using decision support information to improve system performance peter f. luongo, ph.d. march 20,...

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Using Decision Support Information to Improve System Performance Peter F. Luongo, Ph.D. March 20, 2008

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Page 1: Using Decision Support Information to Improve System Performance Peter F. Luongo, Ph.D. March 20, 2008

Using Decision Support Information to Improve System

Performance

Peter F. Luongo, Ph.D.March 20, 2008

Page 2: Using Decision Support Information to Improve System Performance Peter F. Luongo, Ph.D. March 20, 2008

Treatment Effectiveness

Alcohol and Drug dependent people who participate in drug treatment

Decrease substance use Decrease criminal activity Increase employment Improve their social and intrapersonal functioning Improve their physical health

Drug use and criminal activity ⇓⇓ for virtually all who enter treatment results the longer they stay in treatment.

Page 3: Using Decision Support Information to Improve System Performance Peter F. Luongo, Ph.D. March 20, 2008

Statewide Maryland Automated Record Tracking (SMART)

SMART is a full electronic clinical record (EHR) All funded treatment providers report data online

SMART addresses key EHR concerns: Admission, treatment encounters and discharge Privacy, Practitioner control, Administrative oversight Interoperability = shares information through XML

Additional Modules eCourt Contract Monitoring and Billing

Page 4: Using Decision Support Information to Improve System Performance Peter F. Luongo, Ph.D. March 20, 2008

Merged Core Programming with National Web Infrastructure for Treatment Services (WITS) Application Created a Maryland Branch with Maryland Specific

programming modules Future programming from the National Application is

shared with Maryland and ADAA will make Maryland specific modules available to other WITS user jurisdictions

Stand Alone Data Analyzer Ready for deployment and training Real time access to data

ProvidersJurisdiction decision makersState oversight (ADAA)

Statewide Maryland Automated Record Tracking (SMART)

Page 5: Using Decision Support Information to Improve System Performance Peter F. Luongo, Ph.D. March 20, 2008

Under development

Automated Drug Testing – May 2008Additional Clinical Assessments and

ScreeningHomicide and suicide lethality assessmentsHIV and TB risk assessmentsMany Others

Page 6: Using Decision Support Information to Improve System Performance Peter F. Luongo, Ph.D. March 20, 2008

What do Substance Abuse Systems Need To Do?

Standardize Patient AssessmentsStandardize Patient Placement CriteriaStandardize Performance MeasuresEnsure Data Validity and ReliabilityPublish the DataAsk What It Means

Page 7: Using Decision Support Information to Improve System Performance Peter F. Luongo, Ph.D. March 20, 2008

Referral Tracking - 2006

Fiscal Year 2006 Discharges

ASAM Referred

From

Referred Patients

Subsequent Admission Within 90 Days Concurrent

Admission Never

Discharged From

No Record Found within

90 DaysActual ASAM Referred To

Different ASAM Than

ASAM Referred To

Level 0.5 18 2 11.1% 3 16.7% 0 0.0% 13 72.2%

Level I 216 12 5.6% 20 9.3% 0 0.0% 184 85.2%

Level II.1 32 5 15.6% 6 18.8% 0 0.0% 21 65.6%

Level III.1 19 2 10.5% 4 21.1% 0 0.0% 13 68.4%

Level III.7 289 28 9.7% 97 33.6% 5 1.7% 159 55.0%

Level III.7.D 9 1 11.1% 3 33.3% 0 0.0% 5 55.6%

Total 583 50 8.6% 133 22.8% 5 0.9% 395 67.8%

Discharges that have been referred or transferred to another level of care using the treatment referral type field

Concurrent admission was never discharged from = the case came to the current level of care from a previous treatment episode that was not closed. The client presumably returned to the previous treatment episode and level of care

Page 8: Using Decision Support Information to Improve System Performance Peter F. Luongo, Ph.D. March 20, 2008

Referral Tracking - 2007

Fiscal Year 2007 Discharges

ASAM Referred

From

Referred Patients

Subsequent Admission Within 90 Days Concurrent

Admission Never

Discharged From

No Record Found within

90 DaysActual ASAM Referred To

Different ASAM Than

ASAM Referred To

Level 0.5 14 0 0.0% 1 7.1% 0 0.0% 13 92.9%

Level I 111 15 13.5% 16 14.4% 1 0.9% 79 71.2%

Level II.1 32 10 31.3% 9 28.1% 0 0.0% 13 40.6%

Level III.1 50 10 20.0% 3 6.0% 3 6.0% 34 68.0%

Level III.7 288 86 29.9% 44 15.3% 6 2.1% 152 52.8%

Level III.7.D 23 12 52.2% 3 13.0% 1 4.3% 7 30.4%

Total 518 133 25.7% 76 14.7% 11 2.1% 298 57.5%

Discharges that have been referred or transferred to another level of care using the treatment referral type field

Concurrent admission was never discharged from = the case came to the current level of care from a previous treatment episode that was not closed. The client presumably returned to the previous treatment episode and level of care

Page 9: Using Decision Support Information to Improve System Performance Peter F. Luongo, Ph.D. March 20, 2008

Referral Tracking – 2006 vs 2007Fiscal Year 2006 - 2007 Discharges

ASAM Referred

From

  

Referred Patients

Subsequent Admission Within 90 Days

Concurrent Admission

Never Discharged

From

No Record Found within

90 DaysActual ASAM Referred To

Different ASAM Than ASAM Referred To

Level 0.5 2006 18 2 11.1% 3 16.7% 0 0.0% 13 72.2%

  2007 14 0 0.0% 1 7.1% 0 0.0% 13 92.9%                     

Level I 2006 216 12 5.6% 20 9.3% 0 0.0% 184 85.2%

  2007 111 15 13.5% 16 14.4% 1 0.9% 79 71.2%                     

Level II.1 2006 32 5 15.6% 6 18.8% 0 0.0% 21 65.6%

  2007 32 10 31.3% 9 28.1% 0 0.0% 13 40.6%                     

Level III.1 2006 19 2 10.5% 4 21.1% 0 0.0% 13 68.4%

  2007 50 10 20.0% 3 6.0% 3 6.0% 34 68.0%                     

Level III.7 2006 289 28 9.7% 97 33.6% 5 1.7% 159 55.0%

  2007 288 86 29.9% 44 15.3% 6 2.1% 152 52.8%                     

Level III.7.D 2006 9 1 11.1% 3 33.3% 0 0.0% 5 55.6%

  2007 23 12 52.2% 3 13.0% 1 4.3% 7 30.4%