Machine intelligence for early targeted precision management and response to outbreaks of respiratory infections

Zhan, Tiange; Goyal, Dev; Guttag, John; Mehta, Rudra; Elahi, Zahoor; Syed, Zeeshan; Saeed, Mohammed

doi:10.37765/ajmc.2020.88456

“…Machine learning-based rankings of orthopedic facilities were generated for each beneficiary undergoing hip replacement in 2018 using a commercially available software system (Precision Navigation; Health at Scale Corp) that uses precision navigation and was trained on data prior to 2018 (ie, from 2013-2017) [18]. The top hospital for each beneficiary was determined as the top-ranked result returned by this system based on the patient's individual characteristics.…”

Section: Precision Navigationmentioning

confidence: 99%

“…These include consumer ratings, government quality ratings (eg, Centers for Medicare & Medicaid Services [CMS] stars), reputation rankings, and average volumes and outcomes [5][6][7][8][9][10][11][12][13][14][15][16][17]. Machine learning-based online tools are emerging as an alternative method of predicting provider performance that can factor patient-specific characteristics into provider rankings [17,18]. Unfortunately, there is little prior research into the comparative performance of these methods in predicting outcomes associated with different providers [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Comparing Precision Machine Learning With Consumer, Quality, and Volume Metrics for Ranking Orthopedic Surgery Hospitals: Retrospective Study

Goyal¹,

Guttag²,

Syed³

et al. 2020

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Background Patients’ choices of providers when undergoing elective surgeries significantly impact both perioperative outcomes and costs. There exist a variety of approaches that are available to patients for evaluating between different hospital choices. Objective This paper aims to compare differences in outcomes and costs between hospitals ranked using popular internet-based consumer ratings, quality stars, reputation rankings, average volumes, average outcomes, and precision machine learning–based rankings for hospital settings performing hip replacements in a large metropolitan area. Methods Retrospective data from 4192 hip replacement surgeries among Medicare beneficiaries in 2018 in a the Chicago metropolitan area were analyzed for variations in outcomes (90-day postprocedure hospitalizations and emergency department visits) and costs (90-day total cost of care) between hospitals ranked through multiple approaches: internet-based consumer ratings, quality stars, reputation rankings, average yearly surgical volume, average outcome rates, and machine learning–based rankings. The average rates of outcomes and costs were compared between the patients who underwent surgery at a hospital using each ranking approach in unadjusted and propensity-based adjusted comparisons. Results Only a minority of patients (1159/4192, 27.6% to 2078/4192, 49.6%) were found to be matched to higher-ranked hospitals for each of the different approaches. Of the approaches considered, hip replacements at hospitals that were more highly ranked by consumer ratings, quality stars, and machine learning were all consistently associated with improvements in outcomes and costs in both adjusted and unadjusted analyses. The improvement was greatest across all metrics and analyses for machine learning–based rankings. Conclusions There may be a substantive opportunity to increase the number of patients matched to appropriate hospitals across a broad variety of ranking approaches. Elective hip replacement surgeries performed at hospitals where patients were matched based on patient-specific machine learning were associated with better outcomes and lower total costs of care.

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“…These include consumer ratings, government quality ratings (eg, Centers for Medicare & Medicaid Services [CMS] stars), reputation rankings, and average volumes and outcomes [5][6][7][8][9][10][11][12][13][14][15][16][17]. Machine learning-based online tools are emerging as an alternative method of predicting provider performance that can factor patient-specific characteristics into provider rankings [17,18]. Unfortunately, there is little prior research into the comparative performance of these methods in predicting outcomes associated with different providers [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Comparing Precision Machine Learning With Consumer, Quality, and Volume Metrics for Ranking Orthopedic Surgery Hospitals: Retrospective Study (Preprint)

Goyal¹,

Guttag²,

Syed³

et al. 2020

Preprint

0

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BACKGROUND Patients’ choices of providers when undergoing elective surgeries significantly impact both perioperative outcomes and costs. There exist a variety of approaches that are available to patients for evaluating between different hospital choices. OBJECTIVE This paper aims to compare differences in outcomes and costs between hospitals ranked using popular internet-based consumer ratings, quality stars, reputation rankings, average volumes, average outcomes, and precision machine learning–based rankings for hospital settings performing hip replacements in a large metropolitan area. METHODS Retrospective data from 4192 hip replacement surgeries among Medicare beneficiaries in 2018 in a the Chicago metropolitan area were analyzed for variations in outcomes (90-day postprocedure hospitalizations and emergency department visits) and costs (90-day total cost of care) between hospitals ranked through multiple approaches: internet-based consumer ratings, quality stars, reputation rankings, average yearly surgical volume, average outcome rates, and machine learning–based rankings. The average rates of outcomes and costs were compared between the patients who underwent surgery at a hospital using each ranking approach in unadjusted and propensity-based adjusted comparisons. RESULTS Only a minority of patients (1159/4192, 27.6% to 2078/4192, 49.6%) were found to be matched to higher-ranked hospitals for each of the different approaches. Of the approaches considered, hip replacements at hospitals that were more highly ranked by consumer ratings, quality stars, and machine learning were all consistently associated with improvements in outcomes and costs in both adjusted and unadjusted analyses. The improvement was greatest across all metrics and analyses for machine learning–based rankings. CONCLUSIONS There may be a substantive opportunity to increase the number of patients matched to appropriate hospitals across a broad variety of ranking approaches. Elective hip replacement surgeries performed at hospitals where patients were matched based on patient-specific machine learning were associated with better outcomes and lower total costs of care.

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Towards data-driven models for diverging emerging technologies for maternal, neonatal and child health services in Sub-Saharan Africa: a systematic review

Batani

¹

,

Maharaj²

2022

Global Health Journal

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Machine intelligence for early targeted precision management and response to outbreaks of respiratory infections

Cited by 4 publications

References 0 publications

Comparing Precision Machine Learning With Consumer, Quality, and Volume Metrics for Ranking Orthopedic Surgery Hospitals: Retrospective Study

Comparing Precision Machine Learning With Consumer, Quality, and Volume Metrics for Ranking Orthopedic Surgery Hospitals: Retrospective Study

Comparing Precision Machine Learning With Consumer, Quality, and Volume Metrics for Ranking Orthopedic Surgery Hospitals: Retrospective Study (Preprint)

Towards data-driven models for diverging emerging technologies for maternal, neonatal and child health services in Sub-Saharan Africa: a systematic review

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