Using Healthcare Analytics to Determine an Effective Diagnostic

Using Healthcare Analytics to Determine an Effective Diagnostic Model for Adult ADHD Diane Mitchnick, MSc. IS Athabasca University October 25, 2014

Introduction • ADHD – Attention Deficit Hyperactivity Disorder (causing inventiveness, hyperactivity, and impulsiveness) • Healthcare analytics used for physical diseases (brain cancer, diabetes, etc. ) [1] • Many tools do data mining (collect and filter data through single algorithm) [2] • Proposing a meta-analysis on existing adult ADHD screening tools to produce a more effective model from accuracy in the indicator data from these tools.

Research Goal Current diagnostic tools for adult ADHD are varied and do not explicitly interoperate or interrelate with each other. Goal: To demonstrate that an analytics based approach will be more beneficial in key areas of healthcare in addition to data mining based approaches.

Previous Work Some studies done on meta-analysis of ADHD screening tools (adult and child), and the methods to predict ADHD: Method Data Classifier Analysis Causal Model Performance Tests DSM-IV for ADHD Correlation Probability Causal Model Self-Rating Scale DSM-IV for ADHD Correlation Probability Diffusion Model MRI Imaging ADHD brain pattern attributes Description of Model (Correlation and Cluster) Causal Model Performance Tests and Interviews ADHD reaction time Hedge g effect sizes related attributes

Methodology • Determine classifier to determine diagnosis (DSM-V for adult ADHD through the American Psychiatric Association). • Obtain patient data information of other screening tool studies for metaanalysis (Pub. Med, EMBASE, Research. Gate), and refine it through Excel (ex. sample size, method results, indicator for diagnosis) • Apply causation and correlation techniques (R) to refined data to determine the best method to model the ADHD screening tool off of - Co-variation on method results calculated through ANCOVA tests [7] - Each process subject a number of rules, determined by the data (algorithms) • Results will be displayed in a web-based tool

Methodology Example CO-VARIANCE OF METHOD RESULTS WITH ADHD DSM-V CLASSIFICATION TERMS FOR VALIDITY AND RELIABILITY SAMPLE PATIENT STUDIES SHEET Method OCCURANCE OF METHODS IN 20 STUDIES Methods # of Studies Self-Rating Scale 6 MRI Imaging 3 Performance Tasks 3 MRI with Self-Rating 3 MRI Imaging Self Rating Scale Third-party Rating Scale Performance Tasks MRI Imaging with Self Rating Scale MRI with Pefromance Match against Indicator* Mean SD F 65% 30 30 0% 75% 11 0 1. 2 5 75% 15 1 20 1% 65% 20 0% 85% 30 0% 80% 20 0% Var. 12 1% * Results reviewed for reliability and validity (DSM-V classifier, ADHD brain pattern, CPT rating)

Expected Results • Controlled study size should reduce bias on results • ANCOVA tests should reduce variance on relationship patterns and validate relationships [8] • Analytics will assist medical community with better diagnoses, with trial being run on nursing and psychology students at Athabasca University in an evaluation study

Actual Results Out of 20 studies done on ADHD screening tools and their effectiveness: • • • 10 using self-reporting ASRS rating scale – 85% match with DSM-V 5 using MRI or PET scans – 80% match with ADHD brain pattern and DSM-V 2 using continuous performance tasks (CPTs) – 60% match with Conner performance task rating and DSM-V • 3 using ASRS, MRI and CPT – 90 -100% match with DSM-V, ADHD brain pattern, and Conner performance task rating (V 0. 8 -1. 5) • Mean age – 30, studies longer than 10 weeks, mean sample size - 60 patients Next steps: design a model based on these variables, ASRS scale, MRI scans, CPT results to run against a sample population mean of 60 with an age group of (18 -35).

Possible Dissemination • Tool intended for medical assistance • Tool possible study aid (analytic tool in education) • If effective, similar models for analytic tools may follow [10] • Thesis as primary publication • May inspire more publications on data analytics (learning/forecasting) • May inspire research on causation and possible prevention of mental illness/diseases • May be used for ADHD students’ performance in writing analytics

Challenges • Obtaining quality studies to use for refining model • Ensuring quality testing on model • Obtaining approval for student trial – will require new Research Ethics Board (REB) application

Timeline Phase 1 (1 year) Phase 2 (1 year) Phase 3 (1 year) Collection and Analysis of Data Application development of Diagnostic Tool evaluation on study groups

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