updates

_quarto.yml

@@ -0,0 +1,30 @@
+project:
+  type: book
+
+book:
+  title: "DHSC-Capstone"
+  author: "Kyle Belanger"
+  date: "8/2/2022"
+  chapters:
+    - index.qmd
+    - chapter2.qmd
+    - chapter3.qmd
+    - references.qmd
+
+
+bibliography: references.bib
+csl: apa.csl
+
+format:
+  html:
+    theme: journal
+  docx:
+    reference-doc: extras/custom-reference.docx
+    number-sections: false
+
+
+editor: visual
+
+exectue:
+  freeze: auto
+

chapter3.qmd

@@ -0,0 +1,7 @@
+# Chapter 3
+
+## Proposed 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.

index.qmd

@@ -1,8 +1,8 @@
 # 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.
+The early 20th century marks 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 began reorganizing their laboratories so that they were 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]. Prior to 1960 almost all testing in the laboratory was preformed using manual methods. In the mid 1960's a limited amount of automated analyzer became available, allowing for more rapid testing, as well as running multiple tests at the same time [@park2017]. Since these early days of automation in the last fifty years the clnical labortory has seen a rapid expanse in 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 lead 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, and/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 a vast array of information-processing needs of laboratories. 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]
 
-## Statement of Problem
+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.
 
 ## Purpose and Research Statement
 
@@ -13,9 +13,3 @@ Develop and test a machine learning algorithm to establish if said algorithm can
 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.

package.json

@@ -1,5 +0,0 @@
-{
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-    "@quarto/netlify-plugin-quarto": "latest"
-  }
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references.bib

@@ -228,3 +228,65 @@ PMID: 33045173}
 	url = {https://www.thyroid.org/patient-thyroid-information/ct-for-patients/february-2018/vol-11-issue-2-p-3-4/},
 	langid = {canadian}
 }
+
+@article{berger1999,
+	title = {A brief history of medical diagnosis and the birth of the clinical laboratory: Part 2--laboratory science and professional certification in the 20th century},
+	author = {Berger, Darlene},
+	year = {1999},
+	month = {08},
+	date = {1999-08},
+	journal = {Medical Laboratory Observer: MLO},
+	pages = {32--4, 36, 38},
+	volume = {31},
+	number = {8},
+	url = {https://www.proquest.com/docview/223382876/citation/57E18BE0383F41C4PQ/1},
+	note = {Num Pages: 5
+Place: Nashville, United States
+Publisher: Endeavor Business Media},
+	langid = {English}
+}
+
+@article{park2017,
+	title = {One hundred years of clinical laboratory automation: 1967{\textendash}2067},
+	author = {Park, Jason Y. and Kricka, Larry J.},
+	year = {2017},
+	month = {08},
+	date = {2017-08-01},
+	journal = {Clinical Biochemistry},
+	pages = {639--644},
+	volume = {50},
+	number = {12},
+	doi = {10.1016/j.clinbiochem.2017.03.004},
+	url = {https://www.sciencedirect.com/science/article/pii/S0009912016303319},
+	langid = {en}
+}
+
+@article{henricks2015,
+	title = {Laboratory Information Systems},
+	author = {Henricks, Walter H.},
+	year = {2015},
+	month = {06},
+	date = {2015-06},
+	journal = {Surgical Pathology Clinics},
+	pages = {101--108},
+	volume = {8},
+	number = {2},
+	doi = {10.1016/j.path.2015.02.016},
+	url = {https://linkinghub.elsevier.com/retrieve/pii/S1875918115000343},
+	langid = {en}
+}
+
+@article{henricks2015,
+	title = {Laboratory Information Systems},
+	author = {Henricks, Walter H.},
+	year = {2015},
+	month = {06},
+	date = {2015-06},
+	journal = {Surgical Pathology Clinics},
+	pages = {101--108},
+	volume = {8},
+	number = {2},
+	doi = {10.1016/j.path.2015.02.016},
+	url = {https://linkinghub.elsevier.com/retrieve/pii/S1875918115000343},
+	langid = {en}
+}