quality and technology

Quality and Technology

N9205 Oct. 17, 2000

Columbia University School of Nursing M6920, Fall, 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

Outcome


Assessing qualityPerson seeks care

Provider

Case FindingScreeningDiagnosis

Diagnosis

ManagementPatient Education

ReferralsTherapy

MonitoringFollowup

Desired effects Office of Technology

Assessment, 1988

Outreach Activities

Primary Prevention

Evaluation ofPresenting ComplaintHistory,Physical

Other DiagnosticProcedures


Critical issues

Selection of domain

Selection of measures

Identification of data source


A special case : technology assessment

Generally includes "machines" Would also cover pharmaceuticals? Other possible "hidden"

technologies• scheduling• staffing patterns• access systems


Use of technologies?

•clinical excellence•technological preeminence•profit maximization

•in a fee-for-service system•in a capitated or global budget

system


Assessing technology

Is this safe? Efficacious? Effective? Efficient?

• speed of outcome• quality of outcome• cost of outcome


Renal dialysis

introduction-late 60's/early 70's use of screening committees ESRD Medicare policy US compared to GB


Heart transplant

early 70’s• everybody try one• few centers persist with procedure

mid 80's• introduction of anti-rejection drugs


CABG surgery

what are the trade-offs in quality of life?

what about skill/competence• limitations on facilities performing in

NY state


BC/BS Technology Assessment Agenda for 1997

Cost Effectiveness Analyses• Cervical Cancer Rescreening Methods• Electron beam computed tomography

for CHD


Clinical Effectiveness Analyses• fetal febrnectin• functional sterotactic radiosurgery• genetic testing for colon cancer• neurostimulation for tremor• non-coronary intravascular ultrasound


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


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


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


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


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)


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


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


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


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?


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?


Mezey et al

Forced choice answers? Findings

• Racial, language and economic differences

• Level of education most significant


Zinn et al

Objective: identify contextual attributes that influence TQM adoption

Data: survey of licensed nursing home administrators, certification files and ARF


Zinn et al, Variables

Variable Definition Source

Dependent Variable

TQM Adoption Nsg. Home has adopted TQM survey

Independent Variables

Perceived competition Admin. Perception TQM survey

Herfindal index Nsg home market concentration MMACS

Excess capacity Average # empty beds/county MMACS

Hospital-based substitutes # hospitals providing LTC ARF

Nursing home size # beds in facility MMACS

M’care market penetration Proportion of dischargesMedicare

ARF

HMO membership Proportion of residents in HMO ARF

Proportion Medicare Proportion of NH residents withMedicare coverage

MMACS

Per capita income (log) Average per capita income incounty

ARF


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


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


Keeler et al

How can a good case mix method be developed?

Combination of birth certificate and hospital discharge data

Retrospective model building effort


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


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???

quality and technology

Documents

high quality

high volume

quality of life

study toassess quality

technology assessmentgenerally

clinical confounders

perfect meehan et

hoursmehan et