114 lines
6.8 KiB
Text
114 lines
6.8 KiB
Text
# Introduction
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The early 20th century marked the beginning of a quality movement in
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hospitals and laboratories that began with physicians and healthcare
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workers. In the early part of the century, many hospitals started
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reorganizing their laboratories to be headed by biochemists.
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Professional organizations emerged as self-regulating groups that helped
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ensure the skills and knowledge of laboratory professionals would pass
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the scrutiny of the hospitals that employed them [@berger1999]. An
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American Medical Association survey later showed that 48% of U.S.
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hospitals had clinical laboratories by 1923 [@berger1999]. Before 1960,
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almost all testing in the laboratory was performed using manual methods.
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In the mid-1960s, a limited amount of automated analyzers became
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available, allowing for more rapid testing and running multiple tests at
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the same time [@park2017]. Since these early days of automation in the
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last fifty years, the clinical laboratory has rapidly expanded
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automation techniques. These include pre-packaged ready-to-use reagents,
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automated dispensing, incubation and measurement, automated sample
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processing (e.g., total laboratory automation systems, one- and
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two-dimensional bar codes, radio frequency identification tags),
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multiplexing tests from a single sample (e.g., microarrays), automated
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data processing (e.g., reference range, alert value comparisons, quality
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control assessment), automated interpretation (e.g., auto-verification),
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image analysis (e.g., automated peripheral blood smear morphology -
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CellaVision, whole slide scanning in surgical pathology), and mobile or
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static robots to operate analyzers [@park2017]. This rise in automation
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in the clinical laboratory has also led to the need for more advanced
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computer systems to go along with the advances in instrument technology.
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Over the past few decades, LISs have evolved from relatively narrow,
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often arcane, or home-grown systems into sophisticated systems that are
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more user-friendly and support a broader range of functions and
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integration with other technologies that laboratories deploy
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[@henricks2015]. Modern LISs consist of complex, interrelated computer
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programs and infrastructure that support laboratories' vast array of
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information-processing needs. LISs have functions in all phases of
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patient testing, including specimen and test order intake, specimen
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processing and tracking, support of analysis and interpretation, and
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report creation and distribution. In addition, LISs provide management
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reports and other data that laboratories need to run their operations
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and to support continuous improvement and quality initiatives
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[@henricks2015].
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The clinical laboratory's primary business purpose is to provide testing
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results requested by physicians and other healthcare professionals. In a
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broad sense, this testing is used to help solve diagnostic problems
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[@verboeket-vandevenne2012]. To continue adding value to the
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laboratory's business purpose, laboratory professionals can add value
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beyond just running the provided tests. Laboratory professionals can add
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value through both reflective and reflex testing. Automated analyzers
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add most tests based on rules (algorithms) established by laboratory
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professionals; this is defined as 'reflex testing.' Clinical biochemists
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add the remainder of tests after considering a more comprehensive range
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of information that can readily be incorporated into reflex testing
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algorithms; this is defined as 'reflective testing' [@srivastava2010].
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Both reflex and reflective testing became possible with the advent of
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laboratory information systems (LIS) that were sufficiently flexible to
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permit modification of existing test requests at various stages of the
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analytical process [@srivastava2010]. This research study will focus
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specifically on reflex testing, those tests added automatically by a set
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of rules established in each laboratory. In most current clinical
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laboratories, reflex testing is performed with a 'hard' cutoff, using a
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specifically established range with no means of flexibility
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[@murphy2021]. This study will examine the use of Machine learning to
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develop algorithms to allow flexibility for automatic reflex testing in
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clinical chemistry. The goal is to fill the gap between hard-coded
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reflex testing and fully manual reflective testing using machine
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learning algorithms.
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## Purpose and Research Statement
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Develop and test a machine learning algorithm to establish if said
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algorithm can perform better then current hard coded rules to reduced
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unnecessary patient testing.
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## Significance
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Health spending in the U.S. increased by 4.6% in 2019 to \$3.8 trillion
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or \$11,582 per capita. This growth rate is in line with 2018 (4.7
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percent) and slightly faster than what was observed in 2017 (4.3
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percent) [@americanmedicalassociation2021]. Although laboratory costs
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comprise only about 5% of the healthcare budget in the United States, it
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is estimated that laboratory services drive up to 70% of all downstream
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medical decisions, which encompass a substantial portion of the budget
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[@ma2019]. As healthcare budgets increase, payers, including Medicare,
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commercial insurers, and employers, will demand accountability and
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eliminate the abuse and misuse of ineffective testing strategies
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[@hernandez2003]. Increasingly, payers demand to know the value of the
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tests, with value equaling quality per unit of cost. Payers want
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laboratories to prove that tests are cost-effective; as reimbursement
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rates decline for many standard laboratory tests, the incentives for
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automated reflex testing rise for many clinical laboratories
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[@hernandez2003]. Unnecessary laboratory tests are a significant source
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of waste in the United States healthcare system. Prior studies suggest
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that 20% of labs performed are unnecessary, wasting 200 billion dollars
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annually [@li2022].
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A typical example of reflex testing is thyrotropin (TSH), relaxing to
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free thyroxine (Free T4 or FT4). TSH measurement is a sensitive
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screening test for thyroid dysfunction. Guidelines from the American
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Thyroid Association, the American Association of Clinical
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Endocrinologists, and the National Academy of Clinical Biochemistry have
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endorsed TSH measurement as the best first-line strategy for detecting
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thyroid dysfunction in most clinical settings [@plebani2020].
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Traditionally the cutoff for reflex testing was simply the reference
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range for a patient's sex and race. However, recent studies have
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suggested that widening these ranges reduces reflex testing by up to 34%
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[@plebani2020]. In an additional study, the authors concluded that the
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TSH reference range leading to reflex Free T4 testing could likely be
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widened to decrease the number of unnecessary Free T4 measurements
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performed. This reduction would reduce overall costs to the medical
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system without likely causing negative consequences of missing the
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detection of people with thyroid hormone abnormalities
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[@woodmansee2018]. Even with the potential reduction in testing, the
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hard-coded reflex rule still exists.
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