Scott McLachlan

1.3k citations
32 papers · 614 · h-index 12

Impact in

Papers in

Scott McLachlan

31 papers receiving 593 citations

Peers

Scott McLachlan
Comparison fields: 5 of 109
  • Health Informatics 38
  • Health Information Management 74
  • Artificial Intelligence 195
  • Modeling and Simulation 22
  • General Decision Sciences 8
Replace Arash Shaban‐Nejad with:
Arash Shaban‐Nejad United States
Enea Parimbelli Italy
Carlos Luís Parra-Calderón Spain
Prem Timsina United States
Harshana Liyanage United Kingdom
Mostafa Langarizadeh Iran
Ali Garavand Iran
Hadi Kazemi-Arpanahi Iran
Danielle L. Mowery United States
Tiffany J. Callahan United States
Scott McLachlan relative to Arash Shaban‐Nejad United States Arash Shaban‐Nejad's profile →
Citations per field
00.5×4.1×
Arash Shaban‐Nejad · 1×
Citations per year

Countries citing papers authored by Scott McLachlan

Since Specialization
Citations

This map shows the geographic impact of Scott McLachlan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Scott McLachlan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Scott McLachlan more than expected).

Fields of papers citing papers by Scott McLachlan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Scott McLachlan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Scott McLachlan. The network helps show where Scott McLachlan may publish in the future.

Co-authors

The 25 scholars most cited alongside Scott McLachlan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Scott McLachlan Line = papers co-authored together Scott McLachlan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 32 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017224
2 202047
3 201838
4 202133
5 202130
6 201629
7 202029
8 201928
9 202025
10 201325
11 202019
12 202018
13 20198
14 20198
15 20197
16 20206
17 20185
18 20184
19 20194
20 20204

About Scott McLachlan

Scott McLachlan is a scholar working on Health Information Management, Artificial Intelligence, Public Health, Environmental and Occupational Health, General Health Professions and Molecular Biology, having authored 32 papers that have together received 614 indexed citations. Recurring topics across this work include Electronic Health Records Systems (6 papers), Machine Learning in Healthcare (5 papers), Bayesian Modeling and Causal Inference (4 papers), Clinical practice guidelines implementation (4 papers), Artificial Intelligence in Healthcare (3 papers), Biomedical Text Mining and Ontologies (3 papers), Global Health Workforce Issues (2 papers) and Primary Care and Health Outcomes (2 papers). The work is most often cited by research in Health Informatics (38 citations), Health Information Management (74 citations), Artificial Intelligence (195 citations), Modeling and Simulation (22 citations) and General Decision Sciences (8 citations). Scott McLachlan has collaborated with scholars based in United Kingdom, New Zealand and United States. Frequent co-authors include Kudakwashe Dube, Norman Fenton, Thomas Gallagher, Martin Neil, Jason Walonoski, Dylan Hall, M Krámer, Joseph C. Nichols, Andre Quina and Magda Osman. Their work appears in journals such as International Journal of Integrated Care, Journal of Risk Research, Artificial Intelligence in Medicine, Journal of the American Medical Informatics Association and Journal of Biomedical Informatics.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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