA, and Member, Rx Summit National Advisory Board

Disclosures

• Brian Manns, PharmD; Udi E. Ghitza, PhD; and Christopher M. Jones, PharmD, MPH, have disclosed no relevant, real, or apparent personal or professional financial relationships with proprietary entities that produce healthcare goods and services.

• Roger Weiss, MD – Consulting Fees: Reckitt-Benckiser

• Cynthia Campbell, PhD – Contracted Research: Purdue Pharma

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

Learning Objectives

1. Explain the epidemiology of the opioid overdose crisis.

2. Describe treatment options clinicians can use to curtail opioid overdose deaths.

3. Advocate directions for advancing research and clinical practice on prevention of opioid overdose deaths.


Brian J. Manns, PharmD

Health Policy Fellow

Centers for Disease Control and Prevention

Disclosure Statement

Brian Manns, PharmD, has disclosed no relevant, real, or apparent personal or professional financial relationships with proprietary entities that produce health care goods and services.

Dramatic Increase in Overdose Deaths Related to Opioid Pain Relievers

CDC, National Center for Health Statistics, National Vital Statistics System

4,030 opioid deaths in 1999

16,235 opioid deaths in 2013

Increase in heroin deaths

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Prescription Opioids

Heroin

Cocaine

Prescription Drug Monitoring Programs (PDMPs)

Source: Alliance of States with Prescription Monitoring Programs

Status of PDMPS – December 20142013

PDMP Characteristics and Provider Utilization

• PDMP registration rate ~35% 1

• 20 states require providers to register with the PDMP 2

• 22 states mandate provider usage of PDMP 2

– Variation across mandatory use situations • In all or nearly all cases when a controlled substance (CS) is

prescribed• Initially/periodically when a CS is prescribed• Only under select circumstances

– Mandatory use requirements have increased enrollment and use, impacted CS prescribing, and reduced multiple provider episodes 3

1. Prescription Drug Monitoring Program Interoperability Standards: A Report to Congress. September 2013. Available at: http://www.healthit.gov/sites/default/files/fdasia1141report_final.pdf2. NAMSDL. Recent Legislative and Regulatory Trends in Prescription Monitoring Programs. Available at: http://www.namsdl.org/library/D651C2DC-B73E-DC6A-C450A4863CC1F73C/3. Prescription Drug Monitoring Program Center of Excellence at Brandeis. Mandating PDMP participation by medical providers: current status and experience in selected states. Available at: http://www.pdmpexcellence.org/sites/all/pdfs/COE_briefing_mandates_2nd_rev.pdf

PDMP Monitoring of Controlled Substances

PA will begin collecting data on all schedules of substances on June 30, 2015

CS Collected States/Territories Number

Schedule II only PA 1

Schedules II-IVAZ, CA, FL, IA, KS, ME, NV, NH, OR, RI, SC, VT, VA, WV, WY

15

Schedules II-V

AL, AK, AR, CO, CT, DE, DC, GA, GU, HI, ID, IL, IN, KY, LA, MD, MA, MI, MN, MS, MT, NE, NJ, NM, NY, NC, ND, OH, OK, SD, TN, TX, UT, WA, WI

35

Demographics

• Men

• 35-54 year olds

• Whites

• American Indians/Alaska Natives

Risk Factors for Overdose

Socioeconomics and Geography

• Medicaid

• Rural

Clinical Characteristics

• Chronic pain

• Substance abuse

• Mental health

• Nonmedical use

• Multiple prescriptions

• Multiple prescribers

• High daily dosage

Overdose Risk Highest Among Small Percentage of Patients at High Dosage, Group Health, 1997-2005

11.44

3.73

8.87

0

10

20

30

40

50

60

70

80

90

100

0

1

2

3

4

5

6

7

8

9

10

1-19 20-49 50-99 100+

% P

ati

en

t Y

ea

rs

Ris

k (

Od

ds

Ra

tio

)

Opioid dosage (MME/d)

Dunn et al, Opioid prescriptions for chronic pain and overdose. Ann Int Med 2010;152:85-92.

PDMP Value to Clinical Care

• Identifying patients at highest risk for overdose

– Elevated Morphine Milligram Equivalence Dose

– Polypharmacy

– Concurrent/Overlapping Prescription

• Potential Drug Interactions

– Substance Use Disorder

• Promote conversation or referral to treatment

• Protect patient from serious or fatal harm

– Opportunity for tapering and avoiding withdrawal

Examples of PDMP Added Value

• Identification of high risk use

– Baumblatt (2014) used Tennessee PDMP data to identify risk factors accounting for 55% of overdose deaths

– Katz (2009) used Massachusetts PDMP data to identify pharmacy and prescriber visit patterns

Practical Steps for Providers When Accessing the PDMP to Reduce Risk

• High number of scripts, providers, pharmacies – Discuss concerns when suspecting SUD

• Consider criteria for SUD and referral to treatment– SAMHSA Buprenorphine Physician and Treatment Locator

(http://buprenorphine.samhsa.gov/bwns_locator/)

– Communicate concerns for patient safety if not seeing an improvement in function

– Use MME to assess risk for overdose– Address potential drug interactions

• Educate patients about risks • Avoid a medication plan that includes potentially dangerous

combinations (e.g. benzodiazepines and opioids)

– Avoid abrupt discontinuation of opioids or benzodiazepines

http://buprenorphine.samhsa.gov/bwns_locator/

CDC PDMP Activities

• PEHRIIE – PDMP Electronic Health Record Integration and Interoperability Expansion Project

• PBSS – Prescription Behavior Surveillance System

• Prescription Drug Overdose: Boost for State Prevention

• Prevention for States Program

PEHRIIE

• Cooperative Agreement – SAMHSA and CDC

• Improve prescribing and dispensing practices

– Integration of PDMP data into EHRs and other health information technology (pharmacy systems)

– Interoperability of PDMPs across state lines.

PBSS

• 11 states currently provide data to PBSS

• PBSS indicators over time and by demographic and controlled substance drug categories

– Patient-Level Behavior

– Prescriber-Level Behavior

– Dispenser-Level Behavior

Prevention BOOST

• Maximizing PDMPs– Expanding interstate data sharing between PDMPs

across state borders

– Refining proactive PDMP reports to identify and address inappropriate prescribing patterns

– Shortening the PDMP reporting interval to make PDMP data more timely and useful

– Leveraging PDMPs as public health surveillance systems to better understand what drives overdose deaths

Prevention for States

• More intense effort to enhance and maximize PDMPs

– Streamline and simplify PDMP registration process

– Data sharing between PDMPs and EHRs

– Support proactive reporting and data analysis

– Move toward a real-time PDMP

Thank You

The findings and conclusions in this report are those of the author and do not necessarily

represent the views of the Centers for Disease Control and Prevention.

Brian J. Manns - - [email protected]

Treatment of Prescription Opioid Dependence

Roger D. Weiss, MDHarvard Medical School

McLean Hospital, Belmont, MANew England Consortium Node, NIDA Clinical Trials

Network

Disclosure

Roger D. Weiss, MD wishes to disclose that he has consulted to Reckitt-Benckiser. He will present this content in a fair and balanced

manner.

Overdose prevention

• Effective treatment is the best way to prevent overdoses

• Most studies of opioid dependence treatment have focused on heroin addicts in methadone maintenance treatment

• It is not clear the degree to which this applies to those with prescription opioid dependencereceiving office-based buprenorphinetreatment

Prescription Opioid Addiction Treatment Study (POATS)

• Compared treatments for prescription opioid dependence, using

– buprenorphine-naloxone (bup-nx) of varying durations

– counseling of varying intensities

• Conducted as part of National Institute on Drug Abuse Clinical Trials Network (NIDA CTN)

• 10 participating sites across the U.S.

• Largest study ever conducted for prescription opioid dependence (N=653)

Weiss RD et al. Arch Gen Psychiatry. 2011;68(12):1238-1246

POATS study questions

• Does adding individual drug counseling to bup-nx+SMM improve outcome? – May be a proxy for drug abuse treatment program

vs. office-based opioid treatment, using bup-nx

• What length of bup-nx is best for these patients? – 1-month taper?

– 3 months, then taper?

– Longer-term maintenance?

Key eligibility criteria

• DSM-IV dx of opioid dependence, not just physical dependence

• ≥20 days opioid use in past 30 days

• Additional SUDs eligible if not requiring immediate medical treatment

• Non-psychotic, psychiatrically stable

• Minimal or no heroin use (never dependent or injected, <5 d in past 30

Phase 1, up to 12 weeks

Phase 2, 24 weeks

POATS Main Trial Results

Successful outcome, Phase 1 (N=653)

Phase 1 successful outcome criteria

• ≤4 days opioid use per month

• No positive urine screens for opioids on 2 consecutive wks

• No other formal substance abuse treatment

• No injection of opioids

• No more than 1 missing urine sample during the 12 weeks

SMM + ODC SMM p

6% 7% .36


Successful outcome, Phase 2 (n=360)

Phase 2 successful outcome criteria

• Abstinent for ≥3 of final 4 weeks (including final week) of bup-nx stabilization (urine-confirmed self-report)

SMM +ODC

SMM p

Week 12

(end of stabilization)52% 47% .3


Phase 2: Successful outcome at end of taper & at follow-up

SMM +ODC

SMM Overall p

Week 16

(end of taper)28% 24% 26% .4

Week 24

(8 wks post-taper)10% 7% 9% .2


POATS Recent Findings

Patient characteristics associated with buprenorphine/naloxone treatment outcome for Rx opioid dependence

Dreifuss et al., DAD, 2013

Significant baseline predictors by outcome: Bivariate analysis (N=360)

0

10

20

30

40

50

60

70

80

90

100

Self-help* Other priortreatment*

Route of usenot as

prescribed**

OxyContinused most in

past 30days**

%

of

pa

tie

nts

Fail

Success

*p<.05, **p<.01

Significant predictors by outcome (cont’d)

Patient characteristics

at baseline

Failure

(n=183)

Successful

(n=177)Sociodemographics

Age, mean (sd)** 31 (9) 34 (10)

Opioid use history

Used heroin* 32% 20%

1st source* Medical Rx 49% 62%

Dealer 14 6

1st reason for use* To relieve pain 60 70

To get high 33 24

Other diagnoses

Major depression** Past year 14 26

Lifetime 27 41

*p<.05, **p<.01

Logistic Regression Model for Predictors of Success (N=360)

Baseline variables Odds ratio

Age, for every +10 years 1.28*

Lifetime major depression 1.82*

Prior opioid use disorder treatment .62*

Lifetime route of use other than oral or

sublingual .51^

^p<.052, *p<.05

39

Who benefits from additional drug counseling among Rx opioid

dependent pts receiving bup-nx and standard medical management?

Weiss et al., DAD, 2014

Key study questions

Do subgroups of Rx opioid dependent patients benefit from more intensive treatment, i.e., drug counseling in addition to bup-nx and standard medical management?

Compared patients with• More severe problems• Greater attendance at treatment sessions, i.e.

adherence• The interaction of the two

Did drug counseling improve outcomes in more severe patients?

Illness severity operationalized as• ASI drug composite score (mean=.34)• Heroin use (26%)• Chronic pain (41%)

RESULTSHeroin users were half as likely to have successful outcomes, but this was not related to being randomized to drug counseling.

The remaining severity measures were not associated with outcome.

Adequate attendance/adherence to treatment

Sessions offered during 12-week Phase 2• Medical management (360): 17• Drug counseling (180): 18 (plus 17 MM)

Adequate adherence set a priori at ≥60% of both MM + drug counseling sessions offered:74% of patients met this criterion

RESULTSAmong patients with adequate attendance/adherence (n=266), treatment assignment was not related to outcome.

Did patients with more severe problems have better outcomes if assigned to drug counseling?

(n=266 with adequate adherence)

Interaction between heroin & treatment p=.03

Interpretation of results

• Heroin users were as likely to succeed as non-heroin users if they were randomized to drug counseling and went (i.e., were adherent).

• Adherent heroin users assigned to SMM alone were half as likely to succeed as all other adherent Rx opioid patients.

Implications

Perhaps there are clinically meaningful subgroupsof patients with prescription opioid dependence• Older • Depressed • Initiated use for pain

vs. • Younger, more “recreational” users, including

heroin users• Latter group may require and benefit from more

intensive counseling (if they attend)

Does early response to buprenorphine-naloxone predict

treatment outcome in prescription opioid dependence?

McDermott et al., J Clin Psychiatry, 2015

Research questions

1) Is it possible to tell early in treatment whether a prescription opioid dependent patient will have a successful bup-nxoutcome?

2) How early can bup-nx treatment response be evaluated accurately?

Methods

Positive predictive value = the degree to which initial opioid abstinence predicted final successful outcome at the end of bup-nxstabilization.

Negative predictive value = the degree to which initial opioid use predicted final unsuccessful outcome at the end of bup-nx stabilization.

Predicting abstinence at end of tx(weeks 9-12)

Initial abstinence

and final

abstinence, n

Initial abstinence

and final lack of

abstinence, n

Positive

Predictive Value,

%

Week 1 101 107 49%Weeks 1-2 88 70 56Weeks 1-3 73 54 57Weeks 1-4 68 45 60

Predicting use in weeks 9-12

Initial use and final lack

of abstinence, n

Initial use and final

abstinence, n

Negative

Predictive Value,

%

Week 1 122 30 80%Wks 1-2 89 6 94Wks 1-3 72 3 96Wks 1-4 58 2 97

Acknowledgements

McLean CTN-0030 research team, with

3 World Series trophies

Our home base: Proctor House, McLean Hospital

Acknowledgements, cont’d

• Investigators, clinicians, & patients at the 10 study sites

• Jennifer Potter, Ph.D., M.P.H. and Walter Ling, M.D.

• CCTN, CCC, and DSC-1 staff

• NIDA Grants U10DA15831 & K24DA022288

54Opiate use disorders treatment:

Importance of effective relapse-

prevention medications

Udi E. Ghitza, Ph.D.

Health Scientist Administrator

National Institute on Drug Abuse (NIDA)

Session - Preventing Opioid Overdose Deaths: Practical Skills for Clinicians

Udi E. Ghitza, Ph.D.Health Scientist AdministratorNational Institute on Drug Abuse (NIDA)

April 7, 2015







Disclosure statement

Udi E. Ghitza, Ph.D. has disclosed no relevant, real or apparent personal or professional financial relationships with proprietary entities that produce health care goods and services.

The opinions in this presentation are those of Udi E. Ghitza, Ph.D. and do not represent the official position of the U.S. government.







Learning objectives of session

1. Explain epidemiology of the opioid overdose crisis.

2. Describe treatment options clinicians can use to curtail opioid overdose deaths.

3. Advocate directions for advancing research and clinical practice on prevention of opioid overdose deaths.

This talk’s focus: New treatment option for opiate use disorders, treatment as a potential means of curtailing opioid overdose deaths







Rates of prescription painkiller sales, deaths and substance abuse treatment admissions (1999-2010)







Specific Illicit Drug Dependence or Abuse in the Past Year among Persons Aged 12 or Older: 2013

Reference: Substance Abuse and Mental Health Services Administration (SAMHSA). Results from the 2013

National Survey on Drug Use and Health: Summary of National Findings. Rockville, MD: SAMHSA, 2014.







Relapse rates for opiate use are high despite available medication-assisted treatment options

Prescription pain relievers that are full μ-opioid receptor agonists are same class of drugs as heroin.

FDA-approved medication-assisted treatments (MAT) for opiate use disorders:

Buprenorphine/naloxoneMethadoneNaltrexone

MAT are adjuncts to evidence-based psychosocial treatments

However, discharges from MAT programs and relapse rates remain high.

Reference: Substance Abuse and Mental Health Services Administration (SAMHSA). Treatment Episode Data Set (TEDS):

2011. Discharges from Substance Abuse Treatment Services. Chapter 8. Rockville, MD: SAMHSA, 2014.







Clonidine maintenance prolongs opioid abstinence anddecouples stress from craving: a randomized controlled trial (RCT) with ecological momentary assessment

• Placebo-controlled RCT testing effectiveness of clonidine (alpha-2 adrenergic receptor agonist) added to buprenorphine to reduce lapses to and extend abstinence from opiate use in opiate-use-disorders patients

• 118 participants who maintained opiate abstinence during weeks 5-6 were continued on buprenorphine and randomized to clonidine (n=61) or placebo (n=57) for 14 weeks.

• Urine was tested thrice weekly.

Reference: Kowalczyk, Phillips, Jobes, Kennedy, Ghitza…Preston (2015) The American Journal of Psychiatry (in press)







Clonidine maintenance prolongs opioid abstinence and decouples stress from craving: a randomized controlled trial (RCT) with ecological momentary assessment

• Clonidine plus buprenorphine produced longest duration of consecutive days of abstinence from opiates during intervention, versus placebo + buprenorphine (34.8±3.7 days versus 25.5±2.7 days, P<0.05, Effect size: d=0.38).

• Clonidine group also took longer to lapse (hazard ratio:0.67, 95% CI=0.45-1.00, P<0.05).

• Ecological momentary assessment showed daily-life stress was decoupled from opiate craving in the clonidine group (interaction F(3,257)=8.8, P<0.01).








The effect of clonidine on longest periodof opioid abstinence








The effect of clonidine on time to opioid lapse








Conclusions

• Clonidine may be useful off-label in treating opiate use disorders, not just to reduce withdrawal signs, but also as an adjunct maintenance treatment that increases duration of opiate abstinence and delays lapses

• Even in absence of withdrawal, it decouples stress from craving

• Participants in the clonidine group were not more likely to report an adverse event compared with placebo. Therefore, the doses of clonidine used appear to be safe and well tolerated in this population.








Clinical Implications

• Clonidine may effectively work off-label as a maintenance medication and adjunct to buprenorphine to enhance prevention of lapses to opiate use and prolong abstinence.

• This is a major new use for a readily available old drug.

• Clinicians have more options in their toolbox to prevent lapses to opiate use and enhance abstinence in opiate-dependent patients.








Acknowledgments

• Kenzie L. Preston Ph.D., NIDA Intramural Research Program (IRP)

• William J. Kowalczyk Ph.D., NIDA IRP

• Karran A. Phillips M.D., NIDA IRP

• Michelle J. Jobes Ph.D., NIDA IRP

• Ashley P. Kennedy Ph.D., National Institute of Mental Health (NIMH)

• Daniel A. Agage M.D., NIDA IRP

• John P. Schmittner M.D., Spectrum Health System

• David H. Epstein Ph.D., NIDA IRP







Thank you!

Addressing the Prescription Opioid Crisis in Health Systems: Role of EHRs and RegistriesPreventing Opioid Overdose Deaths: Practical Skills for Clinicians

Cynthia Campbell, PhD, Constance Weisner, DrPHKaiser Permanente Division of Research

Drug and Alcohol Research Team

National Rx Drug Abuse Summit, Atlanta, GA

April 7, 2015

Disclosures

• Cynthia Campbell, PhD – Contracted Research: Purdue Pharma

Overview• What we know from studying a health plan

– Long-term opioid use – patient characteristics

– Substance abuse, depression, adverse events

• Clinical implications

– EHR-supported registries

• What goes into an EHR

• How this informs clinical tools and registries

• Clinician Query Tool

Prescription Opioid Problem: Context for Health Care Systems

Integrated health care delivery system (medical, psychiatry & AOD services)

3.6 + million members (45% of market share, diversity increasing with ACA)

Longitudinal data & long membership enrollment

Harmonized data with 18 health plans

Pain and Opioids: Challenges in a Health System

• Complex patients, high utilizers

• Health systems very interested• Chronic pain programs

• Purchasers very interested

• Highlighted SU as a problem• Identifying misuse

Trends in Prescription Opioid Use in Health Systems: Substance Abuse History

0%

10%

20%

30%

40%

50%

60%

1997

1998

1999

2000

2001

2002

2003

2004

2005

Prevalence of Long-Term Opioid Use by Substance Use Diagnosis (diagnosis in prior 2 years)

Prevalence

long-term

opioid use

No Drug or Alcohol (including Opioid)

diagnosis

Kaiser N California Group Health

Supported by NIDA Grant R01 DA022557

Weisner CM, Campbell CI, Ray GT, Saunders K, Merrill JO, Banta-Green C, Sullivan MD, Silverberg MJ, Mertens JR, Boudreau D, Von Korff M. Trends in prescribed opioid therapy for non-cancer pain for individuals with prior substance use disorders. Pain 2009;145(3):287-293.

0%

10%

20%

30%

40%

50%

60%

1997

1998

1999

2000

2001

2002

2003

2004

2005


Prevalence

long-term

opioid use

Drug or Alcohol Diagnosis


diagnosis


0%

10%

20%

30%

40%

50%

60%

1997

1998

1999

2000

2001

2002

2003

2004

2005

Prevalence

long-term

opioid use

Opioid Disorder Diagnosis

Drug or Alcohol Diagnosis


diagnosis



Prevalence of Long-Term Opioid Use by Depression Dx in Prior 2 Years, 1997-2005

Kaiser N California (solid lines) & Group Health (dashed lines)

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

1997

1998

1999

2000

2001

2002

2003

2004

2005

Prevalence (%)

of long-term

opioid use

Depression Dx

No Depression Dx

Braden J, Sullivan MD, Ray GT, Saunders K, Merrill J, Silverberg MJ, Rutter CM, Weisner C, Banta-Green,

Campbell CI, Von Korff M, et al. (2009). Trends in Long-term Opioid Therapy for Non-Cancer Pain among Persons

with a History of Depression. General Hospital Psychiatry. 31(6):564-70

Opioid overdoses (fatal and non-fatal)

• Higher dosage levels related to higher likelihood of overdose– Patients with highest dose (100 mg/d+) had highest risk (HR=8.9, 95% CI 4.0-

20) compared with patients receiving 1 to 20 mg/d

• Those with highest dose compared with lowest:

– Male, substance abuse , current smokers, depression, higher Charlson comorbidity scores

Dunn KM, Saunders KW, Rutter CM, Banta-Green CJ, Merrill JO, Sullivan MD, Weisner CM, Silverberg MJ, Campbell CI, Psaty BM, Von Korff M. (2010). Opioid prescriptions for chronic pain and overdose. Annals of Internal Medicine. 152(2):85-92

Controlled for age, sex, smoking, depression diagnosis, substance abuse diagnosis, index

pain diagnosis, and chronic disease comorbidity adjustors

Electronic health record and opioid registries: Clinical implications

What is a registry?

• Continually refreshed database on a group of people meeting certain criteria

• Provides up-to-date information (e.g. clinical characteristics, health care utilization, medication use)

Example: Prescription Opioid Registry

• Proof-of-concept project with NIDA to explore use of EHR

• Looking across four years (2010-2014)

• Creating daily records of opioid use as well as episodes of use with varying criteria

• Data elements include:– Pharmacy data: new and long term prescription opioid users, opioid

misuse, overdose and poisoning, mortality, other adverse events (e.g. fractures).

– Demographics, socioeconomic, comorbidities

– Health services use

• Can refresh periodically to make a “living” registry

Registry Development

Health System/Clinical

EncountersEHR Registries

Health System Encounters

Primary Care Specialty

(Substance use, Mental

Health)

Hospital

Health IT

Pharmacy

Laboratory

Integration of Substance Use

Specialty Care

Primary Care

Screen and treat in PC

(if moderate problem,

continue monitoring)

Specialty care if needed

Back to primary care for

monitoring

Bodenheimer T, Wagner E, Grumback K. Improving primary care for patients with chronic illness. JAMA .

2002; 288:1775-1779.

Von Korff M, Gruman J, Schaefer J, Curry SJ, Wagner E. Collaborative management of chronic illness.

Ann Intern Med. 1997; 127(12):1097-1102.

Chi FW, Parthasarathy S, Mertens JR, Weisner C. (2011) Continuing care and long-term substance use

outcomes in managed care: initial evidence for a primary care based model. Psychiatr Serv.

2011;62(10):1194–1200.

How Does A Registry Develop?



Transforming Healthcare Data Into Usable Information

92

Utilization

Research Database

Data Span: 1960 – Current

REG+ (Legacy ED & Clinic encounters)

Diagnosis Procedures

Pharmacy Lab Results Lab Notes

EnrollmentDemographic

sECG

ProvidersRehabilitatio

nVitals

Enrollment

SSA Death

CA Death

Cancer/SEERCause of

deathCensus

DiabetesMellitus

IP Clinical Warehouse

KidneyDisease

OSCR (Legacy ED & Clinic Diagnoses & Procs)

AOMS (Referrals for Contracted Non-KP Care)

CATS (Non-KP Emergency Claims)

eConsult (Referrals within KP)

ADT (Legacy Hospital Diagnoses & Procedures)

KITS (Immunization)

LURS (Inpatient & Outpatient Labs)

CPM (Facilities)

PATDEM (Patient Demographic Features)

TRRS (Radiology Reports)

FRSS (Provider Info)

PARRS (KP Appointments)

CAMMOLOT/COPS (Legacy Chemotherapy)

TraceMaster (ECGs)

CoPath (Pathology)

KP.Org

KP HealthConnect (Clarity)

Mortality

Cancer Registry

RPGEHAd-hoc SAS Data Sets

KP CESR Virtual Data Warehouse

FDA Mini-Sentinel Common Data Model

Web Application

Oracle 11G

14TB

KP Virtual Data Warehouse (VDW)

How Does A Registry Develop?



Data cleaning, algorithm development, QC, clinical consult

How Can Registries Be Used?• Can identify high risk patients according to various criteria• Track outcomes of interest over time

– Long term use– Poisoning/overdose, abuse/dependence, ER use

• Examine comorbidities• Family members• Correlation with other health outcomes with access to the whole

medical record, other registries• Can target certain populations for disease management

– Cancer Registry, HIV Registry, Diabetes, Preventing Heart Attack and Stroke Registry (PHASE)

• Treatment and care processes• Can identify providers of these patients for interventions• Collaborating with other health systems to create networks

– Potential for surveillance for some substances– Common Data Elements

How Clinicians Use These Data

Data Warehouse/Registries

Query Tool

VDW Query Tool Capabilities

• Web-based interface for querying• Flexible, fast• Draws from multiple data sources, including registry

data• Large-volume, longitudinal data• Basic and advanced queries• Breakdown by patient demographics and KP facility• Results delivered to browser, Excel export, or e-mail• Save, re-run and re-edit queries• As good as the user’s questions

98

Implications

• Exciting time– Integration of SU treatment with primary care

– EHRs more widely available

• More data becoming available in the EHR that can feed SUD registries

• More information available to help manage patients– Development of clinical tools

– Health IT

AOD Research at Division of ResearchPrincipal InvestigatorsCynthia Campbell, PhDLyndsay Ammon Avila, PhDDerek Satre, PhDStacy Sterling, MSW, MPHKelly Young-Wolff, PhDConnie Weisner, DrPH, LCSW

Health EconomistSujaya Parthasarathy, PhD

Senior Research AdministratorAlison Truman, MHA

Analysts/BiostaticiansFelicia Chi, MPHAndrea H Kline Simon, MSWendy Lu, MPHTom Ray, MBA

Interview SupervisorGina Smith Anderson

Project ManagerMonique Does, MPHLuisa Hamilton

Project CoordinatorsSabrina Wood, BA

Research Associates Georgina BerriosVirginia BrowningNancy CharvatMelanie JacksonElinette NicholsDiane Lott-GarciaIrene Kane

KPNC Members

KPNC Primary Care

KPNC Chemical Dependency Quality Improvement Committee

KPNC Adolescent Medicine Specialists Committee

KPNC Adolescent Chemical Dependency Coordinating Committee

KPNC Oakland Pediatrics Department

KPNC ADHD Steering Committee

KPNC Regional Mental Health and Chemical Dependency

Research Clinicians

Thekla B Ross, PsyD

Ashley Jones, PsyD

Amy Leibowitz, PsyD

Catherine Marino, PsyD

Clinical Partners

Anna Wong, PhD

Charles Wibbelsman, MD

David Pating, MD

Barry Levine, MD

Charles Moore, MD, MBA

Don Mordecai, MD

Cosette Taillac, LCSW

Murtuza Ghadiali, MD

Mason Turner, MD

Andrea Rubinstein, MD

Thank you!

[email protected]

Treatment Track


Presenters:

• Brian Manns, PharmD, Health Policy Fellow, Policy Research Analysis and Development Office, CDC

• Roger Weiss, MD, Professor of Psychiatry, Harvard Medical School, and Chief, Division of Alcohol and Drug Abuse, McLean Hospital

• Udi E. Ghitza, PhD, Health Science Administrator, NIDA

• Cynthia Campbell, PhD, Research Scientist, Division of Research, Kaiser Permanente, Northern California

Moderator: CDR Christopher M. Jones, PharmD, MPH, Senior Advisor, Office of Public Health Strategy and Analysis, Office of the Commissioner, FDA, and Member, Rx Summit National Advisory Board

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