Laboratory information system and necessary improvements in function and programming

Abstract Since the 1970s, computer supported data processing has been implemented in the laboratory and laboratory information systems (LIS) are being developed. In the following years, the programs were expanded and new laboratory requirements were inserted to the LIS. In the last few years, the requirements have grown more and more. The current tasks of the LIS are not only the management of laboratory requirements but also management of processes, data security and data transfer and they have become very important. Therefore, the current monolithic architecture of LIS has reached its limits. New methodologies like service oriented architecture, e.g. microservices, should be implemented. Thereby different specialized manufacturers provide software for one or a few tasks. These tasks can be more easily actualized like in the new field of agile software development. This new concept has been designed to provide updates and customer requirements according to its new organization structure in program development in a short time. For efficient data transfer, new interfaces and a standardization of master data like logical observation identifier names and codes (LOINC®) are advisable. With the growing data transfer, data security plays an increasingly important role. New concepts like blockchain programming (e.g. Medrec) are currently tested in (laboratory) medicine. To get an overview of the requirements of the own LIS, an Ishikawa diagram should be created. The main points of an Ishikawa diagram are shown and discussed. Based on the today-collected data, expert systems will be developed. For this kind of data mining, a structured data exchange is necessary.

[1]  Mi Jung Rho,et al.  Information System Success Model for Customer Relationship Management System in Health Promotion Centers , 2013, Healthcare informatics research.

[2]  Christian Lovis,et al.  Interoperability in Hospital Information Systems: a Return-On-Investment Study Comparing CPOE with and without Laboratory Integration , 2010, MIE.

[3]  D. Johnston,et al.  The impact of electronic health records on diagnosis , 2017, Diagnosis.

[4]  Dave McKenna,et al.  The Art of Scrum , 2016 .

[5]  Sungkee Lee,et al.  Comparison and Analysis of ISO/IEEE 11073, IHE PCD-01, and HL7 FHIR Messages for Personal Health Devices , 2018, Healthcare informatics research.

[6]  Brian E Dixon,et al.  Learning From the Crowd in Terminology Mapping: The LOINC Experience. , 2015, Laboratory medicine.

[7]  Harold R. Solbrig,et al.  A Consensus-Based Approach for Harmonizing the OHDSI Common Data Model with HL7 FHIR , 2017, MedInfo.

[8]  Andrew Lippman,et al.  MedRec: Using Blockchain for Medical Data Access and Permission Management , 2016, 2016 2nd International Conference on Open and Big Data (OBD).

[9]  Mario Plebani,et al.  Harmonization in laboratory medicine: the complete picture , 2013, Clinical chemistry and laboratory medicine.

[10]  Vincenzo Morabito,et al.  Business Innovation Through Blockchain , 2017 .

[11]  Jürgen Durner,et al.  Clinical chemistry: challenges for analytical chemistry and the nanosciences from medicine. , 2009, Angewandte Chemie.

[12]  Giuseppe Lippi,et al.  Risk management in the preanalytical phase of laboratory testing , 2007, Clinical chemistry and laboratory medicine.

[13]  Sima Ajami,et al.  Radio Frequency Identification (RFID) technology and patient safety , 2013, Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences.

[14]  Karen Y He,et al.  Big Data Analytics for Genomic Medicine , 2017, International journal of molecular sciences.

[15]  Jürgen Durner,et al.  The future of the laboratory information system – what are the requirements for a powerful system for a laboratory data management? , 2014, Clinical chemistry and laboratory medicine.

[16]  Michael Morrison,et al.  The European General Data Protection Regulation: challenges and considerations for iPSC researchers and biobanks , 2017, Regenerative medicine.

[17]  John D. Ainsworth,et al.  Enabling Patient Control of Personal Electronic Health Records Through Distributed Ledger Technology , 2017, MedInfo.

[18]  Pascal Borry,et al.  Rules for processing genetic data for research purposes in view of the new EU General Data Protection Regulation , 2018, European Journal of Human Genetics.

[19]  C. McDonald,et al.  LOINC, a universal standard for identifying laboratory observations: a 5-year update. , 2003, Clinical chemistry.

[20]  Peter Triantafillou,et al.  Making electronic health records support quality management: A narrative review , 2017, Int. J. Medical Informatics.

[21]  Donald S Young,et al.  The ideal laboratory information system. , 2013, Archives of pathology & laboratory medicine.

[22]  Hyeon-Eui Kim,et al.  Blockchain distributed ledger technologies for biomedical and health care applications , 2017, J. Am. Medical Informatics Assoc..

[23]  Nora Nikolac,et al.  Preanalytical quality improvement: in quality we trust , 2013, Clinical chemistry and laboratory medicine.

[24]  Ronald G. Hauser,et al.  Unit conversions between LOINC codes , 2018, J. Am. Medical Informatics Assoc..

[25]  Jacqueline Moss,et al.  Evidence-Based Guidelines for Interface Design for Data Entry in Electronic Health Records , 2018, Computers, informatics, nursing : CIN.

[26]  Ulysses G. J. Balis,et al.  The growing need for microservices in bioinformatics , 2016, Journal of pathology informatics.

[27]  Cloves Carneiro,et al.  Microservices From Day One , 2016, Apress.

[28]  M. Langarizadeh,et al.  Enhance hospital performance from intellectual capital to business intelligence. , 2013, Radiology management.

[29]  A. Šimundić,et al.  Preanalytical quality improvement: from dream to reality , 2011, Clinical chemistry and laboratory medicine.

[30]  R. Stewart,et al.  ‘Big data’ in mental health research: current status and emerging possibilities , 2016, Social Psychiatry and Psychiatric Epidemiology.