Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Data Collection I01:30

Data Collection I

Data collection gathers information needed to make accurate judgments about a patient's present condition. During a health history interview, subjective data is collected from the patient, their caregivers, or family members, and objective data is collected through observations and physical assessment. Patients are the primary source of subjective data. Thus information gathered from patients through interviews, observations, and physical examination is primary data. Secondary sources of data...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Data Reporting and Recording01:24

Data Reporting and Recording

Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
Nursing Assessment01:29

Nursing Assessment

The two sources for collecting information are primary and secondary. After gathering information, interpretation and validation help to complete the data. The purpose of assessment is to establish data with the initial information, to interpret data about the patient's perceived needs and health problems, and to respond to these problems identified.
The nurse collects all aspects of the patient's health in the initial assessment, establishing priorities for ongoing focused assessments and...
Relative Risk01:12

Relative Risk

Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Non-Profit Hospital Governance, Conduct, and CEO Pay.

Inquiry : a journal of medical care organization, provision and financing·2025
Same author

Trauma Center Hospitals Charged Higher Prices For Some Nontrauma Care Than Non-Trauma Center Hospitals, 2012-18.

Health affairs (Project Hope)·2024
Same author

The relationship between provider age and opioid prescribing behavior.

The American journal of managed care·2022
Same author

Does Multispecialty Practice Enhance Physician Market Power?

American journal of health economics·2021
Same author

The effects of medicare advantage on opioid use.

Journal of health economics·2020
Same author

Competition in Outpatient Procedure Markets.

Medical care·2018
Same journal

Designing Care Beyond the Hospital: Revealing Hidden Care Demands in Hospital-At-Home Services.

Inquiry : a journal of medical care organization, provision and financing·2026
Same journal

Fundamental Values in Nursing Care: The Person's Perspective.

Inquiry : a journal of medical care organization, provision and financing·2026
Same journal

Network Analysis of Healthcare Worker Burnout: Organizational Factors Show Highest Centrality.

Inquiry : a journal of medical care organization, provision and financing·2026
Same journal

Corrigendum to: "A Systematic Review on the Effectiveness of the Cure Violence Approach".

Inquiry : a journal of medical care organization, provision and financing·2026
Same journal

Nurse Influencers: The Digital Performance of Care, Professional Identity, and the Commodification of Empathy-An Integrative Narrative Review.

Inquiry : a journal of medical care organization, provision and financing·2026
Same journal

Digital Access and Primary Care Use in Chronic Disease Management: Preliminary Evidence From Three Caribbean Cities in Colombia.

Inquiry : a journal of medical care organization, provision and financing·2026
See all related articles

Related Experiment Video

Updated: May 19, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

How should risk adjustment data be collected?

Daniel P Kessler1

  • 1Law School and Graduate School of Business, Stanford University, Stanford, CA 94305, USA. fkessler@stanford.edu

Inquiry : a Journal of Medical Care Organization, Provision and Financing
|August 31, 2012
PubMed
Summary
This summary is machine-generated.

This study supports distributed data collection for health insurance risk adjustment, showing it performs as well as or better than centralized methods. This approach helps calibrate models and calculate payments while supporting ACA goals.

More Related Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Related Experiment Videos

Last Updated: May 19, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

Area of Science:

  • Health economics
  • Health services research
  • Data management

Background:

  • Risk adjustment is crucial for the Patient Protection and Affordable Care Act (ACA).
  • Limited guidance exists on optimal data collection methods for risk adjustment.
  • Current practices often involve centralized data submission, raising privacy and efficiency concerns.

Purpose of the Study:

  • To provide analytical support for a distributed data collection approach in risk adjustment.
  • To compare the efficacy of distributed versus centralized data models for risk adjustment.
  • To demonstrate how distributed methods can support ACA objectives and broader data aggregation needs.

Main Methods:

  • Analytical modeling of distributed data approaches.
  • Theoretical examination of distributed data analysis for model calibration and payment calculation.
  • Practical application insights drawn from existing distributed models in other domains.

Main Results:

  • Distributed approaches are analytically sound and perform comparably to or exceed centralized methods in achieving risk adjustment goals.
  • Distributed data analysis effectively calibrates risk adjustment models and calculates payments.
  • Distributed methods enhance data privacy and security by allowing insurers to retain claims data.

Conclusions:

  • Distributed data collection methods offer a viable and effective alternative for implementing risk adjustment programs.
  • States should consider adopting distributed methods for their risk adjustment initiatives.
  • Distributed approaches align with and support the broader objectives of the ACA, including data security and efficient program operation.