update chapter 1

move chapter 1 to new file

_quarto.yml

@@ -7,6 +7,7 @@ book:
   date: "8/2/2022"
   chapters:
     - index.qmd
+    - chapter1.qmd
     - chapter2.qmd
     - chapter3.qmd
     - references.qmd

chapter1.qmd

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+# Introduction
+
+The early 20th century marked the beginning of a quality movement in
+hospitals and laboratories that began with physicians and healthcare
+workers. In the early part of the century, many hospitals started
+reorganizing their laboratories to be headed by biochemists.
+Professional organizations emerged as self-regulating groups that helped
+ensure the skills and knowledge of laboratory professionals would pass
+the scrutiny of the hospitals that employed them [@berger1999]. An
+American Medical Association survey later showed that 48% of U.S.
+hospitals had clinical laboratories by 1923 [@berger1999]. Before 1960,
+almost all testing in the laboratory was performed using manual methods.
+In the mid-1960s, a limited amount of automated analyzers became
+available, allowing for more rapid testing and running multiple tests at
+the same time [@park2017]. Since these early days of automation in the
+last fifty years, the clinical laboratory has rapidly expanded
+automation techniques. These include pre-packaged ready-to-use reagents,
+automated dispensing, incubation and measurement, automated sample
+processing (e.g., total laboratory automation systems, one- and
+two-dimensional bar codes, radio frequency identification tags),
+multiplexing tests from a single sample (e.g., microarrays), automated
+data processing (e.g., reference range, alert value comparisons, quality
+control assessment), automated interpretation (e.g., auto-verification),
+image analysis (e.g., automated peripheral blood smear morphology -
+CellaVision, whole slide scanning in surgical pathology), and mobile or
+static robots to operate analyzers [@park2017]. This rise in automation
+in the clinical laboratory has also led to the need for more advanced
+computer systems to go along with the advances in instrument technology.
+Over the past few decades, LISs have evolved from relatively narrow,
+often arcane, or home-grown systems into sophisticated systems that are
+more user-friendly and support a broader range of functions and
+integration with other technologies that laboratories deploy
+[@henricks2015]. Modern LISs consist of complex, interrelated computer
+programs and infrastructure that support laboratories' vast array of
+information-processing needs. LISs have functions in all phases of
+patient testing, including specimen and test order intake, specimen
+processing and tracking, support of analysis and interpretation, and
+report creation and distribution. In addition, LISs provide management
+reports and other data that laboratories need to run their operations
+and to support continuous improvement and quality initiatives
+[@henricks2015].
+
+The clinical laboratory's primary business purpose is to provide testing
+results requested by physicians and other healthcare professionals. In a
+broad sense, this testing is used to help solve diagnostic problems
+[@verboeket-vandevenne2012]. To continue adding value to the
+laboratory's business purpose, 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 that 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.
+
+## 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
+annually [@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.