A Model for Improving Infection Detection Accuracy through Integrated Laboratory Information Systems

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@article{1723089,
      author = {Kazeem Abdulrazaq, Habeeb Damilola Yusuf, Helen Ekwi Osinem, Florence Eribenne},
      title = {A Model for Improving Infection Detection Accuracy through Integrated Laboratory Information Systems},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {3},
      number = {6},
      pages = {650-675},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723089.pdf},
      abstract = {Infection detection depends on laboratory data that is usually scattered across analyser interfaces, standalone laboratory information systems, paper registers, medical records and notification channels that do not exchange information with one another. Detection is consequently slow, incomplete, and prone to both missed cases and false alarms. This paper develops a layered model, the Integrated Laboratory Information System for Infection Detection Accuracy (ILIS-IDA), which combines semantic standardisation of laboratory data, cross-source record linkage, and a hybrid analytic engine pairing deterministic case definitions with a supervised classifier whose disagreement with those definitions is treated as diagnostic information. Developed through design science research, the model spans six layers running from data acquisition through terminology binding, identity resolution, hybrid detection, alerting and governance, and is situated within work on diagnostic laboratory infrastructure, electronic laboratory reporting, automated surveillance of healthcare-associated infections, and health data protection. An accompanying evaluation framework measures diagnostic accuracy, timeliness, completeness and operational burden together, and is demonstrated on synthetic data. The central argument is that accuracy gains come less from any single algorithm than from the quality and integration of the pipeline that feeds it.},
      keywords = {laboratory information system, infection detection, diagnostic accuracy, health information exchange, interoperability, electronic laboratory reporting, clinical decision support, disease surveillance.},
      month = {December},
  }