Web Software to develop an RDoC Compatible Adaptive Diagnostic Nosology the SID

n psychiatry clinical judgment has been the predominant method of diagnosis The `gold standard for clinical research is the Structured Clinical Interview for DSM SCID The SCID promotes reliability but its validity is questionable There is growing recognition that biomarkers can be used to identify more homogeneous patient populations but there is substantial phenotypic discordance for schizophrenia even in identical twins In the absence of symptom data biomarkers alone are unlikely to yield a clinical diagnosis In a past SBIR project TeleSage Inc successfully converted the paper SCID into a web based software program The NetSCID is now widely used in research Nevertheless in order to achieve the goal of turning clinical care networks into centers for research Insel Directorandapos s Blog and furthering the RDoC initiative we need a means of gathering rigorous diagnostic data in routine clinical care This software must a require minimal clinician time b allow clinics to bill according to the current DSM categories and c be able to gather the large amount of data necessary to inform a broad research agenda including machine learning techniques TeleSage proposes to develop a self report diagnostic assessment that satisfies both immediate clinical needs and broader research goals the Screening Interview for Diagnosis or SID TeleSage has worked with an expert panel including Dr Michael First the primary author of the SCID iteratively developing and testing self report items Based on expert panel review and cognitive interviewing we identified a final set of unique self report Likert scale items covering all of the individual sub symptoms described within each of the SCID criteria TeleSage has also created a behavioral health PORTAL that includes the NetSCID IRT CAT item administration randomization and longitudinal reporting capabilities The PORTAL exchanges data and reports with several EHRs including the NetSmart EHR system This Direct to Phase II application aims to create the SID which will a reside on our existing secure web PORTAL b administer simple Likert scale self report items c generate DSM and ICD diagnoses for billing d use minimal clinician time e pull pre defined data fields from the EHR e g family history demographic and biomarker data f be able to add new self report items for exploration g integrate with machine learning tools and h send raw data and interpretive reports to EHR systems Because of these features we believe that the SID has the potential to be used widely by both clinicians and researchers The SID is intended to help transcend DSM and to support RDoC The SID product must be commercially successful and useful both in routine clinical care and research The SID is intended to facilitate the development and evolution of a new behavioral health nosology based on the aggregation of biomarker and symptom data a nosology that is more analogous to those found in other fields of medicine The Structured Clinical Interview for DSM SCID represents the current diagnostic gold standard in behavioral health and yields much more reliable diagnoses than the unstructured patient interviews that predominate in clinical settings however the full SCID is a lengthy and complex paper and pencil instrument that takes approximately minutes to administer and is thus seldom used outside of clinical research settings We want to create a new self report software tool that will save clinicians time relative to the paper SCID significantly reduce diagnostic error rates greatly increase the reliability of diagnoses in routine clinical care and integrate seamlessly into Electronic Health Record EHR systems the Screening Inventory for Diagnosis SID The SID is a self report computer adaptive diagnostic assessment based on easy to understand five point Likert scale items The SID is intended to help transcend DSM support RDoC and facilitate the development and evolution of a new behavioral health nosology based on the aggregation of biomarker and symptom data