Dataset Information

General practice performance in referral for suspected cancer: influence of number of cases and case-mix on publicly reported data.

ABSTRACT:

Background

Publicly available data show variation in GPs' use of urgent suspected cancer (USC) referral pathways. We investigated whether this could be due to small numbers of cancer cases and random case-mix, rather than due to true variation in performance.

Methods

We analysed individual GP practice USC referral detection rates (proportion of the practice's cancer cases that are detected via USC) and conversion rates (proportion of the practice's USC referrals that prove to be cancer) in routinely collected data from GP practices in all of England (over 4 years) and northeast Scotland (over 7 years). We explored the effect of pooling data. We then modelled the effects of adding random case-mix to practice variation.

Results

Correlations between practice detection rate and conversion rate became less positive when data were aggregated over several years. Adding random case-mix to between-practice variation indicated that the median proportion of poorly performing practices correctly identified after 25 cancer cases were examined was 20% (IQR 17 to 24) and after 100 cases was 44% (IQR 40 to 47).

Conclusions

Much apparent variation in GPs' use of suspected cancer referral pathways can be attributed to random case-mix. The methods currently used to assess the quality of GP-suspected cancer referral performance, and to compare individual practices, are misleading. These should no longer be used, and more appropriate and robust methods should be developed.

SUBMITTER: Murchie P

PROVIDER: S-EPMC4647257 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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Publications

General practice performance in referral for suspected cancer: influence of number of cases and case-mix on publicly reported data.

Murchie P P Chowdhury A A Smith S S Campbell N C NC Lee A J AJ Linden D D Burton C D CD

British journal of cancer 20150416 11

<h4>Background</h4>Publicly available data show variation in GPs' use of urgent suspected cancer (USC) referral pathways. We investigated whether this could be due to small numbers of cancer cases and random case-mix, rather than due to true variation in performance.<h4>Methods</h4>We analysed individual GP practice USC referral detection rates (proportion of the practice's cancer cases that are detected via USC) and conversion rates (proportion of the practice's USC referrals that prove to be c ...[more]

PMID: 25880009

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Project description:ImportanceThe Centers for Medicare and Medicaid Services's (CMS's) 30-day risk-standardized mortality rate (RSMR) and risk-standardized readmission rate (RSRR) models do not adjust for do-not-resuscitate (DNR) status of hospitalized patients and may bias Hospital Readmissions Reduction Program (HRRP) financial penalties and Overall Hospital Quality Star Ratings.ObjectiveTo identify the association between hospital-level DNR prevalence and condition-specific 30-day RSMR and RSRR and the implications of this association for HRRP financial penalty.Design, setting, and participantsThis cross-sectional study obtained patient-level data from the Medicare Limited Data Set Inpatient Standard Analytical File and hospital-level data from the CMS Hospital Compare website for all consecutive Medicare inpatient encounters from July 1, 2015, to June 30, 2018, in 4484 US hospitals. Hospitalized patients had a principal diagnosis of acute myocardial infarction (AMI), heart failure (HF), stroke, pneumonia, or chronic obstructive pulmonary disease (COPD). Incoming acute care transfers, discharges against medical advice, and patients coming from or discharged to hospice were among those excluded from the analysis.ExposuresPresent-on-admission (POA) DNR status was defined as an International Classification of Diseases, Ninth Revision diagnosis code of V49.86 (before October 1, 2015) or as an International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnosis code of Z66 (beginning October 1, 2015). Hospital-level prevalence of POA DNR status was calculated for each of the 5 conditions.Main outcomes and measuresHospital-level 30-day RSMRs and RSRRs for 5 condition-specific cohorts (mortality cohorts: AMI, HF, stroke, pneumonia, and COPD; readmission cohorts: AMI, HF, pneumonia, and COPD) and HRRP financial penalty status (yes or no).ResultsIncluded in the study were 4 884 237 inpatient encounters across condition-specific 30-day mortality cohorts (patient mean [SD] age, 78.8 [8.5] years; 2 608 182 women [53.4%]) and 4 450 378 inpatient encounters across condition-specific 30-day readmission cohorts (patient mean [SD] age, 78.6 [8.5] years; 2 349 799 women [52.8%]). Hospital-level median (interquartile range [IQR]) prevalence of POA DNR status in the mortality cohorts varied: 11% (7%-16%) for AMI, 13% (7%-23%) for HF, 14% (9%-22%) for stroke, 17% (9%-26%) for pneumonia, and 10% (5%-18%) for COPD. For the readmission cohorts, the hospital-level median (IQR) POA DNR prevalence was 9% (6%-15%) for AMI, 12% (6%-22%) for HF, 16% (8%-24%) for pneumonia, and 9% (4%-17%) for COPD. The 30-day RSMRs were significantly higher for hospitals in the highest quintiles vs the lowest quintiles of DNR prevalence (eg, AMI: 12.9 [95% CI, 12.8-13.1] vs 12.5 [95% CI, 12.4-12.7]; P < .001). The inverse was true among the readmission cohorts, with the highest quintiles of DNR prevalence exhibiting the lowest RSRRs (eg, AMI: 15.3 [95% CI, 15.1-15.5] vs 15.9 [95% CI, 15.7-16.0]; P < .001). A 1% absolute increase in risk-adjusted hospital-level DNR prevalence was associated with greater odds of avoiding HRRP financial penalty (odds ratio, 1.06; 95% CI, 1.04-1.08; P < .001).Conclusions and relevanceThis cross-sectional study found that the lack of adjustment in CMS 30-day RSMR and RSRR models for POA DNR status of hospitalized patients may be associated with biased readmission penalization and hospital-level performance.

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Dataset Information

General practice performance in referral for suspected cancer: influence of number of cases and case-mix on publicly reported data.

Background

Methods

Results

Conclusions

Publications

General practice performance in referral for suspected cancer: influence of number of cases and case-mix on publicly reported data.

Similar Datasets

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