| Title |
Transforming Healthcare Delivery: Integrating Dynamic Simulation Modelling and Big Data in Health Economics and Outcomes Research
|
|---|---|
| Published in |
PharmacoEconomics, October 2015
|
| DOI | 10.1007/s40273-015-0330-7 |
| Pubmed ID | |
| Authors |
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, Nathaniel D. Osgood |
| 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. |
X Demographics
The data shown below were collected from the profiles of 14 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 29% |
| France | 3 | 21% |
| Netherlands | 1 | 7% |
| Australia | 1 | 7% |
| Colombia | 1 | 7% |
| New Zealand | 1 | 7% |
| Unknown | 3 | 21% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 64% |
| Scientists | 4 | 29% |
| Science communicators (journalists, bloggers, editors) | 1 | 7% |
Mendeley readers
The data shown below were compiled from readership statistics for 175 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Sweden | 2 | 1% |
| United States | 2 | 1% |
| United Kingdom | 1 | <1% |
| Unknown | 170 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 35 | 20% |
| Researcher | 26 | 15% |
| Student > Master | 22 | 13% |
| Student > Bachelor | 17 | 10% |
| Other | 10 | 6% |
| Other | 33 | 19% |
| Unknown | 32 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 29 | 17% |
| Computer Science | 19 | 11% |
| Engineering | 18 | 10% |
| Economics, Econometrics and Finance | 13 | 7% |
| Social Sciences | 12 | 7% |
| Other | 44 | 25% |
| Unknown | 40 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 28. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 26 April 2017.
All research outputs
#1,294,739
of 24,224,854 outputs
Outputs from PharmacoEconomics
#48
of 1,949 outputs
Outputs of similar age
#19,844
of 289,425 outputs
Outputs of similar age from PharmacoEconomics
#1
of 22 outputs
Altmetric has tracked 24,224,854 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,949 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one has done particularly well, scoring higher than 97% of its peers.
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We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.