How do Hospitals Respond to Payment Incentives?

Gowrisankaran, Gautam; Joiner, Keith A.; Lin, Jianjing

doi:10.3386/w22873

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Empirical evidence is mixed: Adler-Milstein & Jha (2) find no change in case mix index following EHR adoption, whereas two other studies (38,70) find that patient severity increases following EHR adoption. Gowrisankaran et al (41) have found that EHRs led to higher codes for medical, but not surgical, claims, following the 2007 Medicare payment reform. This change increased the difficulty of documenting complications and appears to have increased EHRs' value for billing.…”

Section: Unintended Consequences Of Ehrsmentioning

confidence: 99%

The Digitization of Patient Care: A Review of the Effects of Electronic Health Records on Health Care Quality and Utilization

Atasoy

Greenwood

McCullough

2019

Annu. Rev. Public Health

187

101

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Electronic health records (EHRs) adoption has become nearly universal during the past decade. Academic research into the effects of EHRs has examined factors influencing adoption, clinical care benefits, financial and cost implications, and more. We provide an interdisciplinary overview and synthesis of this literature, drawing on work in public and population health, informatics, medicine, management information systems, and economics. We then chart paths forward for policy, practice, and research.

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Section: Unintended Consequences Of Ehrsmentioning

confidence: 99%

The Digitization of Patient Care: A Review of the Effects of Electronic Health Records on Health Care Quality and Utilization

Atasoy

Greenwood

McCullough

2019

Annu. Rev. Public Health

187

101

View full text Add to dashboard Cite

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Association of Diagnosis Coding With Differences in Risk-Adjusted Short-term Mortality Between Critical Access and Non–Critical Access Hospitals

Kosar

Loomer

Thomas

et al. 2020

JAMA

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IMPORTANCE Critical access hospitals (CAHs) provide care to rural communities. Increasing mortality rates have been reported for CAHs relative to non-CAHs. Because Medicare reimburses CAHs at cost, CAHs may report fewer diagnoses than non-CAHs, which may affect risk-adjusted comparisons of outcomes.OBJECTIVE To assess serial differences in risk-adjusted mortality rates between CAHs and non-CAHs after accounting for differences in diagnosis coding. DESIGN, SETTING, AND PARTICIPANTSSerial cross-sectional study of rural Medicare Fee-for-Service beneficiaries admitted to US CAHs and non-CAHs for pneumonia, heart failure, chronic obstructive pulmonary disease, arrhythmia, urinary tract infection, septicemia, and stroke from 2007 to 2017. The final date of follow-up was December 31, 2017. EXPOSURE Admission to a CAH vs non-CAH.MAIN OUTCOMES AND MEASURES Discharge diagnosis count including trends from 2010 to 2011 when Medicare expanded the allowable number of billing codes for hospitalizations, and combined in-hospital and 30-day postdischarge mortality adjusted for demographics, primary diagnosis, preexisting conditions, and with vs without further adjustment for Hierarchical Condition Category (HCC) score to understand the contribution of in-hospital secondary diagnoses.RESULTS There were 4 094 720 hospitalizations (17% CAH) for 2 850 194 unique Medicare beneficiaries (mean [SD] age, 76.3 [11.7] years; 55.5% women). Patients in CAHs were older (median age, 80.1 vs 76.8 years) and more likely to be female (58% vs 55%). In 2010, the adjusted mean discharge diagnosis count was 7.52 for CAHs vs 8.53 for non-CAHs (difference, −0.99 [95% CI, −1.08 to −0.90]; P < .001). In 2011, the CAH vs non-CAH difference in diagnoses coded increased (P < .001 for interaction between CAH and year) to 9.27 vs 12.23 (difference, −2.96 [95% CI, −3.19 to −2.73]; P < .001). Adjusted mortality rates from the model with HCC were 13.52% for CAHs vs 11.44% for non-CAHs (percentage point difference, 2.08 [95% CI, 1.74 to 2.42]; P < .001) in 2007 and increased to 15.97% vs 12.46% (difference, 3.52 [95% CI, 3.09 to 3.94]; P< .001) in 2017 (P < .001 for interaction). Adjusted mortality rates from the model without HCC were not significantly different between CAHs and non-CAHs in all years except 2007 (12.19% vs 11.74%; difference, 0.45 [95% CI, 0.12 to 0.79]; P = .008) and 2010 (12.71% vs 12.28%; difference, 0.42 [95% CI, 0.07 to 0.77]; P = .02). CONCLUSIONS AND RELEVANCEFor rural Medicare beneficiaries hospitalized from 2007 to 2017, CAHs submitted significantly fewer hospital diagnosis codes than non-CAHs, and short-term mortality rates adjusted for preexisting conditions but not in-hospital comorbidity measures were not significantly different by hospital type in most years. The findings suggest that short-term mortality outcomes at CAHs may not differ from those of non-CAHs after accounting for different coding practices for in-hospital comorbidities.

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How do Hospitals Respond to Payment Incentives?

Cited by 2 publications

References 19 publications

The Digitization of Patient Care: A Review of the Effects of Electronic Health Records on Health Care Quality and Utilization

The Digitization of Patient Care: A Review of the Effects of Electronic Health Records on Health Care Quality and Utilization

Association of Diagnosis Coding With Differences in Risk-Adjusted Short-term Mortality Between Critical Access and Non–Critical Access Hospitals

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