Iteration 4

Iteration 4 - Correlating Diagnoses and Medications

Iteration 4 is still being developed. This document is up for comment.

Question(s) Being Answered:

What is the prevalence of atypical anti-psychotics prescriptions in the network (at a point in time)?

What are the most common diagnoses associated with atypical anti-psychotic prescriptions?

Rationale:

This iteration and likely several others are still about building out the clinical content for questioning. Through the two questions we are answering this iteration, we expand what is moved the endpoint to include more general patient characteristics and diagnoses.

Also, this iteration, the question types are different. We are now asking SCOOP to provide and aggregate prevalence data (question 1) for a class of medications.

We are also asking SCOOP to rank prevalence of associated conditions across the network (question 2). This may prove tricky, but is an essential feature. Key to this iteration, then, is the ability to generate a rank list of characteristics (e.g. diagnoses) in a population of patients (those on anti-psychotics) and do so across endpoints with varying sized populations (both total population and those in the study group).

Features:

Feature 4-xx: Ask a prevalence question In order to determine the prevalence of a particular circumstance within the network The question manager should be able to code a prevalence question that defines the query that will discover the size of the subpopulation of interest (i.e. prevalence relative another subpopulation).

Feature 4-XX: Calculate Local Prevalence Data In order to measure prevalence data in a subpopulation of patients at a site The endpoint should be able to Calculate take specific parameters for numerator and denominator and return prevalence values to the hub.

Feature 4-XX: Compile Prevalence Data In order to get prevalence data in a subpopulation of patients across the network The hub should be able to compile local prevalence data from multiple endpoint responses into an aggregate prevalence that takes into account practice sizes.

Feature 4-xx: Measure Endpoint Variability In order to describe the prevalence findings across the network The hub should be able to calculate and report variability of results across all responding endpoints in the network

Feature 4-xx: Ask a common feature question In order to determine common correlations between two patient characteristics The question manager should be able to Describe a subpopulation (patients on atypical antispychotics) and a second characteristic (diagnoses) to determine the frequency counts and most common occurrences of items in that second characteristic.

Feature 4-xx: Create Local Rank List In order to determine the local correlations between one patient feature and another The endpoint should be able to search its repository and generate a rank list (with values) of the prevalence of diagnoses in the group of patients that are on atypical antipsychotics.

Feature 4-XX: Compile Network Rank List In order to determine the most common diagnoses associated with atypical antipsychotic use The Hub should be able to query and compile results from multiple endpoints to create a representative list of common diagnoses associated with atypical antipsychotics.