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California Medical Review Determinations
Health Information Systems & Technology
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Free
About
This dataset originates from the California Department of Managed Health Care (DMHC) and contains decisions from all Independent Medical Reviews (IMR) administered by the DMHC since 1 January 2001. An IMR is an independent review of a healthcare service that a health plan has deemed not medically necessary, experimental/investigational, or non-emergent/urgent. If the IMR decision favours the enrollee, the health plan is required to authorise the requested service or treatment. This textual dataset is particularly suitable for Natural Language Processing (NLP) enthusiasts.
Columns
- Reference ID: A unique identifier for each case.
- Report Year: The year in which the case was reported.
- Diagnosis Category: The primary diagnosis category for the case.
- Diagnosis Sub Category: The secondary diagnosis category for the case.
- Treatment Category: The primary treatment category related to the case.
- Treatment Sub Category: The secondary treatment category related to the case.
- Determination: Indicates whether the health plan's decision was upheld or overturned.
- Type: Specifies the nature of the case, such as Experimental/Investigational, Urgent Care, or Medical Necessity.
- Age Range: The patient's age group.
- Patient Gender: The patient's gender.
- Findings: A summary of the case findings.
Distribution
The dataset is provided in a tabular format, typically a CSV file. It comprises a collection of decisions from Independent Medical Reviews, with each record representing a distinct case. While specific row counts are not provided, the dataset includes annual data from 2001 onwards, with varying numbers of cases reported each year, indicating a substantial volume of records.
Usage
This dataset is ideal for various applications, including:
- Healthcare Analytics: Analysing trends in denied healthcare services and IMR outcomes.
- Policy Research: Informing healthcare policy decisions related to medical necessity and patient appeals.
- Natural Language Processing (NLP): Developing and testing NLP models for extracting insights from textual summaries of medical review cases.
- Predictive Modelling: Building models to anticipate IMR determinations or identify factors influencing appeal outcomes.
- Health Information Systems: Enhancing systems for managing and interpreting healthcare appeals data.
Coverage
The dataset covers Independent Medical Review decisions from California, with records spanning from 1 January 2001 up to at least 2016. It includes demographic information such as patient age ranges (e.g., 51-64, 41-50) and gender (e.g., Female, Male). The cases encompass various types of determinations, with approximately 56% of decisions upholding the health plan's stance and 44% overturning it. Case types predominantly include Medical Necessity (71%) and Experimental/Investigational (27%). Common diagnosis categories include Orthopedic/Musculoskeletal and Mental health, while frequently encountered treatment categories involve Pharmacy/Prescription Drugs and Diagnostic Imaging, Screening and Testing.
License
CCO
Who Can Use It
- Healthcare Researchers: To study trends in denied health services and appeal processes.
- Data Scientists: For machine learning applications, particularly in NLP and predictive analytics related to healthcare outcomes.
- Healthcare Analysts: To gain insights into the drivers of IMR decisions and their impact.
- Policy Makers: To evaluate the effectiveness of managed healthcare regulations and identify areas for reform.
- Health Tech Developers: To build tools and applications that leverage real-world healthcare appeal data.
Dataset Name Suggestions
- California Independent Medical Review Decisions
- DMHC Healthcare Appeals Data
- California Medical Review Determinations
- Independent Medical Review Outcomes (California)
Attributes
Original Data Source: California Independent Medical Review Dataset
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