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Related Concept Videos

Integrated Healthcare System01:20

Integrated Healthcare System

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An integrated healthcare system (IHS) is a set of organizations that provides for or arranges to provide coordinated and continuous service to a defined population. The IHS takes responsibility for that particular population's health status and outcome, both clinically and fiscally. An integrated healthcare system is a well-organized, well-coordinated, and collaborative network. The integrated delivery system is a network that connects different healthcare providers to deliver organized,...
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Methods Of Healthcare Delivery System01:26

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Substitution Rule Applied to Indefinite Integrals01:27

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When a force is applied to a linear spring, the restoring force increases proportionally with the amount of displacement. This behavior is described by Hooke’s law, which allows the work done on the spring to be determined directly from the force–displacement relationship. In this case, the force varies in a simple and predictable manner, making the calculation relatively simple.On the other hand, a nonlinear spring does not obey Hooke’s law. Its restoring force depends on...
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Substitution Rule Applied to Definite Integrals01:24

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When evaluating a definite integral whose integrand matches the structure of a composite function, the substitution method provides an efficient way to simplify the calculation. This method is based on reversing the chain rule from differentiation, allowing a complicated expression to be rewritten in a simpler form. When the integrand contains an inner function and its derivative, substitution naturally reduces the complexity of the problem.The core idea of substitution for definite integrals...
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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...
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Related Experiment Video

Updated: Jan 30, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Identifying Patients With Relapsing-Remitting Multiple Sclerosis Using Algorithms Applied to US Integrated Delivery

Hoa Van Le1, Chi Thi Le Truong2, Aaron W C Kamauu3

  • 1PAREXEL Int., Durham, NC, USA.

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research
|January 22, 2019
PubMed
Summary
This summary is machine-generated.

Validated algorithms accurately identify patients with relapsing-remitting multiple sclerosis (RRMS) using electronic health records and claims data. This enables confident real-world clinical research for RRMS patient cohorts.

Keywords:
algorithmclaimselectronic health recordsmultiple sclerosisrelapsing-remitting multiple sclerosis

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Area of Science:

  • Clinical Informatics
  • Epidemiology
  • Neurology

Background:

  • Relapsing-remitting multiple sclerosis (RRMS) significantly impacts patients, necessitating improved understanding through real-world evidence.
  • Accurate identification of RRMS patients in real-world data is crucial for clinical research and understanding disease burden.

Purpose of the Study:

  • To develop and validate algorithms for identifying patients with RRMS.
  • To utilize both unstructured clinical notes from electronic health records (EHRs) and structured healthcare claims data for patient identification.

Main Methods:

  • Algorithms were developed to search EHR clinical notes and claims data for adult patients meeting RRMS criteria, excluding progressive MS.
  • A cohort of possible multiple sclerosis (MS) patients was established from US Integrated Delivery Network data (2010-2014).
  • Manual medical record review was performed for algorithm validation, calculating positive predictive values.

Main Results:

  • EHR clinical notes-based algorithms identified 837 RRMS patients with a positive predictive value of 99.1%.
  • Claims-based algorithms identified 2271 RRMS patients with positive predictive values ranging from 94.6% to 94.9%.
  • 779 patients were identified by both algorithm types.

Conclusions:

  • The developed algorithms reliably identify real-world cohorts of RRMS patients.
  • These validated algorithms enable confident clinical research on RRMS populations, excluding those with progressive MS.