Published since January 22, 2014

Diagnosis

Official Journal of the Society to Improve Diagnosis in Medicine (SIDM)

ISSN: 2194-802X

Editors-in-chief: Mark L. Graber, Mario Plebani

Submit manuscript

About this journal

Objective
Diagnosis is a subscription journal, which focuses on how diagnosis can be advanced, how it is taught, and how and why it can fail, leading to diagnostic errors. The journal welcomes both fundamental and applied works, improvement initiatives, opinions, and debates to encourage new thinking on improving this critical aspect of healthcare quality. Diagnosis is the official journal of the Society to Improve Diagnosis in Medicine.

Please submit your manuscript here

Topics

Factors that promote diagnostic quality and safety
Clinical reasoning
Diagnostic errors in medicine
The factors that contribute to diagnostic error: human factors, cognitive issues, and system-related breakdowns
Improving the value of diagnosis – eliminating waste and unnecessary testing
How culture and removing blame promote awareness of diagnostic errors
Training and education related to clinical reasoning and diagnostic skills
Advances in laboratory testing and imaging that improve diagnostic capability
Local, national and international initiatives to reduce diagnostic error

Article formats

Research Reports – Reports of original research
Short Communications – Reports of early research results and pilot studies
Reviews – Systematic, narrative, and focused reviews. Review articles are normally published by invitation, but suggestions to the Editors are welcome
Opinion Papers and Editorials
Letters to the Editor
Point/Counterpoint Papers
Guidelines and Recommendations
Innovations in diagnostic testing – Advances in laboratory testing or diagnostic imaging are appropriate subjects, or evaluations of recent innovations
Case Reports – Learning from Tragedy – Case reports of diagnostic error or dilemma discussed from a multi-stakeholder perspective; should include the facts of the case, a discussion focused on a root cause analysis, take-away points or action items resulting from the analysis, and whenever possible input from both the affected patients and their providers
Case Reports – Lessons in Clinical Reasoning: Pitfalls, Myths, and Pearls – Case reports in which a clinician discusses their diagnostic approach after clinical information is presented, focusing on a case in which a diagnostic error (or near miss = great catch) occurred
Patient-Focused Articles – Papers in this category should be written in lay language on a topic of interest to the patient stakeholder community
Diagnosis in the News – Brief reports of news-worthy advances in diagnosis, or problems involving diagnostic error

Your Benefits

Indexed in PubMed (all articles since 2014) and other abstracting and indexing services
Peer-reviewed and led by a multi-institutional editorial board
Focused on how the diagnostic process can be advanced to reduce diagnostic errors
Cutting-edge and authoritative contributions
Academic and clinical aspects of quality and safety in healthcare combination
International readership and global reach

History

founded in 2014
volumes 1 and 2 are Open Access

Other publications in the field

Abstracting and Indexing

Diagnosis is covered by the following services:

Baidu Scholar
Cabells Journalytics
Case
Chemical Abstracts Service (CAS) - CAplus
Chemical Abstracts Service (CAS) - SciFinder
CNKI Scholar (China National Knowledge Infrastructure)
CNPIEC - cnpLINKer
Dimensions
EBSCO (relevant databases)
EBSCO Discovery Service
EMBASE
EMCare
Google Scholar
Japan Science and Technology Agency (JST)
J-Gate
JournalTOCs
KESLI-NDSL (Korean National Discovery for Science Leaders)
Medline
MyScienceWork
Naver Academic
Naviga (Softweco)
Primo Central (ExLibris)
Publons
PubMed
QOAM (Quality Open Access Market)
ReadCube
Reaxys
SCImago (SJR)
SCOPUS
Semantic Scholar
Sherpa/RoMEO
Summon (ProQuest)
TDNet
Text Mining
Ulrich's Periodicals Directory/ulrichsweb
WanFang Data
Web of Science - Emerging Sources Citation Index
WorldCat (OCLC)
X-MOL
Yewno Discover

Supplementary Materials

Topics

External Links

Volume 10 Issue 1

February 2023

Publicly Available February 17, 2023

Frontmatter

Page range: i-ii

Download PDF

Editorial

Publicly Available February 1, 2023

The future of diagnosis – where are we going?

Verity Schaye, Andrew S. Parsons, Mark L. Graber, Andrew P. J. Olson

Page range: 1-3

Download PDF

Articles

Publicly Available August 22, 2022

Improving diagnosis: adding context to cognition

Mark Linzer, Erin E. Sullivan, Andrew P. J. Olson, Maram Khazen, Maria Mirica, Gordon D. Schiff

Page range: 4-8

More Download PDF

Abstract

Background The environment in which clinicians provide care and think about their patients is a crucial and undervalued component of the diagnostic process. Content In this paper, we propose a new conceptual model that links work conditions to clinician responses such as stress and burnout, which in turn impacts the quality of the diagnostic process and finally patient diagnostic outcomes. The mechanism for these interactions critically depends on the relationship between working memory (WM) and long-term memory (LTM), and ways WM and LTM interactions are affected by working conditions. Summary We propose a conceptual model to guide interventions to improve work conditions, clinician reactions and ultimately diagnostic process, accuracy and outcomes. Outlook Improving diagnosis can be accomplished if we are able to understand, measure and increase our knowledge of the context of care.

Publicly Available December 1, 2022

The cognitive apprenticeship: advancing reasoning education by thinking aloud

Anand D. Jagannath, Jessica J. Dreicer, John C. Penner, Gurpreet Dhaliwal

Page range: 9-12

More Download PDF

Abstract

Teaching clinical reasoning has long challenged educators because it requires familiarity with reasoning concepts, experience with describing thinking, and comfort with exposing uncertainty and error. We propose that teachers adopt the cognitive apprenticeship model and a method of disclosing uncertainty known as intellectual streaking. These approaches reflect a shift in the educator’s mindset from transmitting medical knowledge to broadcasting cognition. We provide several examples to guide the adoption of these strategies and make recommendations for teachers and training programs to improve the teaching of clinical reasoning.

Publicly Available September 12, 2022

Teaching the science of uncertainty

Glenn Moulder, Emily Harris, Lekshmi Santhosh

Page range: 13-18

More Download PDF

Abstract

As we increasingly acknowledge the ubiquitous nature of uncertainty in clinical practice (Meyer AN, Giardina TD, Khawaja L, Singh H. Patient and clinician experiences of uncertainty in the diagnostic process: current understanding and future directions. Patient Educ Counsel 2021;104:2606–15; Han PK, Klein WM, Arora NK. Varieties of uncertainty in health care: a conceptual taxonomy. Med Decis Making 2011;31:828–38) and strive to better define this entity (Lee C, Hall K, Anakin M, Pinnock R. Towards a new understanding of uncertainty in medical education. J Eval Clin Pract 2020; Bhise V, Rajan SS, Sittig DF, Morgan RO, Chaudhary P, Singh H. Defining and measuring diagnostic uncertainty in medicine: a systematic review. J Gen Intern Med 2018;33:103–15), as educators we should also design, implement, and evaluate curricula addressing clinical uncertainty. Although frequently encountered, uncertainty is often implicitly referred to rather than explicitly discussed (Gärtner J, Berberat PO, Kadmon M, Harendza S. Implicit expression of uncertainty - suggestion of an empirically derived framework. BMC Med Educ 2020;20:83). Increasing explicit discussion of – and comfort with -uncertainty has the potential to improve diagnostic reasoning and accuracy and improve patient care (Dunlop M, Schwartzstein RM. Reducing diagnostic error in the intensive care unit. Engaging. Uncertainty when teaching clinical reasoning. Scholar;1:364–71). Discussion of both diagnostic and prognostic uncertainty with patients is central to shared decision-making in many contexts as well, (Simpkin AL, Armstrong KA. Communicating uncertainty: a narrative review and framework for future research. J Gen Intern Med 2019;34:2586–91) from the outpatient setting to the inpatient setting, and from undergraduate medical education (UME) trainees to graduate medical education (GME) trainees. In this article, we will explore the current status of how the science of uncertainty is taught from the UME curriculum to the GME curriculum, and describe strategies how uncertainty can be explicitly discussed for all levels of trainees.

Publicly Available November 23, 2022

The future of teaching management reasoning: important questions and potential solutions

Emily A. Abdoler, Andrew S. Parsons, Thilan P. Wijesekera

Page range: 19-23

More Download PDF

Abstract

Management reasoning is distinct from but inextricably linked to diagnostic reasoning in the iterative process that is clinical reasoning. Complex and situated, management reasoning skills are distinct from diagnostic reasoning skills and must be developed in order to promote cogent clinical decisions. While there is growing interest in teaching management reasoning, key educational questions remain regarding when it should be taught, how it can best be taught in the clinical setting, and how it can be taught in a way that helps mitigate implicit bias. Here, we describe several useful tools to structure teaching of management reasoning across learner levels and educational settings. The management script provides a scaffold for organizing knowledge around management and can serve as a springboard for discussion of uncertainty, thresholds, high-value care, and shared decision-making. The management pause reserves space for management discussions and exploration of a learner’s reasoning. Finally, the equity reflection invites learners to examine management decisions from a health equity perspective, promoting the practice of metacognition around implicit bias. These tools are easily deployable, and – when used regularly – foster a learning environment primed for the successful teaching of management reasoning.

Publicly Available December 8, 2022

Self-regulated learning and the future of diagnostic reasoning education

Alexander Goldowsky, Joseph Rencic

Page range: 24-30

More Download PDF

Abstract

Diagnostic reasoning is a foundational ability of health professionals. The goal of enhancing clinical reasoning education is improved diagnostic accuracy and reduced diagnostic error. In order to do so, health professions educators need not only help learners improve their clinical reasoning, but teach them how to develop expert performance. An evidence-based learning strategy that is strongly associated with expert performance is self-regulated learning (SRL). SRL is the modulation of “self-generated thoughts, feelings, and actions that are planned and cyclically adapted to the attainment of personal goals”. At this time, there is little data on the use of SRL to improve diagnostic reasoning. However, there appear to be numerous opportunities to utilize SRL in novel ways to improve diagnostic reasoning given what is already known about this competency. Examples that are discussed include the role SRL can play in simulation, clinical experiences, assessment, and novel technologies such as virtual reality, artificial intelligence, and machine learning. SRL is central to the philosophy that health professionals are life-long learners, as it teaches learners “how to learn”. SRL has the potential to help achieve the goal of improved diagnostic accuracy and reduced diagnostic error.

Publicly Available November 16, 2022

Imagining the future of diagnostic performance feedback

Benjamin I. Rosner, Laura Zwaan, Andrew P.J. Olson

Page range: 31-37

More Download PDF

Abstract

Diagnostic performance is uniquely challenging to measure, and providing feedback on diagnostic performance to catalyze diagnostic recalibration remains the exception to the rule in healthcare. Diagnostic accuracy, timeliness, and explanation to the patient are essential dimensions of diagnostic performance that each intersect with a variety of technical, contextual, cultural, and policy barriers. Setting aside assumptions about current constraints, we explore the future of diagnostic performance feedback by describing the “minimum viable products” and the “ideal state” solutions that can be envisioned for each of several important barriers. Only through deliberate and iterative approaches to breaking down these barriers can we improve recalibration and continuously drive the healthcare ecosystem towards diagnostic excellence.

Open Access August 25, 2022

Improving diagnostic decision support through deliberate reflection: a proposal

Henk G. Schmidt, Sílvia Mamede

Page range: 38-42

More Download PDF

Abstract

Digital decision support (DDS) is expected to play an important role in improving a physician’s diagnostic performance and reducing the burden of diagnostic error. Studies with currently available DDS systems indicate that they lead to modest gains in diagnostic accuracy, and these systems are expected to evolve to become more effective and user-friendly in the future. In this position paper, we propose that a way towards this future is to rethink DDS systems based on deliberate reflection, a strategy by which physicians systematically review the clinical findings observed in a patient in the light of an initial diagnosis. Deliberate reflection has been demonstrated to improve diagnostic accuracy in several contexts. In this paper, we first describe the deliberate reflection strategy, including the crucial element that would make it useful in the interaction with a DDS system. We examine the nature of conventional DDS systems and their shortcomings. Finally, we propose what DDS based on deliberate reflection might look like, and consider why it would overcome downsides of conventional DDS.

Publicly Available September 21, 2022

A clinically-guided unsupervised clustering approach to recommend symptoms of disease associated with diagnostic opportunities

Aaron C. Miller, Alan T. Arakkal, Scott H. Koeneman, Joseph E. Cavanaugh, Philip M. Polgreen

Page range: 43-53

More Download PDF

Abstract

Objectives A first step in studying diagnostic delays is to select the signs, symptoms and alternative diseases that represent missed diagnostic opportunities. Because this step is labor intensive requiring exhaustive literature reviews, we developed machine learning approaches to mine administrative data sources and recommend conditions for consideration. We propose a methodological approach to find diagnostic codes that exhibit known patterns of diagnostic delays and apply this to the diseases of tuberculosis and appendicitis. Methods We used the IBM MarketScan Research Databases, and consider the initial symptoms of cough before tuberculosis and abdominal pain before appendicitis. We analyze diagnosis codes during healthcare visits before the index diagnosis, and use k-means clustering to recommend conditions that exhibit similar trends to the initial symptoms provided. We evaluate the clinical plausibility of the recommended conditions and the corresponding number of possible diagnostic delays based on these diseases. Results For both diseases of interest, the clustering approach suggested a large number of clinically-plausible conditions to consider (e.g., fever, hemoptysis, and pneumonia before tuberculosis). The recommended conditions had a high degree of precision in terms of clinical plausibility: >70% for tuberculosis and >90% for appendicitis. Including these additional clinically-plausible conditions resulted in more than twice the number of possible diagnostic delays identified. Conclusions Our approach can mine administrative datasets to detect patterns of diagnostic delay and help investigators avoid under-identifying potential missed diagnostic opportunities. In addition, the methods we describe can be used to discover less-common presentations of diseases that are frequently misdiagnosed.

Open Access November 21, 2022

“Cephalgia” or “migraine”? Solving the headache of assessing clinical reasoning using natural language processing

Christopher R. Runyon, Polina Harik, Michael A. Barone

Page range: 54-60

More Download PDF

Abstract

In this op-ed, we discuss the advantages of leveraging natural language processing (NLP) in the assessment of clinical reasoning. Clinical reasoning is a complex competency that cannot be easily assessed using multiple-choice questions. Constructed-response assessments can more directly measure important aspects of a learner’s clinical reasoning ability, but substantial resources are necessary for their use. We provide an overview of INCITE, the Intelligent Clinical Text Evaluator, a scalable NLP-based computer-assisted scoring system that was developed to measure clinical reasoning ability as assessed in the written documentation portion of the now-discontinued USMLE Step 2 Clinical Skills examination. We provide the rationale for building a computer-assisted scoring system that is aligned with the intended use of an assessment. We show how INCITE’s NLP pipeline was designed with transparency and interpretability in mind, so that every score produced by the computer-assisted system could be traced back to the text segment it evaluated. We next suggest that, as a consequence of INCITE’s transparency and interpretability features, the system may easily be repurposed for formative assessment of clinical reasoning. Finally, we provide the reader with the resources to consider in building their own NLP-based assessment tools.

Ahead of Print / Just Accepted

Ahead of Print / Just Accepted

Volume 10 (2023)

Issue 1 Special issue: The Future of Diagnosis; Issue Editors: Mark Graber, Ava L. Liberman, Andrew Olson, Andrew S. Parsons, Verity Schaye

Volume 9 (2022)

Volume 8 (2021)

Volume 7 (2020)

Volume 6 (2019)

Volume 5 (2018)

Volume 4 (2017)

Volume 3 (2016)

Volume 2 (2015)

Volume 1 (2014)

Cite Score	4.6
Journal Citation Indicator	1.20
SCImago Journal Rank	0.817
Source Normalized Impact per Paper	1.261

Submit

Submission
You can easily submit your manuscript online. Simply go to http://mc.manuscriptcentral.com/diagnosis and you will be guided through the whole submission and publishing process.

Your benefits of publishing with us

Rapid publication times
No submission and publication fee
Optional Open Access Publication
Free color illustrations
Accepted papers will be published online first as DOI-citable, forward-linked articles for quickest possible visibility for the scientific community
Every article easily discoverable because of SEO and comprehensive abstracting and indexing services
Secure archiving by De Gruyter and the independent archiving service Portico

Submission process

Submission of your manuscript via our submission tool ScholarOne following the Information for Authors
Single-blind peer review process
A decision on your paper is aspired within 2-4 weeks
Accepted articles are published online ahead of print approx. 4 weeks after acceptance

Please note

Manuscripts must be written in clear and concise English
Before submitting your article please have a look at the DIAGNOSIS Submission Checklist, the Publication Ethics Statement and our copyright agreement
As a condition of submission, the Template for Ethical and Legal Declarations must be customized by the submitting author on behalf of all others and uploaded as a separate Word file. Instructions on how to make declarations can be found here
Once your article is accepted you have the option to publish it open access
Our repository policy allows you to distribute 30 PDF copies of your published article to colleagues (the PDF has to include the information that it is an author's copy). Please also feel free to distribute the link to the online abstract
Please also feel free to distribute the link to the online abstract If you have any general questions please visit our FAQ page for authors
For more detailed information please contact the Diagnosis Editorial Office Diagnosis.Editorial@degruyter.com

We look forward to receiving your manuscript!

Hybrid Open Access

In this journal, authors have the option to publish their article under an open access license. Open Access allows you as an author to retain copyright and share your findings with colleagues and interested parties worldwide without any restraints.
Please note that authors from institutions with which we have a transformative agreement can publish open access without paying an article processing charge (APC). More information on the eligible institutions and articles can be found　under the "Funding and Support" tab HERE.

Editorial

Co-Editors
Mark L. Graber, Society to Improve Diagnosis in Medicine (SIDM), Plymouth, MA
Mario Plebani, University Hospital, Padova, Italy

Associate Editors
Christina L. Cifra, Boston Children's Hospital, MA
Giuseppe Lippi, University Hospital of Verona, Verona, Italy
Andrew Olson, University of Minnesota Medical School, Minneapolis, MN
Satid Thammasitboon, Baylor College of Medicine, Texas Children's Hospital, Houston, TX
Dario Torre, Primary Uniformed Services, University of Medicine, Bethesda, MD
Laura Zwaan, Institute of Medical Education Research Rotterdam (iMERR), Erasmus MC, Rotterdam, Netherlands

Section Editors – Case Reports
Andrew Parsons, University of Virginia, Charlottesviille, VA
Melissa Plesac, University of Minnesota Medical Center, Minneapolis, MN
Verity Schaye, New York University School of Medicine, New York, NY

Editorial Board
Daniela Basso, University Hospital of Padova, Padova, Italy
Carmel Crock, Melbourne, Royal Victorian Eye and Ear Hospital, Australia
Pat Croskerry, Dalhousie University, Halifax, Canada
Gurpreet Dhaliwal, San Francisco VA Medical Center, San Francisco, CA
John Ely, University of Iowa Carver College of Medicine, Iowa City, IA
Evangelos Giannitsis, University of Heidelberg, Heidelberg, Germany
Michael Laposata, University of Texas Medical Branch, Galveston, TX
Yoryos Lyratzopoulos, University of Cambridge, Cambridge, United Kingdom
Jason Maude, Isabel Healthcare, Surrey, United Kingdom
David Newman-Toker, Johns Hopkins Hospital, Baltimore, MD
Joseph J. Rencic, Boston University School of Medicine, Boston, MA
Taro Shimizu, Dokkyo Medical University Graduate School of Medicine, Tochigi, Japan
Geeta Singhal, Baylor College of Medicine, Pediatrics, Houston, TX
Dean F. Sittig, University of Texas Health Science Center, Houston, TX
Alexander von Meyer, Weiden, Germany

Editorial Office
Heike Jahnke
Walter de Gruyter GmbH
Genthiner Str. 13
10785 Berlin
Germany
Tel. +49-30-26005-220
E-mail: diagnosis.editorial@degruyter.com

(Deutsch)

Online ISSN: 2194-802X
Type: Journal
Language: English
Publisher: De Gruyter
First published: January 22, 2014
Publication Frequency: 4 Issues per Year
Audience: academics, physicians, educators, healthcare managers and leaders, patients, the diagnostic industry, and policy makers