Subfield · Health Information Management
Most cited papers in Health Information Management
What are the most cited papers in Health Information Management?
The most cited paper in Health Information Management is "An introduction to ROC analysis" (Tom E. Fawcett, 2005), with 22,014 citations. Next come "Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data" (10,878) and "Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases" (10,573).
Showing 50 of 99 papers. Every paper with more than 1,446 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | An introduction to ROC analysis Tom E. Fawcett · Pattern Recognition Letters | 22 Dec 2005 | 22,014 |
| 2 | Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data Hude Quan, Vijaya Sundararajan, Patricia Halfon et al. · Medical Care | 13 Oct 2005 | 10,878 |
| 3 | Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases Richard A. Deyo · Journal of Clinical Epidemiology | Jun 1992 | 10,573 |
| 4 | Comorbidity Measures for Use with Administrative Data Anne Elixhauser, Claudia Steiner, Donald Robert Harris et al. · Medical Care | 1998 | 10,043 |
| 5 | Measuring individual differences in empathy: Evidence for a multidimensional approach. Mark H. Davis · Journal of Personality and Social Psychology | 1983 | 9,786 |
| 6 | Prediction of Coronary Heart Disease Using Risk Factor Categories Peter W.F. Wilson, Ralph B. D’Agostino, Daniel Levy et al. · Circulation | 12 May 1998 | 9,689 |
| 7 | The use of the area under the ROC curve in the evaluation of machine learning algorithms Andrew P. Bradley · Pattern Recognition | Jul 1997 | 7,349 |
| 8 | Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond Michael J. Pencina, Ralph B. D' Agostino, Ralph B. D' Agostino et al. · Statistics in Medicine | 13 Jun 2007 | 6,517 |
| 9 | Updating and Validating the Charlson Comorbidity Index and Score for Risk Adjustment in Hospital Discharge Abstracts Using Data From 6 Countries Hude Quan, Bing Li, Chantal Marie Couris et al. · American Journal of Epidemiology | 17 Feb 2011 | 6,044 |
| 10 | Audit and feedback: effects on professional practice and healthcare outcomes Noah Michael Ivers, Gro Jamtvedt, Signe Agnes Flottorp et al. · Cochrane Database of Systematic Reviews | 8 Jun 2012 | 5,554 |
| 11 | External review and validation of the Swedish national inpatient register Jonas Filip Ludvigsson, Eva Andersson, Anders Ekbom et al. · BMC Public Health | 9 Jun 2011 | 5,148 |
| 12 | Machine Learning in Medicine Alvin Rajkomar, Jay B. Dean, Isaac S. Kohane · New England Journal of Medicine | 3 Apr 2019 | 4,470 |
| 13 | Statistical methods for assessing agreement between two methods of clinical measurement John M. Bland, Douglas G. Altman · International Journal of Nursing Studies | May 2010 | 4,016 |
| 14 | Predicting the Future — Big Data, Machine Learning, and Clinical Medicine Ziad Obermeyer, Ezekiel Emanuel · New England Journal of Medicine | 28 Sep 2016 | 3,703 |
| 15 | Machine Learning in Medicine Rahul C. Deo · Circulation | 16 Nov 2015 | 3,562 |
| 16 | Machine learning applications in cancer prognosis and prediction Konstantina D. Kourou, Themis P. Exarchos, Konstantinos Exarchos et al. · Computational and Structural Biotechnology Journal | 15 Nov 2014 | 3,415 |
| 17 | Systematic Review: Impact of Health Information Technology on Quality, Efficiency, and Costs of Medical Care Basit Iqbal Chaudhry, Jerome Wang, Shinyi Wu et al. · Annals of Internal Medicine | 16 May 2006 | 3,188 |
| 18 | New Patterns of Management. Amitai Etzioni, Rensis Likert · American Sociological Review | Oct 1962 | 3,153 |
| 19 | Deep learning for healthcare: review, opportunities and challenges Riccardo Miotto, Fei Wang, Shuang Wang et al. · Briefings in Bioinformatics | 5 Apr 2017 | 3,149 |
| 20 | Revolutionizing healthcare: the role of artificial intelligence in clinical practice Shuroug A. Alowais, Sahar S. Alghamdi, Nada Alsuhebany et al. · BMC Medical Education | 22 Sep 2023 | 3,123 |
| 21 | An overview of clinical decision support systems: benefits, risks, and strategies for success Reed Taylor Sutton, David Pincock, Daniel C. Baumgart et al. · npj Digital Medicine | 6 Feb 2020 | 3,082 |
| 22 | Big data analytics in healthcare: promise and potential Wullianallur Raghupathi, Viju Raghupathi · Health Information Science and Systems | 7 Feb 2014 | 3,077 |
| 23 | Beyond Adoption: A New Framework for Theorizing and Evaluating Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies Trisha Greenhalgh, Joseph P. Wherton, Chrysanthi Papoutsi et al. · Journal of Medical Internet Research | Nov 2017 | 3,034 |
| 24 | Effects of Computerized Clinical Decision Support Systems on Practitioner Performance and Patient Outcomes Amit X. Garg, Neill K. J. Adhikari, Heather McDonald et al. · JAMA | 8 Mar 2005 | 2,988 |
| 25 | SQUIRE 2.0 ( Standards for QUality Improvement Reporting Excellence) : revised publication guidelines from a detailed consensus process Greg S. Ogrinc, Louise Davies, Daisy Goodman et al. · BMJ Quality & Safety | 14 Sep 2015 | 2,965 |
| 26 | AI in health and medicine Pranav Rajpurkar, Emma Chen, Oishi Banerjee et al. · Nature Medicine | 2022 | 2,914 |
| 27 | Artificial intelligence in healthcare Kun‐Hsing Yu, Andrew L. Beam, Isaac S. Kohane · Nature Biomedical Engineering | 5 Oct 2018 | 2,799 |
| 28 | Data Resource Profile: Clinical Practice Research Datalink (CPRD) Emily L. Herrett, Arlene M. Gallagher, Krishnan Bhaskaran et al. · International Journal of Epidemiology | Jun 2015 | 2,787 |
| 29 | Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success Kensaku Kawamoto, Caitlin A Houlihan, E. Andrew Balas et al. · BMJ | 14 Mar 2005 | 2,774 |
| 30 | Critical appraisal skills programme Jatinder Singh · Journal of Pharmacology and Pharmacotherapeutics | 22 Feb 2013 | 2,626 |
| 31 | Scalable and accurate deep learning with electronic health records Alvin Rajkomar et al. · npj Digital Medicine | 4 May 2018 | 2,621 |
| 32 | ESPEN guidelines on definitions and terminology of clinical nutrition Tommy Cederholm et al. · Clinical Nutrition | 15 Sep 2016 | 2,569 |
| 33 | Mobile App Rating Scale: A New Tool for Assessing the Quality of Health Mobile Apps Stoyan R. Stoyanov, Leanne M. Hides, David John Kavanagh et al. · JMIR mhealth and uhealth | 11 Mar 2015 | 2,525 |
| 34 | New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality Vijaya Sundararajan, Toni Henderson, Catherine Perry et al. · Journal of Clinical Epidemiology | Dec 2004 | 2,519 |
| 35 | Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes Daniel Shu Wei Ting et al. · JAMA | 12 Dec 2017 | 2,445 |
| 36 | The Quality in Australian Health Care Study Ross McL Wilson, William B. Runciman, Robert W Gibberd et al. · The Medical Journal of Australia | Nov 1995 | 2,401 |
| 37 | A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems David J. Hand, Robert John Till · Machine Learning | Nov 2001 | 2,311 |
| 38 | Recurrent Neural Networks for Multivariate Time Series with Missing Values Zhengping Che, Sanjay Purushotham, Kyunghyun Cho et al. · Scientific Reports | 11 Apr 2018 | 2,266 |
| 39 | Logistic regression and artificial neural network classification models: a methodology review Stephan Dreiseitl, Lucila Ohno‐Machado · Journal of Biomedical Informatics | Oct 2002 | 2,205 |
| 40 | Dual coding theory: Retrospect and current status. Allan Paivio · Canadian Journal of Psychology/Revue Canadienne de Psychologie | Sep 1991 | 2,189 |
| 41 | Narrative Medicine Rita Charon · JAMA | 17 Oct 2001 | 2,186 |
| 42 | Role of Computerized Physician Order Entry Systems in Facilitating Medication Errors Ross Koppel · JAMA | 8 Mar 2005 | 2,145 |
| 43 | Effect of Computerized Physician Order Entry and a Team Intervention on Prevention of Serious Medication Errors David Westfall Bates · JAMA | 21 Oct 1998 | 2,111 |
| 44 | Deep Learning for Health Informatics Daniele Ravì, Charence Wong, Fani Deligianni et al. · IEEE Journal of Biomedical and Health Informatics | 29 Dec 2016 | 2,025 |
| 45 | Medication Errors and Adverse Drug Events in Pediatric Inpatients Rainu Kaushal · JAMA | 25 Apr 2001 | 1,950 |
| 46 | Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques Senthilkumar Mohan, Chandrasegar Thirumalai, Gautam Srivastava · IEEE Access | 2019 | 1,947 |
| 47 | The future vision of simulation in health care David M. Gaba · BMJ Quality & Safety | Oct 2004 | 1,937 |
| 48 | Systematic review of the application of the plan–do–study–act method to improve quality in healthcare Michael J. Taylor, Chris McNicholas, Chris Nicolay et al. · BMJ Quality & Safety | 11 Sep 2013 | 1,902 |
| 49 | Classification Based on Decision Tree Algorithm for Machine Learning Bahzad Charbuty, Adnan Mohsin Abdulazeez · Journal of Applied Science and Technology Trends | 24 Mar 2021 | 1,879 |
| 50 | Development of a comorbidity index using physician claims data Carrie N. Klabunde, Arnold L. Potosky, Julie M. Legler et al. · Journal of Clinical Epidemiology | Dec 2000 | 1,867 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.