Medical Articles

Hash and colleagues at Vanderbilt University Medical Center found that the SimulConsult diagnostic software saved time and was perceived by clinicians as useful in improving diagnosis.

Kulchak Rahm and colleagues combined Natural Language Processing of Electronic Health Record (EHR) data with SimulConsult’s ability to discern what information is useful. The result was a system that identifies useful information in the EHR, and uses it for accurate diagnosis.

In a research paper, geneticists at Vanderbilt University Medical Center discussed their protocol for using SimulConsult in the diagnosis process. They suggested “In addition to increased communication, programs such as OMIM and SimulConsult can be used to analyze combinations of signs and symptoms to identify and prioritize differential diagnoses.”

The SimulConsult Genome-Phenome Analyzer supports all HGNC gene symbols in use since 1 January 2010, even if the symbol has been changed. The hyperlinked article details how HGNC is systematically changing all the symbols that Excel turns into dates in a spreadsheet. But even if you use the old symbols such as SEPT12 in a variant table, or search for it in the software, SimulConsult recognizes that as being the HGNC symbol SEPTIN12.

In the 81 cases (with 216 individuals) the gene abnormality was recognized in 100%, and it was ranked #1 in 94% of cases. Large CNVs could be analyzed in an integrated analysis, performed in 24 of the cases. The process is rapid enough to allow for periodic reanalysis of unsolved cases.

“In all families (84 pregnancies) pertinence was > 95% between signs/symptoms and molecular cause”, meaning that not only was the correct diagnosis ranked as #1 but no other diagnoses were close.

Published in The Journal for Electronic Health Data and Methods on December 6, 2017 by Michael M. Segal, Alanna K. Rahm, Nathan C. Hulse, Grant Wood, Janet L. Williams, Lynn Feldman, Gregory J. Moore, David Gehrum, Michelle Yefko, Steven Mayernick, Roger Gildersleeve, Margie C. Sunderland, Steven B. Bleyl, Peter Haug, Marc S. Williams

The article describes the result of an NIH-sponsored study of integrating SimulConsult’s diagnostic decision support system with Electronic Health Record systems.

Published in the Journal of Genetic Counselors in April 2018 (open access) by Williams JL, Rahm AK, Zallen DT, Stuckey H, Fultz K, Fan AL, Bonhag M, Feldman L, Segal MM, Williams MS.

The article describes the result of a PCORI-sponsored study of providing information about genetic testing to patients, including a display driven by the highly granular onset information in SimulConsult’s diagnostic decision support system.

Published by the Journal of Pediatric Rheumatology Online on December 13, 2016 (open access) by Michael M. Segal, Balu Athreya, Mary Beth F. Son, Irit Tirosh, Jonathan S. Hausmann, Elizabeth Y. N. Ang, David Zurakowski, Lynn K. Feldman, and Robert P. Sundel

The article describes the result of an NIH-sponsored study of adding rheumatology information to the SimulConsult diagnostic decision support system. The 26 clinicians demonstrated a significant reduction in diagnostic errors following introduction of the software, from 28% errors while unaided to 15% using decision support (p < 0.0001). Improvement was greatest for emergency medicine physicians (p = 0.013) and clinicians in practice for less than 10 years (p = 0.012). This error reduction occurred despite the fact that testers employed an “open book” approach to generate their initial lists of potential diagnoses, spending an average of 8.6 min using printed and electronic sources of medical information before using the diagnostic software.

Published in the American Journal of Medical Genetics in May 2016 (open access) by Williams JL, Rahm AK, Stuckey H, Green J, Feldman L, Zallen DT, Bonhag M, Segal MM, Fan AL, Williams MS.

The article describes the responses by clinicians to SimulConsult’s prognosis table, as used in genomic reports. One sample: “I love it—no clicks, detailed, comprehensive enough that I didn’t feel I needed another source—everything a pediatrician would think about.”

Published in Applied Translational Genomics, in July 22, 2015 by Michael M. Segal.

Excerpt:
In a brain MRI report, the following words often appear: “clinical correlation is recommended”. These words signify that inadequate clinical information was provided, or that an unexpected finding on the MRI should be assessed clinically. “Clinical correlation is recommended” is less common in a report about a single gene or simple gene panel. This is because the very act of ordering the test conveys much of what is important about the clinical situation, and only rarely is further information needed.

Genetics labs are moving into new territory as they adopt next-generation genomic sequencing. When moving beyond single gene tests and simple panels, more clinical correlation is needed. The complexity of interpretation becomes similar to a brain MRI, only more so.

In an exome, thousands of variants are found. Even after comparing to other family members, and using estimates of variant pathogenicity, many genes must be considered. Sometimes clinical correlation can be as simple as using the key clinical finding, assuming that you know which finding is key. But sometimes the situation is more complicated: variants are found in a gene that hadn’t been considered clinically, or two genes are needed to explain the clinical picture, and more clinical correlation is needed.

Published in the Journal of Child Neurology in June of 2015 by Michael M. Segal, MD, PhD, Mostafa Abdellateef, Ayman W. El-Hattab, MD, Brian S. Hilbush, PhD, Francisco M. De La Vega, PhD, Gerard Tromp, PhD, Marc S. Williams, MD, Rebecca A. Betensky, PhD, and Joseph Gleeson, MD

Abstract:
We describe an “integrated genome-phenome analysis” that combines both genomic sequence data and clinical information for genomic diagnosis. It is novel in that it uses robust diagnostic decision support and combines the clinical differential diagnosis and the genomic variants using a “pertinence” metric. This allows the analysis to be hypothesis-independent, not requiring assumptions about mode of inheritance, number of genes involved, or which clinical findings are most relevant. Using 20 genomic trios with neurologic disease, we find that pertinence scores averaging 99.9% identify the causative variant under conditions in which a genomic trio is analyzed and family-aware variant calling is done. The analysis takes seconds, and pertinence scores can be improved by clinicians adding more findings. The core conclusion is that automated genome-phenome analysis can be accurate, rapid, and efficient. We also conclude that an automated process offers a methodology for quality improvement of many components of genomic analysis.

Published by the American Journal of Medical Genetics (open access) on June 18, 2015 by Heather Stuckey, Janet L. Williams, Audrey L. Fan, Alanna Kulchak Rahm, Jamie Green, Lynn Feldman, Michele Bonhag, Doris T. Zallen, Michael M. Segal, Marc S. Williams

The article describes the responses by families to SimulConsult’s Prognosis Table, as used in genomic reports. Some samples:

– Gives idea of what to look for in the future
– Timeframes very helpful
– Would use as baseline for reference
– Everything on it is necessary
– Can use with provider for discussion

Published in the Journal of Child Neurology in April 2014 by Segal MM, Williams MS, Gropman AL, Torres AR, Forsyth R, Connolly AM, El-Hattab AW, Perlman SJ, Samanta D, Parikh S, Pavlakis SG, Feldman LK, Betensky RA, Gospe SM Jr.

Abstract:
Using vignettes of real cases and the SimulConsult diagnostic decision support software, neurologists listed a differential diagnosis and workup before and after using the decision support. Using the software, there was a significant reduction in error, up to 75% for diagnosis and 56% for workup. This error reduction occurred despite the baseline being one in which testers were allowed to use narrative resources and Web searching. A key factor that improved performance was taking enough time (>2 minutes) to enter clinical findings into the software accurately. Under these conditions and for instances in which the diagnoses changed based on using the software, diagnostic accuracy improved in 96% of instances. There was a 6% decrease in the number of workup items accompanied by a 34% increase in relevance. The authors conclude that decision support for a neurological diagnosis can reduce errors and save on unnecessary testing.

Published in Genome Biology on March 25, 2014 by Brownstein CA et al.

The article describes the CLARITY genome interpretation contest, and how SimulConsult had by far the fastest analysis times.

Presented on March 22, 2013 by Segal MM, Wiliams MS, Tromp G and Gleeson JG at the American College of Medical Genetics 2013 annual Meeting.

Published in Neurology on May 15, 2012 by Segal MM and Schiffmann R.

The editorial discusses the role for diagnostic decision support in facilitating an evidence-based discussion between clinicians and payers.

Published in “Pediatric Neurology: Principles and Practice”, Swaiman KF et al., editors, 5th edition, Saunders, Chapter 108 by Segal M and Lever S

Published in Brain on July 12, 2010 by Peter Garrard John Stephenson Vijeya Ganesan Timothy Peters

The authors used SimulConsult to analyze a historical diagnosis.

Published as a book in September 2009 by King MD and Stephenson BP, Mac Keith Press

In the book the authors discuss SimulConsult, beginning on page 1 with “With the explosion of electronic information such as found on PubMed and the availability of freely accessible diagnostic software such as SimulConsult one might ask what possible use is there for a handbook like this.