diff --git a/_quarto.yml b/_quarto.yml index 820276c..2fcbcf7 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -7,6 +7,7 @@ book: date: "8/2/2022" chapters: - index.qmd + - chapter1.qmd - chapter2.qmd - chapter3.qmd - references.qmd diff --git a/chapter1.qmd b/chapter1.qmd new file mode 100644 index 0000000..ae50a98 --- /dev/null +++ b/chapter1.qmd @@ -0,0 +1,114 @@ +# 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.