118 lines
6.9 KiB
Text
118 lines
6.9 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].
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Since these early days of automation in the last fifty years, the
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clinical laboratory has rapidly expanded automation techniques. These
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include pre-packaged ready-to-use reagents, automated dispensing,
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incubation and measurement, automated sample processing (e.g., total
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laboratory automation systems, one- and two-dimensional bar codes, radio
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frequency identification tags), multiplexing tests from a single sample
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(e.g., microarrays), automated data processing (e.g., reference range,
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alert value comparisons, quality control assessment), automated
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interpretation (e.g., auto-verification), image analysis (e.g.,
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automated peripheral blood smear morphology - CellaVision, whole slide
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scanning in surgical pathology), and mobile or static robots to operate
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analyzers [@park2017]. This rise in automation in the clinical
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laboratory has also led to the need for more advanced computer systems
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to go along with the advances in instrument technology.
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Over the past few decades, Laboratory Information Systems (LIS) have
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evolved from relatively narrow, often arcane, or home-grown systems into
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sophisticated systems that are more user-friendly and support a broader
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range of functions and integration with other technologies that
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laboratories deploy [@henricks2015]. Modern LISs consist of complex,
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interrelated computer programs and infrastructure that support
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laboratories' vast array of information-processing needs. LISs have
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functions in all phases of patient testing, including specimen and test
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order intake, specimen processing and tracking, support of analysis and
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interpretation, and report creation and distribution. In addition, LISs
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provide management reports and other data that laboratories need to run
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their operations and to support continuous improvement and quality
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initiatives [@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].
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This research study will focus specifically on reflex testing, those
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tests added automatically by a set of rules established in each
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laboratory. In most current clinical laboratories, reflex testing is
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performed with a 'hard' cutoff, using a specifically established range
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with no means of flexibility [@murphy2021]. This study will examine the
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use of Machine learning to develop algorithms to allow flexibility for
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automatic reflex testing in clinical chemistry. The goal is to fill the
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gap between hard-coded reflex testing and fully manual reflective
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testing using machine 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 than current hard-coded rules to reduce
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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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