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Quality and Technology

Quality and Technology. N9205 Oct. 17, 2000. Assessing the quality of care or services. Was the right thing done? Was it done done right? Did it yield the right results?. Donabedian framework. Structure/input capital investment staffing relationships Process content sequence

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Quality and Technology

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  1. Quality and Technology N9205 Oct. 17, 2000

  2. Assessing the quality of care or services • Was the right thing done? • Was it done done right? • Did it yield the right results? M6920, Fall, 2000

  3. Donabedian framework • Structure/input • capital investment • staffing • relationships • Process • content • sequence • Outcome M6920, Fall, 2000

  4. Assessing quality Person seeks care Provider Case Finding Primary Prevention Screening Diagnosis Evaluation of Presenting Complaint Outreach Activities Diagnosis History,Physical Other Diagnostic Procedures Management Patient Education Referrals Therapy Monitoring Followup Office of Technology Assessment, 1988 Desired effects M6920, Fall, 2000

  5. Selection of domain Selection of measures Identification of data source Critical issues M6920, Fall, 2000

  6. A special case : technology assessment • Generally includes "machines" • Would also cover pharmaceuticals? • Other possible "hidden" technologies • scheduling • staffing patterns • access systems M6920, Fall, 2000

  7. Use of technologies? • clinical excellence • technological preeminence • profit maximization • in a fee-for-service system • in a capitated or global budget system M6920, Fall, 2000

  8. Assessing technology • Is this safe? • Efficacious? • Effective? • Efficient? • speed of outcome • quality of outcome • cost of outcome M6920, Fall, 2000

  9. Renal dialysis • introduction-late 60's/early 70's • use of screening committees • ESRD Medicare policy • US compared to GB M6920, Fall, 2000

  10. Heart transplant • early 70’s • everybody try one • few centers persist with procedure • mid 80's • introduction of anti-rejection drugs M6920, Fall, 2000

  11. CABG surgery • what are the trade-offs in quality of life? • what about skill/competence • limitations on facilities performing in NY state M6920, Fall, 2000

  12. BC/BS Technology Assessment Agenda for 1997 *17 • Cost Effectiveness Analyses • Cervical Cancer Rescreening Methods • Electron beam computed tomography for CHD M6920, Fall, 2000

  13. Clinical Effectiveness Analyses • fetal febrnectin • functional sterotactic radiosurgery • genetic testing for colon cancer • neurostimulation for tremor • non-coronary intravascular ultrasound M6920, Fall, 2000

  14. Critical policy problems • who is "disinterested observer" to conduct assessment? • use of consensus panels (NIH/RAND models) • one discipline? inclusion of "doers"? • OTA elimination; AHCPR down-sizing • defining "experimental"? • appeal to the courts M6920, Fall, 2000

  15. Critical research questions • use/role of public opinion • professional opinion and practice • too rapid adoption • delayed adoption • financial incentives to use/not use • short and long-term outcomes M6920, Fall, 2000

  16. Hamilton & HO • Objective: understand the relationship between volume and quality • Reason: Is it “practice makes perfect” or selective referral patterns? • Method: regression analysis of 3 years of data M6920, Fall, 2000

  17. Hamilton & Ho, Cont. • Result: negative relationship between volume and length of stay • But: fluctuations in volume had no effect on LOS or mortality • Conclusion: high volume = high quality for reasons other than practice makes perfect M6920, Fall, 2000

  18. Meehan et al. • PRO study to • assess quality of care for Medicare patients with pneumonia • determine whether process of care performance is associated with lower mortality • multi-center retrospective cohort study (14,069 patients; 3555 hospitals in US) M6920, Fall, 2000

  19. Mehan et al, cont. • Definition of process of care • time from arrival to antibiotic administration • blood culture before initial antibiotics • blood culture within 24 hours of hospital arrival • oxygenation assessment within 24 hours M6920, Fall, 2000

  20. Mehan et al, cont. • Sample Selection • decision on ICD-9-CM codes • exclusion criteria (primarily clinical confounders such as HIV) • Data collection • training of medical records abstractors M6920, Fall, 2000

  21. Mehan et al, cont. • 1/4 of elderly patients do not receive antibiotics until at least 8 hrs post admission; doing so is associated with 15% lower odds of mortality • 1/3 of elderly patients do not have a blood culture drawn within 24 hours; doing so associated with 10% lower odds of mortality M6920, Fall, 2000

  22. Mehan et al, cont. • high rate of unconfirmed pneumonia diagnoses when clinical criteria were included • Intriguing query: did presence of DNR orders limit therapy for some patients? M6920, Fall, 2000

  23. Mezey et al • Cross sectional telephone survey • Sample of 1016 from 1452 calls • over 18 • English or Spanish speaking • medical or surgical admission • no nursing home pre or post stay • Instrument? M6920, Fall, 2000

  24. Mezey et al • Forced choice answers? • Findings • Racial, language and economic differences • Level of education most significant M6920, Fall, 2000

  25. Zinn et al • Objective: identify contextual attributes that influence TQM adoption • Data: survey of licensed nursing home administrators, certification files and ARF M6920, Fall, 2000

  26. Zinn et al, Variables M6920, Fall, 2000

  27. Zinn et al, cont. • 1: more competitive markets lead to adoption--Partial support • 3: facilities in areas with higher M’care discharges more likely to adopt--support • 4: facilities in areas with greater HMO penetration are more likely to adopt--significant support M6920, Fall, 2000

  28. Zinn et al, cont • 2: Larger facilities are more likely to adopt--no support • 5: Facilities with grated proportion of M’care recipients in total census are more likely to adopt--no support M6920, Fall, 2000

  29. Keeler et al • How can a good case mix method be developed? • Combination of birth certificate and hospital discharge data • Retrospective model building effort M6920, Fall, 2000

  30. Keeler et al • Factors ruled out • race and management decisions • Factors had to have • consistent coding practices • unequivocally risk not outcome • prevalence consistent with clinician view • recorded variable associated with outcomes M6920, Fall, 2000

  31. Keeler et al • Merged data better than only one source • Simple model explains 30% of variance among hospitals • Best model explains 37% • Is the remainder practitioner choice??? M6920, Fall, 2000

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