# Introduction

The primary business purpose of the clinical laboratory is to provide
results of testing requested by physicians and other healthcare
professionals. This testing in a broad sense is used to help solve
diagnostic problems [@verboeket-vandevenne2012]. To continue to add
value to the business purpose of the laboratory, laboratory
professionals can add value beyond just running the provided tests.
Laboratory professionals can add value through both reflective and
reflex testing. Automated analyzers add most tests based on rules
(algorithms) established by laboratory professionals; this is defined as
'reflex testing.' Clinical biochemists add the remainder of tests after
considering a more comprehensive range of information than can readily
be incorporated into reflex testing algorithms; this is defined as
'reflective testing' [@srivastava2010]. Both reflex and reflective
testing became possible with the advent of laboratory information
systems (LIS) that were sufficiently flexible to permit modification of
existing test requests at various stages of the analytical process
[@srivastava2010]. This research study will focus specifically on reflex
testing, those tests added automatically by a set of rules established
in each laboratory. In most current clinical laboratories, reflex
testing is performed with a 'hard' cutoff, using a specifically
established range with no means of flexibility [@murphy2021].

<!--# Rewrite this section -->

This study will examine the use of Machine learning to develop
algorithms to allow flexibility for automatic reflex testing in clinical
chemistry. The goal is to fill the gap between hard coded reflex testing
and fully manual reflective testing using machine learning algorithms.

<!--#  -->

## Statement of Problem

## Purpose and Research Question

### Draft Question

What are the beliefs, attitudes, opinions, and knowledge about machine
learning in the clinical laboratory.

## Significance
