# 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]. 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 Statement

Develop and test a machine learning algorithm to establish if said
algorithm can perform better then current hard coded rules to reduced
unnecessary patient testing.

## Significance

Health spending in the U.S. increased by 4.6% in 2019 to \$3.8 trillion
or \$11,582 per capita. This growth rate is in line with 2018 (4.7
percent) and slightly faster than what was observed in 2017 (4.3
percent) [@americanmedicalassociation2021]. Although laboratory costs
comprise only about 5% of the healthcare budget in the United States, it
is estimated that laboratory services drive up to 70% of all downstream
medical decisions, which encompass a substantial portion of the budget
[@ma2019]. As healthcare budgets increase, payers, including Medicare,
commercial insurers, and employers, will demand accountability and
eliminate the abuse and misuse of ineffective testing strategies
[@hernandez2003]. Increasingly, payers demand to know the value of the
tests, with value equaling quality per unit of cost. Payers want
laboratories to prove that tests are cost-effective; as reimbursement
rates decline for many standard laboratory tests, the incentives for
automated reflex testing rise for many clinical laboratories
[@hernandez2003]. Unnecessary laboratory tests are a significant source
of waste in the United States healthcare system. Prior studies suggest
that 20% of labs performed are unnecessary, wasting 200 billion dollars
each year [@li2022].

A typical example of reflex testing is thyrotropin (TSH), relaxing to
free thyroxine (Free T4 or FT4). TSH measurement is a sensitive
screening test for thyroid dysfunction. Guidelines from the American
Thyroid Association, the American Association of Clinical
Endocrinologists, and the National Academy of Clinical Biochemistry have
endorsed TSH measurement as the best first-line strategy for detecting
thyroid dysfunction in most clinical settings [@plebani2020].
Traditionally the cutoff for reflex testing was simply the reference
range for a patient's sex and race. However, recent studies have
suggested that widening these ranges reduces reflex testing by up to 34%
[@plebani2020]. In an additional study, the authors concluded that the
TSH reference range leading to reflex Free T4 testing could likely be
widened to decrease the number of unnecessary Free T4 measurements
performed. This reduction would reduce overall costs to the medical
system without likely causing negative consequences of missing the
detection of people with thyroid hormone abnormalities
[@woodmansee2018]. Even with the potential reduction in testing the
hard-coded reflex rule still exists.

## Purposed Study Set Up

Using the Medical Information Mart for Intensive Care (MIMIC) IV
Database develop and test a machine learning algorithm to determine if
TSH reflex testing can be further reduced.

The MIMIC-IV database contains patient records from 2008 to 2019 for
patients admitted to the critical care units of Beth Israel Deaconess
Medical Center. It is a common database used for various studies. The
data will be cleaned and tided to contain various patient demographics,
and all available laboratory testing for each patient. The exact
structure of the cleaned data will be determined later. Once cleaned the
data will be split into a training and testing data set. The training
data will be used to develop various machine learning algorithms to
attempt to develop an algorithm that can perform better then the hard
coded rules in place today. The study will primarily focus on TSH reflex
testing as this is the most common reflex test used in most
laboratories. The hypothesis however is that this model could be used
for many different types of reflex testing in the lab.
