Health Care Spending: Historical Trends and New Directions

Chen, Alice; Goldman, Dana P.

doi:10.1146/annurev-economics-080315-015317

Cited by 23 publications

(8 citation statements)

References 126 publications

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“…The results suggest that providing higher payment does not lead to an improvement in various aspects of hospital services. On the contrary, I find evidence that hospitals facing a price increase are more liable to the perverse incentives that the PPS is known to encourage, namely, selecting or shifting patients into higher‐paying diagnosis codes (Chen & Goldman, ). By contrast, previous theoretical works (Chalkley & Malcomson, ; Hodgkin & McGuire, ) predict that an increase in fixed payment under the PPS can lead to quality improvement, provided that the improvement in quality is followed by increased demand.…”

Section: Resultsmentioning

confidence: 99%

Hospital responses to price shocks under the prospective payment system

Shin

2018

Health Economics

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Under the prospective payment system (PPS), hospitals receive a bundled payment for an entire episode of treatment based on diagnosis‐related groups (DRG). Although there is ample evidence regarding the impact of the introduction of the PPS, there is little research on the effects of the ensuing changes in payment levels under the PPS. In 2005, the Medicare PPS changed its definition of payment areas from the Metropolitan Statistical Areas to the Core‐Based Statistical Areas, generating substantial area‐specific price shocks. Using these exogenous price variations, this study examines hospital responses to price changes under the PPS. The results demonstrate that, while the average payment amount significantly increases in the affected areas, no parallel trend is observed in admission volume, treatment intensity, and quality of services. Conversely, hospitals facing a price increase are more liable to the perverse incentives that the PPS is known to encourage, namely, selecting or shifting patients into higher‐paying DRGs. These results suggest that paying a higher price for a given service may not induce hospitals to offer services of better quality, but can rather prompt even higher payments through other behavioral responses.

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Section: Resultsmentioning

confidence: 99%

Hospital responses to price shocks under the prospective payment system

Shin

2018

Health Economics

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“…Under pressure to curb rising health care costs in the US [1], private and public payers, policymakers, clinical societies, and other stakeholders have increasingly turned to formalized health technology assessments (HTA) to assess the relative value of health interventions [2][3][4]. Health economic (HE) modeling [5] is used to predict and compare the costs and health outcomes of interventions and is commonly utilized to inform HTA outside the US [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…-Caregiver burden OutputThe model should offer a range of model outputs that decision makers can reference in decision making -QALY is a commonly used but imperfect measure -Consider clinically based outcome measures such as 'number of responders', or 'number of remitters' Other considerations As the IVI model seeks to incorporate patient-important value elements in the model design, such elements might change over time through the course of their treatment experiences CE cost effectiveness, HAM-D Hamilton Depression Rating Scale, HE health economic, MADRS Montgomery-Asberg Depression Rating Scale, MDD major depressive disorder, PHQ-9 Patient Health Questionnaire-9, QALY quality-adjusted life-years1 "Appendix 1" in the electronic supplementary material (ESM) describes briefly how the AG members were identified and recruited. Please note some members represent multiple stakeholder perspectives.…”

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confidence: 99%

Putting Stakeholder Engagement at the Center of Health Economic Modeling for Health Technology Assessment in the United States

Xie¹,

Malik²,

Linthicum³

et al. 2021

PharmacoEconomics

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While evidence generated from health economic (HE) models is being used more commonly in health technology assessment (HTA) in the US, it is not consistently adopted by different stakeholder groups or across therapeutic areas. We hypothesize that actively engaging with multiple stakeholder groups throughout the model development process may result in models more widely considered by decision makers. To test this hypothesis, the Innovation and Value Initiative has launched a modeling effort to build an open-source HE model focusing on the disease state 'major depressive disorder'. A 20-member advisory group has been formed with representatives from patients, employers, clinicians, innovators, payers, and researchers to guide the model development process. While this effort is still in the early stages, the ongoing stakeholder engagement effort has yielded valuable insights that inform the model design. We have also identified several challenges to implementing this new approach. Our early findings suggest that the stakeholder engagement approach to HE model development has the potential to improve HTA in the US.

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“…Health care expenditures in the United States (US) represent more than 19% of its gross domestic product. This is approximately twice that of other high-income countries, with a similar outcome in life expectancy [1,2,3]. Advanced imaging, including computed tomography (CT) and magnetic resonance imaging (MRI), strongly contributes to health care costs.…”

Section: Introductionmentioning

confidence: 99%

Deep-learning based Tools for Automated Protocol Definition of Advanced Diagnostic Imaging Exams

Nencka¹,

Sherafati²,

Goebel³

et al. 2021

Preprint

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This study evaluates the effectiveness and impact of automated order-based protocol assignment for magnetic resonance imaging (MRI) exams using natural language processing (NLP) and deep learning (DL).Methods: NLP tools were applied to retrospectively process orders from over 116,000 MRI exams with 200 unique sub-specialized protocols ("Local" protocol class). Separate DL models were trained on 70% of the processed data for "Local" protocols as well as 93 American College of Radiology ("ACR") protocols and 48 "General" protocols. The DL Models were assessed in an "autoprotocoling (AP)" inference mode which returns the top recommendation and in a "clinical decision support (CDS)" inference mode which returns up to 10 protocols for radiologist review. The accuracy of each protocol recommendation was computed and analyzed based on the difference between the normalized output score of the corresponding neural net for the top two recommendations.Results: The top predicted protocol in AP mode was correct for 82.8%, 73.8%, and 69.3% of the test cases for "General", "ACR", and "Local" protocol classes, respectively. Higher levels of accuracy over 96% were obtained for all protocol classes in CDS mode. However, at current validation performance levels, the proposed models offer modest, positive, financial impact on large-scale imaging networks.Conclusions: DL-based protocol automation is feasible and can be tuned to route substantial fractions of exams for auto-protocoling, with higher accuracy with more general protocols. Economic analyses of the tested algorithms indicate that improved algorithm performance is required to yield a practical exam auto-protocoling tool for sub-specialized imaging exams.

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Health Care Spending: Historical Trends and New Directions

Abstract: NBER working papers are circulated for discussion and comment purposes. They have not been peerreviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.

Cited by 23 publications

References 126 publications

Hospital responses to price shocks under the prospective payment system

Hospital responses to price shocks under the prospective payment system

Putting Stakeholder Engagement at the Center of Health Economic Modeling for Health Technology Assessment in the United States

Deep-learning based Tools for Automated Protocol Definition of Advanced Diagnostic Imaging Exams

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