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Development of a rules-based framework for patient-facing interpretation of laboratory results

HealthRadar is a physician-informed, rules-based framework designed to move laboratory reporting beyond isolated abnormality flags. This paper describes its architecture for integrating related biomarkers, identifying clinically meaningful physiologic patterns, stratifying severity and urgency, reducing contradictory messaging, and generating bounded patient-facing explanations. The framework is intended as an interpretive communication layer rather than a diagnostic or autonomous clinical decision-making system.

Working paper — not peer reviewed: This manuscript is an early-stage research publication and should not be treated as completed validation, evidence of clinical effectiveness, or medical advice.

Abstract

To describe development of a physician-informed, rules-based framework for contextual, patient-facing interpretation of routine laboratory testing. The framework was designed to address limitations of analyte-centric reporting by integrating biomarker relationships, prioritizing clinically meaningful signals, and clarifying overall urgency.

Keywords

  • Clinical laboratory information systems
  • Patient portals
  • Consumer health informatics
  • Health communication
  • Clinical decision support systems

Suggested citation

Rose EA, Rose SE. Development of a rules-based framework for patient-facing interpretation of laboratory results. HealthRadar Working Paper 001. 2026.

Stated limitations

The framework remains an early-stage prototype and has not undergone formal prospective clinical validation. The observations described in this manuscript are primarily conceptual and qualitative rather than outcome-based. Formal studies evaluating patient comprehension, effects on health-related anxiety, clinical appropriateness, interobserver agreement, and real-world workflow integration have not yet been completed.

References

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  2. Zhang Z, Citardi D, Xing A, et al. Patient Challenges and Needs in Comprehending Laboratory Test Results: Mixed Methods Study. J Med Internet Res. 2020;22(12):e18725.
  3. Steitz BD, Turer RW, Lin CT, et al. Perspectives of Patients About Immediate Access to Test Results Through an Online Patient Portal. JAMA Netw Open. 2023;6(3):e233572.
  4. Fraccaro P, Vigo M, Balatsoukas P, et al. Presentation of laboratory test results in patient portals: influence of interface design on risk interpretation and visual search behaviour. BMC Med Inform Decis Mak. 2018;18:11.
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  9. Ojeda Meixueiro VH, Perez-Campos Mayoral L, Hernandez Huerta MT, et al. Relevance of a Customized Version of ChatGPT Explaining Laboratory Test Results in Patient Education. J Med Educ Curric Dev. 2024;11:23821205241260239.
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  12. Zikmund-Fisher BJ, Scherer AM, et al. Effect of Harm Anchors in Visual Displays of Test Results on Patient Perceptions of Urgency About Near-Normal Values: Experimental Study. J Med Internet Res. 2018;20(3):e98.
  13. Wong KKL, Han Y, Cai Y, et al. From Trust in Automation to Trust in AI in Healthcare: A 30-Year Longitudinal Review and an Interdisciplinary Framework. Bioengineering (Basel). 2025;12(10).
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  15. Thayer JG, Franklin A, Miller JM, et al. A scoping review of rule-based clinical decision support malfunctions. J Am Med Inform Assoc. 2024;31(10):2405-2413.

Tags

  • Clinical laboratory information systems
  • Patient portals
  • Consumer health informatics
  • Health communication
  • Clinical decision support systems

Version archive

VersionDateStatusChange
1.0August 31, 2026PublishedInitial public version of Working Paper 001.
Research and development only: This material is not medical advice and must not be used for medical decisions.