Published in the Wall Street Journal on May 13, 2019 by Tom Mysz
We shouldn’t become scientific zealot by silencing, ridiculing or playing down the genuine concerns of people on the fringe.
Published in the Wall Street Journal on May 1, 2019 by Michael Segal, MD PhD
They can appear to be the proximate cause of a condition when they have nothing to do with the ultimate cause. Why are people afraid of vaccines? The already strong evidence of their safety got stronger in March with the release of a large Danish study that addressed several objections to previous studies and found that the measles vaccine poses no additional risk of developing autism.
Released April 1, 2019
SimulConsult announces a new version of its diagnostic decision support tool. The tool combines the power of
curated human expertise and computational artificial intelligence (AI) to empower clinicians in diagnosis and
workup of patients. It is used today in 118 countries. The new version has a completely new interface that allows it to run on mobile devices as well as on computers. It is fast to use and puts the diagnostic power into a clinician’s hands whenever needed.
SimulConsult’s newly-formed research partner, PhenoSolve, LLC., received an SBIR I from the National Human Genome Research Institute (NHGRI) of the NIH beginning September 19, 2018 with Michael M. Segal MD PhD as Primary Investigator
The rationale is that the current state of genome analysis and clinical use is lagging in part due to a situation of siloed information and capabilities, a situation that Clayton Christensen characterizes as requiring end-to-end integration to set the stage for a subsequent environment of interoperable standards. While analogous to the radiology PACS, the G-PACS will also have advanced analysis capabilities and sophisticated handling of 2 types of information – patient findings and annotated genomic variants to enable genome-phenome analysis by clinicians and laboratorians using these components.
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.
SimulConsult receives SBIR II from the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the NIH of the NIH beginning August 1, 2017 with Michael M. Segal MD PhD as Primary Investigator
This research aims to ease the shortage of pediatric rheumatologists by using decision support software to improve the ability of generalist physicians to make rheumatologic diagnoses. It extends to actual clinical practice our previous work that demonstrated large reductions in diagnostic error when tested using written case summaries, using a randomized controlled study. The research will be done after implementing improvements to the diagnostic tool suggested by the earlier pilot study.
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.
SimulConsult, Inc. of Chestnut Hill, MA has been awarded START Stage I funding for their work on the SimulConsult® diagnostic decision support system. Lynn Feldman, CEO of SimulConsult, Inc. said “the START program fills a critical gap between the SBIR research program and achieving a revenue-producing product – investment in commercialization activities. The START program will allow us to migrate our technology to a new platform, which means we can price our product more competitively, improving our financial prospects and rate of growth.”
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.
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
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
SimulConsult receives SBIR II from the National Human Genome Research Institute (NHGRI) of the NIH beginning March 27 2013 with Michael M. Segal MD PhD as Primary Investigator
With the declining cost of whole exome and genome sequencing, the main cost of such testing is becoming the cost of interpreting the huge amount of data that is generated. This project combines the power of diagnostic software that examines all known diagnoses (the “phenome”) with the power of whole exome sequencing to examine the genome, and uses advanced capabilities such as compatibility with all family structures and the ability to take into account DNA deletions and duplications. In automating the genome-phenome analysis, this project brings the power of genome analysis to clinical practice – lowering costs while increasing accuracy.
SimulConsult has commercial contracts with a number of laboratories for its Genome-Phenome Analyzer to analyze exome data. These contracts make use of the ongoing research and product development from this grant, including the addition of the ability to analyze copy number variations from genomic data or a traditional microarray and families with samples from family members other than a trio.
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.
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.
SimulConsult receives SBIR II from the National Library of Medicine of the NIH beginning September 2013 with Michael M. Segal MD PhD as Primary Investigator
This project uses the power of diagnostic decision support software to provide advanced capabilities to multiple electronic health records, including complex test selection, superior clinical reporting, payor integration and learning from patient results, using as research areas radiology and genetics, in the diagnosis of neurological diseases. The aims are to use the deep knowledge of such a tool to improve accuracy and cost-effectiveness of medical care. More generally, the goal is to advance the vision of “best of breed” knowledge tools making medical care better and more affordable.
SimulConsult and the Geisinger Genomic Medicine Institute won the Bio-IT World Best Practices Award in Informatics for their use of SimulConsult’s Genome-Phenome Analyzer to analyze genomes accurately and quickly.
SimulConsult receives SBIR I from the National Human Genome Research Institute (NHGRI) of the NIH beginning March 27 2013 with Michael M. Segal MD PhD as Primary Investigator
With the declining cost of whole genome sequencing, the main cost of such testing is becoming the cost of interpreting the huge amount of data that is generated. This project combines the power of using diagnostic software to examine all known diagnoses (the “phenome”) with the power of whole exome sequencing to examine the genome. In automating the genome-phenome analysis, this project brings the power of genome analysis to clinical practice – lowering costs while increasing accuracy.
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.