# 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].

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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.

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## Statement of Problem

## Purpose and Research Question

### Draft Question

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

## Significance