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COVID-19 is at the forefront of the consciousness of humanity, by and large, and the informatics tools we implement for managing, treating, and surveilling the disease are of great import. From disease databases to [[electronic health record]]s, from [[bioinformatics]] tools for peptide and protein modeling to laboratory tools such as LIMS and LIS, we continue to fight back against the threat of the SARS-CoV-2 virus. Yet despite the gravity of the pandemic, this is neither the first nor the last time [[Laboratory informatics|laboratory]] and scientific informatics will play a positive role in testing for disease and improving public health outcomes.
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Health informatics technology, when used responsibly, has already proven to be useful in studying and treating contagious diseases. In a 2013 research paper published in the journal ''BMJ Quality & Safety'', El-Kareh ''et al.'' analyzed and described the state of diagnostic health information technology (HIT). They noted that without the aid of HIT, clinicians are more error-prone, leaving them "vulnerable to fallible human memory, variable disease presentation, clinical processes plagued by communication lapses, and a series of well-documented ‘heuristics,’ biases, and disease-specific pitfalls."<ref name="El-KarehUseOf13">{{cite journal |title=Use of health information technology to reduce diagnostic errors |journal=BMJ Quality & Safety |author=El-Kareh, R.; Hasan, O.; Schiff, G.D. |volume=22 |issue=Suppl. 2 |pages=ii40–ii51 |year=2013 |doi=10.1136/bmjqs-2013-001884 |pmid=23852973 |pmc=PMC3786650}}</ref> Appropriate, well-designed HIT systems are capable of helping clinicians and laboratorians by providing more timely access to information, improved communication, better clinical reasoning and decision making, and improved workflows, as well as a reduction in diagnostic errors, and, as a result, improved patient safety and health outcomes.<ref name="NASEMImprov15">{{cite book |url=https://www.nap.edu/read/21794/chapter/7 |chapter=Chapter 5: Technology and Tools in the Diagnostic Process |title=Improving Diagnosis in Health Care |author=National Academies of Sciences, Engineering, and Medicine |publisher=The National Academies Press |pages=217–62 |year=2015 |doi=10.17226/21794 |isbn=9780309377720}}</ref>
==''Introduction to Quality and Quality Management Systems''==
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The goal of this short volume is to act as an introduction to the quality management system. It collects several articles related to quality, quality management, and associated systems.


From a public health perspective, the application of informatics to disease surveillance, reporting, and health habit promotion is also vital. Winters-Miner ''et al.'' note in particular the value of using informatics tools and methods to implement predictive analytics and data mining into public health. They use disease prevention and biosurveillance as major examples. We could, for example "analyze large populations of people to quantify risks related to public health, and help physicians to develop intervention programs for those patients at highest risk of some ailment or medical condition."<ref name="Winters-MinerBiomedical15">{{cite book |chapter=Chapter 3: Biomedical Informatics |title=Practical Predictive Analytics and Decisioning Systems for Medicine |author=Winters-Miner, L.A.; Bolding, P.S.; Hilbe, J.M. et al. |publisher=Academic Press |pages=42–59 |year=2015 |doi=10.1016/B978-0-12-411643-6.00003-X |isbn=9780124116436}}</ref> Additionally, through the use of syndromic surveillance systems (tools aiding in the detection of indicators leading up to disease diagnosis for individuals and populations<ref name="MandlImplement04">{{cite journal |title=Implementing syndromic surveillance: A practical guide informed by the early experience |journal=JAMIA |author=Mandl, K.D.; Overhage, J.M.; Wagner, M.M. et al. |volume=11 |issue=2 |pages=141–50 |year=2004 |doi=10.1197/jamia.M1356 |pmid=14633933 |pmc=PMC353021}}</ref>), they suggest that outbreaks can be better detected at local and national levels, and public health measures can be better implemented, increasing public awareness and hindering the spread of disease.<ref name="Winters-MinerBiomedical15" />
;1. What is quality?
:''Key terms''
:[[Quality (business)|Quality]]
:[[Quality assurance]]
:[[Quality control]]
:''The rest''
:[[Data quality]]
:[[Information quality]]
:[[Nonconformity (quality)|Nonconformity]]
:[[Service quality]]
;2. Processes and improvement
:[[Business process]]
:[[Process capability]]
:[[Risk management]]
:[[Workflow]]
;3. Mechanisms for quality
:[[Acceptance testing]]
:[[Conformance testing]]
:[[Clinical quality management system]]
:[[Continual improvement process]]
:[[Corrective and preventive action]]
:[[Good manufacturing practice]]
:[[Malcolm Baldrige National Quality Improvement Act of 1987]]
:[[Quality management]]
:[[Quality management system]]
:[[Total quality management]]
;4. Quality standards
:[[ISO 9000]]
:[[ISO 13485]]
:[[ISO 14000|ISO 14001]]
:[[ISO 15189]]
:[[ISO/IEC 17025]]
:[[ISO/TS 16949]]
;5. Quality in software
:[[Software quality]]
:[[Software quality assurance]]
:[[Software quality management]]


In the clinical laboratory, informatics systems have been influencing workflow improvements and improved service delivery for more than five decades.<ref name="JonesInform14">{{cite journal |title=Informatics and the Clinical Laboratory |journal=The Clinical Biochemist Reviews |author=Jones, R.G.; Johnson, O.A.; Baststone, G. |volume=35 |issue=3 |pages=177–192 |year=2014 |pmid=25336763 |pmc=PMC4204239}}</ref> And while improvements have been seen in the laboratory from not only the introduction of computerized systems<ref name="El-KarehUseOf13" /><ref name="NASEMImprov15" /><ref name="RaeenHowLab18">{{cite journal |title=How laboratory informatics has impacted healthcare overall |journal=Applied Research Projects |author=Raeen, M.R. |volume=54 |year=2018 |url=https://dc.uthsc.edu/hiimappliedresearch/54 |doi=10.21007/chp.hiim.0056}}</ref> but also the realization of quality control<ref name="ChawlaEval10">{{cite journal |title=Evaluating laboratory performance with quality indicators |journal=Laboratory Medicine |author=Chawla, R.; Goswami, B.; Singh, B. et al. |volume=41 |issue=5 |pages=297–300 |year=2010 |doi=10.1309/LMS2CBXBA6Y0OWMG}}</ref> and point-of-care testing<ref name="PricePoint01">{{cite journal |title=Poing of care testing |journal=BMJ |author=Price, C.P. |volume=322 |issue=7297 |pages=1285–8 |year=2001 |doi=10.1136/bmj.322.7297.1285 |pmid=11375233 |pmc=PMC1120384}}</ref>, more challenges remain. For example, quality management in the laboratory is still often a manual, time-consuming activity. While the LIMS and LIS have some tools to assist with this task, the inclusion of laboratory analytics and business intelligence tools into those systems may lead to even further improvements in quality and efficiency in the lab.<ref name="ZiaguraUsing19">{{cite web |url=https://www.mlo-online.com/information-technology/lis/article/13017560/using-analytics-to-manage-qa-and-reduce-laboratory-errors |title=Using analytics to manage QA and reduce laboratory errors |author=Ziaugra, K.; Hawrylak, V.; Bickley, T. et al. |work=Medical Laboratory Observer |date=20 March 2019 |accessdate=25 April 2020}}</ref> And in the realm of point-of-care testing, oversight and control of instruments can be lost when connectivity and training is lacking. Proper interfacing of these lab instruments could lead to improvements in those areas, says Siemens Healthineers' Daniel Gundler. "Maintaining POC instruments and overseeing the operators performing POC tests would be much easier if all the information and data from each instrument were accessible through one user interface in which coordinators could manage both the instruments and operators."<ref name="GundlerPOCT19">{{cite web |url=https://www.mlo-online.com/home/article/13017228/poct-made-easier-with-informatics |title=POCT made easier with informatics |author=Gundler, D. |work=Medical Laboratory Observer |date=23 January 2019 |accessdate=25 April 2020}}</ref>
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==References==
{{Reflist|colwidth=30em}}

Latest revision as of 19:46, 9 February 2022

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Introduction to Quality and Quality Management Systems

The goal of this short volume is to act as an introduction to the quality management system. It collects several articles related to quality, quality management, and associated systems.

1. What is quality?
Key terms
Quality
Quality assurance
Quality control
The rest
Data quality
Information quality
Nonconformity
Service quality
2. Processes and improvement
Business process
Process capability
Risk management
Workflow
3. Mechanisms for quality
Acceptance testing
Conformance testing
Clinical quality management system
Continual improvement process
Corrective and preventive action
Good manufacturing practice
Malcolm Baldrige National Quality Improvement Act of 1987
Quality management
Quality management system
Total quality management
4. Quality standards
ISO 9000
ISO 13485
ISO 14001
ISO 15189
ISO/IEC 17025
ISO/TS 16949
5. Quality in software
Software quality
Software quality assurance
Software quality management