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Doc Jaan Altosaar Li 2023-09-01 13:28:51 -06:00 committed by GitHub
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@ -19,7 +19,7 @@ This branch of the common data model, called `payless_health`, is used to develo
<img width="400" alt="image" src="https://github.com/OHDSI/CommonDataModel/assets/5317244/0a506b94-8d21-435e-b834-dca0541ea157">
Example [prototype dashboard](https://beta.payless.health/examples/mount-sinai.html) using [public data](https://www.mountsinai.org/files/MSHealth/Assets/HS/131624096_mount-sinai-hospital_standardcharges.zip) used to inform the development of the `COST` and `PRICE` tables (Open source Jupyter notebook using python for this dashboard is [here](https://colab.research.google.com/github/onefact/data_build_tool_payless.health/blob/main/notebooks/230809-mount-sinai.ipynb)).
Example [prototype dashboard]([[https://beta.payless.health/examples/mount-sinai.html](https://www.payless.health/hospital/mount-sinai)](https://www.payless.health/hospital/mount-sinai)) using [public data](https://www.mountsinai.org/files/MSHealth/Assets/HS/131624096_mount-sinai-hospital_standardcharges.zip) used to inform the development of the `COST` and `PRICE` tables (Open source Jupyter notebook using python for this dashboard is [here](https://colab.research.google.com/github/onefact/data_build_tool_payless.health/blob/main/notebooks/230809-mount-sinai.ipynb)).
## Use cases for the `COST` and `PRICE` tables
@ -28,7 +28,7 @@ There are several use cases for the `COST` and `PRICE` tables we have identified
* `[Operations]` Populating the Payer Plan Period for patients with payors mapped to the [Source of Payment Typology](https://www.nahdo.org/sites/default/files/2020-12/SourceofPaymentTypologyVersion9_2%20-Dec%2011_2020_Final2.pdf).
* `[Cost Effectiveness]` Connecting care to cost. Understanding value-based care contracts with the OMOP data model (e.g. using FHIR messages to populate the `COST` & `PRICE` tables in OMOP) that supports repatable analyses to demonstrate return on investment, and enable understanding the impact of value-based care on health outcomes and impact on costs.
* `[Health Economics & Outcomes]` Linking to the N3C data enclave (https://ncats.nih.gov/n3c)
* `[Health Economics & Outcomes; Policy Research]` Understanding how insurer market concentration affects for-profit and non-profit hospitals (e.g. replicating this type of study: https://www.healthaffairs.org/doi/10.1377/hlthaff.2022.01184).
* `[Health Economics & Outcomes; Policy Research]` Understanding how insurer market concentration affects for-profit and non-profit hospitals (e.g. replicating this type of study: https://www.healthaffairs.org/doi/10.1377/hlthaff.2022.01184). Similarly, in health deserts (areas with a lack of hospitals) some hospitals have been found to charge many multiples times Medicare cost to insurance companies. The `COST` and `PRICE` tables can understand these marketplace dynamics and how they affect health system access, usage, costs, and outcomes
* `[Health Equity; Health Economics & Outcomes]` Understanding patterns in [4000+ hospital price sheets](https://data.payless.health/#hospital_price_transparency/) that have been aggregated, and linking these to social and environmental determinants of health such as the [area deprivation index](https://www.neighborhoodatlas.medicine.wisc.edu/).
* `[Observational Health Studies]` Using the [phenotype workflow](https://arxiv.org/abs/2304.06504) developed in collaboration with the Phenotype Development & Evaluation Workgroup to assess unobserved confounding due to `COST` and `PRICE` information being unavailable or unreliable in electronic health records databases, while claims databases have unreliable.
* `[Care Navigation]` Public price information collected from [4000+ hospitals](https://data.payless.health/#hospital_price_transparency/) can be used to help patients and employers make decisions about health care benefits. Examples like the [demo above](https://beta.payless.health/examples/mount-sinai.html) ([code](https://colab.research.google.com/github/onefact/data_build_tool_payless.health/blob/main/notebooks/230809-mount-sinai.ipynb)) show how prices vary by insurance product. The common data model can help people and organizations link information contained in explanations of benefits or claim feeds to public price information; this can inform how benefits are structured to also optimize health care costs and health outcomes.