Evidence map›Paper›PMID 26497003›Full record

ArticlePharmacoEconomics2016

Transforming Healthcare Delivery: Integrating Dynamic Simulation Modelling and Big Data in Health Economics and Outcomes Research.

Deborah A Marshall, Lina Burgos-Liz, Kalyan S Pasupathy, William V Padula, Maarten J IJzerman, Peter K Wong, Mitchell K Higashi, Jordan Engbers, Samuel Wiebe, William Crown and 1 more

Abstract read
PubMed Publisher
In one paragraph

Article in PharmacoEconomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Healthcare Quality Improvement: The Need for a Macro-Systems Approach.Annual IEEE Systems Conference. IEEE Systems Conference · 2022
    Article
  5. Article
  6. The Grand Challenges of Medical Technology.Frontiers in medical technology · 2020
    Article
  7. Review
  8. Article
  9. "What Goes Around Comes Around": Lessons Learned from Economic Evaluations of Personalized Medicine Applied to Digital Medicine.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2017
    Article
  10. Article
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Deborah A MarshallDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Room 3C56 Health Research Innovation Centre, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada. damarsha@ucalgary.ca.
Lina Burgos-LizDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, Room 3C58 Health Research Innovation Centre, 3280 Hospital Drive NW, Calgary, AB, T2N 4Z6, Canada.
Kalyan S PasupathyClinical Engineering Learning Lab, Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Rochester, MN, USA.
William V PadulaDepartment of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
Maarten J IJzermanDepartment of Health Technology and Services Research, University of Twente, Enschede, The Netherlands.
Peter K WongIllinois Divisions and HSHS Medical Group, Hospital Sisters Health System (HSHS), Belleville, IL, USA.
Mitchell K HigashiGE Healthcare, Barrington, IL, USA.
Jordan EngbersClinical Research Unit, University of Calgary, Calgary, AB, Canada.
Samuel WiebeClinical Research Unit, University of Calgary, Calgary, AB, Canada.
William CrownOptum Labs, Boston, MA, USA.
Nathaniel D OsgoodDepartment of Computer Science, University of Saskatchewan, Saskatoon, Canada.

Funding

Using EHR Data to Dynamically Target Patients for Braden Scale Risk-AssessmentF32HS023710 · AHRQ · UNIVERSITY OF CHICAGO · PI PADULA, WILLIAM VINCENT · 2014 to 2014
$68k
AHRQ HHS 1 F32 HS023710-01).
6 · The paper itself

Abstract

In the era of the Information Age and personalized medicine, healthcare delivery systems need to be efficient and patient-centred. The health system must be responsive to individual patient choices and preferences about their care, while considering the system consequences. While dynamic simulation modelling (DSM) and big data share characteristics, they present distinct and complementary value in healthcare. Big data and DSM are synergistic-big data offer support to enhance the application of dynamic models, but DSM also can greatly enhance the value conferred by big data. Big data can inform patient-centred care with its high velocity, volume, and variety (the three Vs) over traditional data analytics; however, big data are not sufficient to extract meaningful insights to inform approaches to improve healthcare delivery. DSM can serve as a natural bridge between the wealth of evidence offered by big data and informed decision making as a means of faster, deeper, more consistent learning from that evidence. We discuss the synergies between big data and DSM, practical considerations and challenges, and how integrating big data and DSM can be useful to decision makers to address complex, systemic health economics and outcomes questions and to transform healthcare delivery.

Indexed as

Economics, MedicalModels, TheoreticalComputer SimulationDecision MakingDelivery of Health CareHumansOutcome Assessment, Health CarePatient-Centered CarePrecision Medicine

Identifiers

PMID26497003

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.