Difference between revisions of "Journal:Design and implementation of a clinical laboratory information system in a low-resource setting"

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Recent studies have tried to categorize errors using phases of the total testing process, which comprises pre-analytical, analytical, and post-analytical phases.<ref name="PlebaniExplor09">{{cite journal |title=Exploring the iceberg of errors in laboratory medicine |journal=Clinical Chimica Acta |author=Plebani, M. |volume=404 |issue=1 |pages=16–23 |year=2009 |doi=10.1016/j.cca.2009.03.022 |pmid=19302995}}</ref> The pre-analytical phase covers all activities from when the test is ordered to when the specimen is delivered to the laboratory for testing. The analytical phase covers the activities involved in the actual testing of the specimen, and the post-analytical phase involves the reporting and interpretation of the laboratory result. Among the phases of the total testing process, it has been observed that most laboratory errors happen outside of the analytical phase.<ref name="PlebaniErrors06">{{cite journal |title=Errors in clinical laboratories or errors in laboratory medicine? |journal=Clinical Chemistry and Laboratory Medicine |author=Plebani, M. |volume=44 |issue=6 |pages=750–9 |year=2006 |doi=10.1515/CCLM.2006.123 |pmid=16729864}}</ref> An example of an error outside the analytical phase is the mislabeling of a specimen, which could happen during the drawing of a sample in the pre-analytical phase. While error rates vary between health facilities, it is estimated that 32% to 75% of all laboratory errors happen in the pre-analytical phase.<ref name="LewinLab08">{{cite web |url=https://www.cdc.gov/labbestpractices/pdfs/2007-status-report-laboratory_medicine_-_a_national_status_report_from_the_lewin_group_updated_2008-9.pdf |format=PDF |title=Laboratory Medicine: A National Status Report |author=The Lewin Group |publisher=Centers for Disease Control and Prevention |date=May 2008}}</ref> Error rates in the analytical phase are estimated in the range of 13% to 32% and in the post-analytical phase in the range of 9% to 31%.<ref name="LewinLab08" />
Recent studies have tried to categorize errors using phases of the total testing process, which comprises pre-analytical, analytical, and post-analytical phases.<ref name="PlebaniExplor09">{{cite journal |title=Exploring the iceberg of errors in laboratory medicine |journal=Clinical Chimica Acta |author=Plebani, M. |volume=404 |issue=1 |pages=16–23 |year=2009 |doi=10.1016/j.cca.2009.03.022 |pmid=19302995}}</ref> The pre-analytical phase covers all activities from when the test is ordered to when the specimen is delivered to the laboratory for testing. The analytical phase covers the activities involved in the actual testing of the specimen, and the post-analytical phase involves the reporting and interpretation of the laboratory result. Among the phases of the total testing process, it has been observed that most laboratory errors happen outside of the analytical phase.<ref name="PlebaniErrors06">{{cite journal |title=Errors in clinical laboratories or errors in laboratory medicine? |journal=Clinical Chemistry and Laboratory Medicine |author=Plebani, M. |volume=44 |issue=6 |pages=750–9 |year=2006 |doi=10.1515/CCLM.2006.123 |pmid=16729864}}</ref> An example of an error outside the analytical phase is the mislabeling of a specimen, which could happen during the drawing of a sample in the pre-analytical phase. While error rates vary between health facilities, it is estimated that 32% to 75% of all laboratory errors happen in the pre-analytical phase.<ref name="LewinLab08">{{cite web |url=https://www.cdc.gov/labbestpractices/pdfs/2007-status-report-laboratory_medicine_-_a_national_status_report_from_the_lewin_group_updated_2008-9.pdf |format=PDF |title=Laboratory Medicine: A National Status Report |author=The Lewin Group |publisher=Centers for Disease Control and Prevention |date=May 2008}}</ref> Error rates in the analytical phase are estimated in the range of 13% to 32% and in the post-analytical phase in the range of 9% to 31%.<ref name="LewinLab08" />


Informatics interventions may be useful in reducing such laboratory errors. Examples of such interventions are computer-aided ordering of laboratory tests, barcode labeling of specimen tubes, and automated reporting of laboratory test results. These interventions are often provided using computer systems that allow physicians to order diagnostic tests, medications, and other procedures, commonly referred to as [[Computerized physician order entry|computerized provider order entry]] (CPOE)<ref name="BaronCompu11">{{cite journal |title=Computerized provider order entry in the clinical laboratory |journal=Journal of Pathology Informatics |author=Baron, J.M.; Dighe, A.S. |volume=2 |pages=35 |year=2011 |doi=10.4103/2153-3539.83740 |pmid=21886891 |pmc=PMC3162747}}</ref>, which is often a part of a larger [[electronic health record]] system. However, such comprehensive electronic health record systems have low penetration in low-resource settings where the burden of disease is high and laboratory errors are further exacerbated by poor infrastructure, shortages in trained workforce, and [[information]]al challenges.<ref name="BecichInform00" /><ref name="PetroseAss16">{{cite journal |title=Assessing Perceived Challenges to Laboratory Testing at a Malawian Referral Hospital |journal=American Journal of Tropical Medicine and Hygiene |author=Petrose, L.G.; Fisher, A.M.; Douglas, G.P. et al. |volume=94 |issue=6 |pages=1426-32 |year=2016 |doi=10.4269/ajtmh.15-0867 |pmid=PMC4889768 |pmc=PMC3162747}}</ref>
Informatics interventions may be useful in reducing such laboratory errors. Examples of such interventions are computer-aided ordering of laboratory tests, barcode labeling of specimen tubes, and automated reporting of laboratory test results. These interventions are often provided using computer systems that allow physicians to order diagnostic tests, medications, and other procedures, commonly referred to as [[Computerized physician order entry|computerized provider order entry]] (CPOE)<ref name="BaronCompu11">{{cite journal |title=Computerized provider order entry in the clinical laboratory |journal=Journal of Pathology Informatics |author=Baron, J.M.; Dighe, A.S. |volume=2 |pages=35 |year=2011 |doi=10.4103/2153-3539.83740 |pmid=21886891 |pmc=PMC3162747}}</ref>, which is often a part of a larger [[electronic health record]] system. However, such comprehensive electronic health record systems have low penetration in low-resource settings where the burden of disease is high and laboratory errors are further exacerbated by poor infrastructure, shortages in trained workforce, and [[information]]al challenges.<ref name="BecichInform00" /><ref name="PetroseAss16">{{cite journal |title=Assessing Perceived Challenges to Laboratory Testing at a Malawian Referral Hospital |journal=American Journal of Tropical Medicine and Hygiene |author=Petrose, L.G.; Fisher, A.M.; Douglas, G.P. et al. |volume=94 |issue=6 |pages=1426-32 |year=2016 |doi=10.4269/ajtmh.15-0867 |pmid=27022150 |pmc=PMC3162747}}</ref>
 
Although [[laboratory information system]]s (LIS) have been shown to help reduce laboratory errors, little information is available on the implementation of these in low-resource settings. Furthermore, most descriptions of LIS implementations in low-resource settings focus on the analytical phase of the total testing process.<ref name="VempalaC4G16">{{cite journal |title=C4G BLIS: Health Care Delivery via Iterative Collaborative Design in Resource-constrained Settings |journal=Proceedings of the Eighth International Conference on Information and Communication Technologies and Development |author=Vempala, S.; Chopra, N.; Rajagopal, A. et al. |at=21 |year=2016 |doi=10.1145/2909609.2909657}}</ref> In this article, we describe preliminary work in developing a LIS that addresses problems using [[Informatics (academic field)|informatics]] interventions to support all phases of the total testing process in a low-resource setting with no pre-existing CPOE system.
 
==Methods==
===Ethical considerations===
The work associate with this article followed all ethical standards for research without direct contact with human or animal subjects.
 
===Setting===
We implemented a LIS at the Kamuzu Central Hospital (KCH) in Lilongwe, Malawi, between January and March 2015. The Kamuzu Central Hospital is a 750-bed government-operated referral hospital. The laboratory at KCH comprises eight departments: microbiology, parasitology, serology, [[hematology]], molecular biology, blood bank, flow cytometry, and biochemistry. These departments perform laboratory tests for both outpatients and inpatients at the hospital and conducted 242,242 tests between July 1, 2010 and June 30, 2011.<ref name="DriessenInform15">{{cite journal |title=Informatics solutions for bridging the gap between clinical and laboratory services in a low-resource setting |journal=African Journal of Laboratory Medicine |author=Driessen, J.; Limula, H.; Gadabu, O.J. et al. |volume=4 |issue=1 |at=176 |year=2015 |doi=10.4102/ajlm.v4i1.176}}</ref> The system described in this article was piloted in the outpatient tuberculosis clinic of the hospital and the microbiology department of the laboratory starting on March 31, 2015.
 
===User requirements and system capabilities===
Requirements for the LIS were provided by laboratory technicians in the form of user stories. A user story is a succinct way of representing a task that a user will want to perform using an information resource.<ref name="CohnUser05">{{cite book |title=User Stories Applied: For Agile Software Development |author=Cohn, M. |publisher=Addison-Wesley Professional |edition=1st |year=2004 |isbn=9780321205681}}</ref> It includes the role of the user, the task or action, and the benefit, goal, or achievement. An example of a user story in this context is:
 
:''As a laboratory technician, I want to know which specimen was drawn first so that I can prioritize it for analysis to reduce the number of non-viable specimens.''
 
We compiled a consolidated list of user stories for each phase of the total testing process. We used that list to define a set of functionality requirements from the laboratory technicians.
 
To ensure that no core functionality was omitted from the specifications, we leveraged the Laboratory Information System Functionality Assessment Toolkit (LIS-FAT) developed by the Association of Pathology Informatics. This assessment toolkit provides 850 declarative statements that describe the functions that a LIS should possess.<ref name="TuthillTheLab14">{{cite journal |title=The laboratory information system functionality assessment tool: Ensuring optimal software support for your laboratory |journal=Journal of Pathology Informatics |author=Tuthill, J.M.; Friedman, B.A.; Balis, U.J. et al. |volume=5 |issue=1 |page=7 |year=2014 |doi=10.4103/2153-3539.127819 |pmid=24741466 |pmc=PMC3986538}}</ref> An example of a functionality statement from LIS-FAT is:
 
:''A laboratory information system should provide intelligent sample labeling that groups samples based on the test to be done and prints them out.''
 
The LIS-FAT was originally intended for use as a LIS evaluation checklist. However, in our implementation, we repurposed it to define capabilities for the proposed system. Furthermore, we recognized that the LIS-FAT was primarily developed for use in a setting with adequate resources, and some aspects of it may not be well suited for a low-resource setting. We therefore assessed the LIS-FAT functionality statements and selected those that focused on direct user needs and were most applicable in a low-resource setting. Special effort was made to ensure that major functional categories of the LIS-FAT were not overlooked. This resulted in a customized LIS-FAT applicable to a low-resource setting, with the declarative statements describing the core requirements for LIS in this setting.
 
To elucidate the dependencies that could drive the design phase, all functionality statements created in this step were grouped into high, medium, and low priority categories by a group of laboratory management personnel. This helped determine the order in which the functionality would be implemented to ensure that the most important functionality was implemented first.
 
===System design and development===
Laboratory information system software can be commercial, open-source, or home-grown. We chose to build on existing open-source LIS software and customize it based on our functional requirements. Before any functionality was implemented, we conducted a design validation study of two open-source LIS software systems to determine the extent to which they implemented the required functionality for the KCH laboratory.<ref name="FriedmanEval06">{{cite book |title=Evaluation Methods in Biomedical Informatics |editor=Friedman, C.P.; Wyatt, J. |publisher=Springer-Verlag |year=2006 |isbn=9780387306773 |doi=10.1007/0-387-30677-3}}</ref> These systems were Open Enterprise Laboratory Information System ([[OpenELIS]]) and Basic Laboratory Information System ([[C4G BLIS]]).<ref name="VempalaC4G16" /><ref name="MonuDesign10">{{cite web |url=http://hdl.handle.net/1853/34792 |title=Design and implementation of a basic laboratory information system for resource-limited settings |author=Monu, R. |work=SMARTech: Georgia Tech Theses and Dissertations |date=27 May 2010}}</ref> We assessed and ranked the systems based on the number of functionality requirements that they satisfied for the LIS implementation at KCH. A functionality requirement was considered satisfied if the LIS had a feature that could be used to achieve the goal of that requirement. The choice between the systems was based on the total number of required functions that each of the systems possessed. The system with the most functionality requirements was selected as the foundation upon which the LIS implementation at KCH would be built, and a comprehensive design was made around it to ensure that all functional requirements were met.


==References==
==References==

Revision as of 21:19, 16 March 2020

Full article title Design and implementation of a clinical laboratory information system in a low-resource setting
Journal African Journal of Laboratory Medicine
Author(s) Mtonga, Timothy M.; Choonara, Faheema E.; Espino, Jeremy U.; Kachaje, Chimwemwe; Kapundi, Kenneth;
Mengezi, Takondwa E.; Mumba, Soyapi L.; Douglas, Gerald P.
Author affiliation(s) University of Pittsburgh, Kamuzu Central Hospital, Baobab Health Trust
Primary contact Email: tmm113 at pitt dot edu
Year published 2019
Volume and issue 8(1)
Article # a841
DOI 10.4102/ajlm.v8i1.841
ISSN 2225-2010
Distribution license Creative Commons Attribution 4.0 International
Website https://ajlmonline.org/index.php/ajlm/article/view/841/1391
Download None
Emblem-important-yellow.svg This article should be considered a work in progress and incomplete. Consider this article incomplete until this notice is removed.

Abstract

Background: Reducing laboratory errors presents a significant opportunity for both cost reduction and healthcare quality improvement. This is particularly true in low-resource settings where laboratory errors are further exacerbated by poor infrastructure and shortages in a trained workforce. informatics interventions can be used to address some of the sources of laboratory errors.

Objectives: This article describes the development process for a clinical laboratory information system (LIS) that leverages informatics interventions to address problems in the laboratory testing process at a hospital in a low-resource setting.

Methods: We designed interventions using informatics methods for previously identified problems in the laboratory testing process at a clinical laboratory in a low-resource setting. First, we reviewed a pre-existing LIS functionality assessment toolkit and consulted with laboratory personnel. This provided requirements that were developed into a LIS with interventions designed to address the problems that had been identified. We piloted the LIS at the Kamuzu Central Hospital in Lilongwe, Malawi.

Results: We implemented a series of informatics interventions in the form of a LIS to address sources of laboratory errors and support the entire laboratory testing process. Custom hardware was built to support the ordering of laboratory tests and review of laboratory test results.

Conclusion: Our experience highlights the potential of using informatics interventions to address systemic problems in the laboratory testing process in low-resource settings. Implementing these interventions may require innovation of new hardware to address various contextual issues. We strongly encourage thorough testing of such innovations to reduce the risk of failure when implemented.

Keywords: low-resource setting, laboratory testing, laboratory information system, Malawi, informatics interventions

Introduction

Laboratory testing plays a vital role in clinical decision-making. It is estimated that up to 70% of medical decisions in high-resource healthcare settings are made based on clinical laboratory test results.[1][2] Even though access to clinical laboratory services is comparatively lower in low-resource settings, studies show that clinicians in low-resource settings also make most decisions based on laboratory testing.[3][4] Despite the importance of laboratory test results in clinical decision-making, little effort has been made in low-resource settings to improve the entire laboratory testing process, which starts when the test is first ordered and ends when the results are interpreted and a clinical decision is made.[5]

Laboratory errors include a wide variety of mistakes in the testing process and have no universally accepted definition. We define a laboratory error as any event or mistake that leads to failure to perform a laboratory test, misdiagnosis of a laboratory test, or delayed reporting of laboratory test results. In 2001, it was estimated that laboratory errors accounted for $200 to $400 million in American healthcare expenditures per annum.[6] Since then, the rate of utilization of laboratory services has increased, making the reduction of laboratory errors a significant opportunity for cost reduction and healthcare quality improvement.

Recent studies have tried to categorize errors using phases of the total testing process, which comprises pre-analytical, analytical, and post-analytical phases.[7] The pre-analytical phase covers all activities from when the test is ordered to when the specimen is delivered to the laboratory for testing. The analytical phase covers the activities involved in the actual testing of the specimen, and the post-analytical phase involves the reporting and interpretation of the laboratory result. Among the phases of the total testing process, it has been observed that most laboratory errors happen outside of the analytical phase.[8] An example of an error outside the analytical phase is the mislabeling of a specimen, which could happen during the drawing of a sample in the pre-analytical phase. While error rates vary between health facilities, it is estimated that 32% to 75% of all laboratory errors happen in the pre-analytical phase.[9] Error rates in the analytical phase are estimated in the range of 13% to 32% and in the post-analytical phase in the range of 9% to 31%.[9]

Informatics interventions may be useful in reducing such laboratory errors. Examples of such interventions are computer-aided ordering of laboratory tests, barcode labeling of specimen tubes, and automated reporting of laboratory test results. These interventions are often provided using computer systems that allow physicians to order diagnostic tests, medications, and other procedures, commonly referred to as computerized provider order entry (CPOE)[10], which is often a part of a larger electronic health record system. However, such comprehensive electronic health record systems have low penetration in low-resource settings where the burden of disease is high and laboratory errors are further exacerbated by poor infrastructure, shortages in trained workforce, and informational challenges.[1][11]

Although laboratory information systems (LIS) have been shown to help reduce laboratory errors, little information is available on the implementation of these in low-resource settings. Furthermore, most descriptions of LIS implementations in low-resource settings focus on the analytical phase of the total testing process.[12] In this article, we describe preliminary work in developing a LIS that addresses problems using informatics interventions to support all phases of the total testing process in a low-resource setting with no pre-existing CPOE system.

Methods

Ethical considerations

The work associate with this article followed all ethical standards for research without direct contact with human or animal subjects.

Setting

We implemented a LIS at the Kamuzu Central Hospital (KCH) in Lilongwe, Malawi, between January and March 2015. The Kamuzu Central Hospital is a 750-bed government-operated referral hospital. The laboratory at KCH comprises eight departments: microbiology, parasitology, serology, hematology, molecular biology, blood bank, flow cytometry, and biochemistry. These departments perform laboratory tests for both outpatients and inpatients at the hospital and conducted 242,242 tests between July 1, 2010 and June 30, 2011.[13] The system described in this article was piloted in the outpatient tuberculosis clinic of the hospital and the microbiology department of the laboratory starting on March 31, 2015.

User requirements and system capabilities

Requirements for the LIS were provided by laboratory technicians in the form of user stories. A user story is a succinct way of representing a task that a user will want to perform using an information resource.[14] It includes the role of the user, the task or action, and the benefit, goal, or achievement. An example of a user story in this context is:

As a laboratory technician, I want to know which specimen was drawn first so that I can prioritize it for analysis to reduce the number of non-viable specimens.

We compiled a consolidated list of user stories for each phase of the total testing process. We used that list to define a set of functionality requirements from the laboratory technicians.

To ensure that no core functionality was omitted from the specifications, we leveraged the Laboratory Information System Functionality Assessment Toolkit (LIS-FAT) developed by the Association of Pathology Informatics. This assessment toolkit provides 850 declarative statements that describe the functions that a LIS should possess.[15] An example of a functionality statement from LIS-FAT is:

A laboratory information system should provide intelligent sample labeling that groups samples based on the test to be done and prints them out.

The LIS-FAT was originally intended for use as a LIS evaluation checklist. However, in our implementation, we repurposed it to define capabilities for the proposed system. Furthermore, we recognized that the LIS-FAT was primarily developed for use in a setting with adequate resources, and some aspects of it may not be well suited for a low-resource setting. We therefore assessed the LIS-FAT functionality statements and selected those that focused on direct user needs and were most applicable in a low-resource setting. Special effort was made to ensure that major functional categories of the LIS-FAT were not overlooked. This resulted in a customized LIS-FAT applicable to a low-resource setting, with the declarative statements describing the core requirements for LIS in this setting.

To elucidate the dependencies that could drive the design phase, all functionality statements created in this step were grouped into high, medium, and low priority categories by a group of laboratory management personnel. This helped determine the order in which the functionality would be implemented to ensure that the most important functionality was implemented first.

System design and development

Laboratory information system software can be commercial, open-source, or home-grown. We chose to build on existing open-source LIS software and customize it based on our functional requirements. Before any functionality was implemented, we conducted a design validation study of two open-source LIS software systems to determine the extent to which they implemented the required functionality for the KCH laboratory.[16] These systems were Open Enterprise Laboratory Information System (OpenELIS) and Basic Laboratory Information System (C4G BLIS).[12][17] We assessed and ranked the systems based on the number of functionality requirements that they satisfied for the LIS implementation at KCH. A functionality requirement was considered satisfied if the LIS had a feature that could be used to achieve the goal of that requirement. The choice between the systems was based on the total number of required functions that each of the systems possessed. The system with the most functionality requirements was selected as the foundation upon which the LIS implementation at KCH would be built, and a comprehensive design was made around it to ensure that all functional requirements were met.

References

  1. ↑ 1.0 1.1 Becich, M.J. (2000). "Information management: moving from test results to clinical information". Clinical Leadership & Management Review 14 (6): 296–300. PMID 11210218. 
  2. ↑ Hallworth, M.J. (2011). "The '70% claim': what is the evidence base?". Annals of Clinical Biochemistry 48 (Pt. 6): 487–8. doi:10.1258/acb.2011.011177. PMID 22045648. 
  3. ↑ Wilson, M.L.; Fleming, K.A.; Kuti, M.A. et al. (2018). "Access to pathology and laboratory medicine services: A crucial gap". Lancet 391 (10133): 1927–38. doi:10.1016/S0140-6736(18)30458-6. PMID 29550029. 
  4. ↑ Moyo, K.;, Porter, C.; Chilima, B. et al. (2015). "Use of laboratory test results in patient management by clinicians in Malawi". African Journal of Laboratory Medicine 4 (1): 277. doi:10.4102/ajlm.v4i1.277. PMC PMC4870597. PMID 27213139. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4870597. 
  5. ↑ Price, C.P.; John, A.S.; Christenson, R. et al. (2016). "Leveraging the real value of laboratory medicine with the value proposition". Clinical Chimica Acta 462: 183–6. doi:10.1016/j.cca.2016.09.006. PMID 27649855. 
  6. ↑ Bologna, L.; Hardy, G.; Mutter, M. (2001). "Reducing specimen and medication error with handheld technology". Proceedings of the 2001 HIMSS Annual Conference. 
  7. ↑ Plebani, M. (2009). "Exploring the iceberg of errors in laboratory medicine". Clinical Chimica Acta 404 (1): 16–23. doi:10.1016/j.cca.2009.03.022. PMID 19302995. 
  8. ↑ Plebani, M. (2006). "Errors in clinical laboratories or errors in laboratory medicine?". Clinical Chemistry and Laboratory Medicine 44 (6): 750–9. doi:10.1515/CCLM.2006.123. PMID 16729864. 
  9. ↑ 9.0 9.1 The Lewin Group (May 2008). "Laboratory Medicine: A National Status Report" (PDF). Centers for Disease Control and Prevention. https://www.cdc.gov/labbestpractices/pdfs/2007-status-report-laboratory_medicine_-_a_national_status_report_from_the_lewin_group_updated_2008-9.pdf. 
  10. ↑ Baron, J.M.; Dighe, A.S. (2011). "Computerized provider order entry in the clinical laboratory". Journal of Pathology Informatics 2: 35. doi:10.4103/2153-3539.83740. PMC PMC3162747. PMID 21886891. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3162747. 
  11. ↑ Petrose, L.G.; Fisher, A.M.; Douglas, G.P. et al. (2016). "Assessing Perceived Challenges to Laboratory Testing at a Malawian Referral Hospital". American Journal of Tropical Medicine and Hygiene 94 (6): 1426-32. doi:10.4269/ajtmh.15-0867. PMC PMC3162747. PMID 27022150. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3162747. 
  12. ↑ 12.0 12.1 Vempala, S.; Chopra, N.; Rajagopal, A. et al. (2016). "C4G BLIS: Health Care Delivery via Iterative Collaborative Design in Resource-constrained Settings". Proceedings of the Eighth International Conference on Information and Communication Technologies and Development: 21. doi:10.1145/2909609.2909657. 
  13. ↑ Driessen, J.; Limula, H.; Gadabu, O.J. et al. (2015). "Informatics solutions for bridging the gap between clinical and laboratory services in a low-resource setting". African Journal of Laboratory Medicine 4 (1): 176. doi:10.4102/ajlm.v4i1.176. 
  14. ↑ Cohn, M. (2004). User Stories Applied: For Agile Software Development (1st ed.). Addison-Wesley Professional. ISBN 9780321205681. 
  15. ↑ Tuthill, J.M.; Friedman, B.A.; Balis, U.J. et al. (2014). "The laboratory information system functionality assessment tool: Ensuring optimal software support for your laboratory". Journal of Pathology Informatics 5 (1): 7. doi:10.4103/2153-3539.127819. PMC PMC3986538. PMID 24741466. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3986538. 
  16. ↑ Friedman, C.P.; Wyatt, J., ed. (2006). Evaluation Methods in Biomedical Informatics. Springer-Verlag. doi:10.1007/0-387-30677-3. ISBN 9780387306773. 
  17. ↑ Monu, R. (27 May 2010). "Design and implementation of a basic laboratory information system for resource-limited settings". SMARTech: Georgia Tech Theses and Dissertations. http://hdl.handle.net/1853/34792. 

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. Grammar was cleaned up for smoother reading. In some cases important information was missing from the references, and that information was added. The original reference the author used for "Baldominos et al." was incorrect; the presumably correct citation was added in its place.

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