Draft 2@AJND

Optimizing clinical complexity in neurodegenerative disorders using medical cognition tools

Abstract:

We illustrate conversational learning through our udhc global and local CBBLE groups around neurodegenerative disorder patients and topics as well as insights from their informational continuity through PaJR groups. We begin with a few Huntington's chorea patients family who approached us through our PaJR groups and finally traveled 2000 kms to meet us in our hospital where focused genomic studies were done that came positive for Huntington's following which our PaJR team tried to get in touch with an ongoing international clinical drug trial to see if they could register our patients. We further share our workflow around multiple neurodegenerative disorder patients to demonstrate how PaJR driven informational continuity has the potential to not just improve health professionals learning outcomes but also real patient outcomes

Keywords: neurodegenerative disorder, case-based blended learning ecosystem, Huntington disease.

Introduction:

Neurodegenerative disorders are a major public health and medical problem that impose substantial damage on people all over the world. The rate and frequency of these diseases increase significantly with age; consequently, the prevalence is anticipated to rise in the near future as life expectancies in many countries keep increasing.(Checkoway et al., 2011) Apart from the selective static neuronal loss seen in metabolic or toxic illnesses, the gradual loss of selectively susceptible neuronal populations characterises neurodegenerative disorders.(Dugger & Dickson, 2017) Pyramidal and extrapyramidal movement disorders, as well as cognitive or behavioural disorders, are the most prevalent clinical manifestations of neurodegenerative diseases. Few patients exhibit clear syndromes, while the majority have complex clinical characteristics.

Medical cognition tools, such as Patients Journey Recording System(PaJR), User Driven Health Care(UDHC), and Case Based Blended Learning Ecosystems (CBBLE) are playing a predominant role in resolving the clinical complexity(diagnostic and therapeutic uncertainty) of different neurodegenerative diseases. Tools like

synchronous face-to-face encounters and asynchronous communication and learning across numerous stakeholders in connected online space (UDHC, PaJR) are frequently utilised through different medical cognitive platforms and blended to construct CBBLE.(Biswas R, 2022b)

Some of the hallmarks of clinical complexity are the presence of ambiguity, non- linearity, and unpredictability while also exhibiting an overarching pattern that, with time, resolves itself via attractor states.(Plsek & Greenhalgh, 2001). As physician attractors, we are especially privileged to "be" with our patients regardless of their diagnosis. This is the only way we can know the outcomes of our patients, where our "being" with them is the most substantial and often

disregarded intervention.(Biswas R, 2022b)

User Driven Healthcare (UDHC) is a subclass of "Medical Cognition" in which numerous users, all healthcare stakeholders, including patients, interact online to comprehend and make decisions regarding meeting patient needs.(Purkayastha et al., 2015) This paper conceptualises coordinated patient care through the perspective of engaged stakeholders utilising information technology integration tools for digital infrastructures and also distinguish this paradigm from the ubiquitous conceptualization of dyadic relationships between clinician-patient, patient-nurse, and clinician-nurse, and provide the holistic integration of all stakeholder inputs, in the clinic and augmented by online communication in a multi- national context. Here, we will explore an illustration of user-driven health care (UDHC), a network of providers, patients, students, and researchers collaborating to improve neurodegenerative patient care. We will describe UDHC as well as its opportunities and challenges in care coordination to reduce costs, bring equity, and enhance care quality, and we'll also share relevant evidence in the field of neurodegenerative disease.

Case-based blended learning ecosystems (CBBLE) seek to provide a new perspective on accuracy-driven "ancient precision medicine" and to fortify the connection between ancient precision approaches and modern technology and omics-driven research.(Podder et al., 2018) Precision medicine is tailored, patient-specific care based on a patient's genetic makeup and medical records. This is crucial for optimising patient needs and outcomes, minimising harm to the healthcare ecosystem by limiting under- and overdiagnosis and treatment. At this juncture, CABBLE is the most important instrument for reducing under/overdiagnosis and under/overtreatment, as it is a practical method for doing so.

Patients Journey Recording System (PaJR), wherein the dyadic relationship between a single single patient and a doctor is transformed in order to provide a team of physicians for a single patient with comprehensive, patient-centered careElectronic records may only improve the accessibility to documentary evidence of incomplete assessment and inappropriate treatment, but they may not depict true quality of

healthcare that reflects respect for the patient, thorough follow-up and medical obligation for individual patients, and more in-depth research of the root causes of illness by teams of medical practitioners who have a professional relationship with the patient.(Biswas R, 2022a) In each of the PaJR groups, the patients are deidentified as well as anonymous, and the majority of them interact by sharing regular updates on the group as their own advocate without revealing their identity to the group. This ensures that no online traces of one‘s identification exist, and all members of the group take the uttermost precautions to protect the patient's privacy and thus the confidentiality is preserved with concern. To begin with, a trained students generate a de-identified, online-accessible patient case report as the initial step and before sharing as an open access case report, the patient would verify the anonymity of all details in the case report. To proceed with this, the patient's signed informed consent form is obtained. It is intended for patients to take command of their own recovery and become more knowledgeable about the science behind their illness journey.

The aforementioned cognitive tools contribute not only to resolving clinical complexity, but also to enhancing the clinical knowledge of health professionals through collective conversational learning on these platforms. Here, we present several of our neurodegenerative cases and illustrate how the these medical cognition tools are greatly influencing the optimisation of clinical complexity and uncertainty.

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Reference:
Biswas R. (2022a, September 2). User driven health care: Current PaJR

workflow and how to make the most of it for the patient and health professional team. User Driven Health Care. https://userdrivenhealthcare.blogspot.com/2022/09/current- pajr-workflow-and-how-to-make.html

Biswas R. (2022b, October 13). User driven health care: Medical Cognition tools to resolve clinical complexity: System 1 (heuristics,eyeballing) and system 2 (evidence based reflective sharing) toward a dynamic ontology in healthcare. User Driven Health Care. http://userdrivenhealthcare.blogspot.com/2022/10/medical- cognition-tools-to-resolve.html

Checkoway, H., Lundin, J. I., & Kelada, S. N. (2011). Neurodegenerative diseases. IARC Scientific Publications, 163, 407–419.

Dugger, B. N., & Dickson, D. W. (2017). Pathology of Neurodegenerative Diseases. Cold Spring Harbor Perspectives in Biology, 9(7), a028035. https://doi.org/10.1101/cshperspect.a028035

Plsek, P. E., & Greenhalgh, T. (2001). The challenge of complexity in health care. BMJ : British Medical Journal, 323(7313), 625–628.

Podder, V., Dhakal, B., Shaik, G. U. S., Sundar, K., Sivapuram, M. S., Chattu, V. K., & Biswas, R. (2018). Developing a Case-Based Blended Learning Ecosystem to Optimize Precision Medicine: Reducing Overdiagnosis and Overtreatment. Healthcare, 6(3), 78. https://doi.org/10.3390/healthcare6030078

Purkayastha, S., Price, A., Biswas, R., Jai Ganesh, A. U., & Otero, P. (2015). From Dyadic Ties to Information Infrastructures: Care-Coordination between Patients, Providers, Students and Researchers. Yearbook of Medical Informatics, 10(1), 68–74. https://doi.org/10.15265/IY-2015-008