Assessing Blood-Based Laboratory Diagnostics for Alzheimers’s Disease: A Systems Approach

This thesis adopts a systems approach to analyze the complex network of stakeholders involved in adopting blood-based laboratory screening tests for Alzheimer’s disease (AD). Traditional diagnostic methods, including cerebrospinal fluid (CSF) testing and positron electron tomography (PET) brain imaging, are invasive, costly, and inaccessible to many. Blood-based tests offer a less invasive and more cost-effective alternative, yet they remain underutilized in clinical practice. By conducting a literature review, stakeholder interviews, and a Kano analysis, the thesis identifies and evaluates the key stakeholder needs to support the widespread adoption of these tests, such as the need for demonstrated clinical performance of these tests, reimbursement, broader education of patients and health care professionals, and safe, effective medicines to treat AD. The research highlights two emerging tests that have published studies demonstrating clinical validation, a key parameter of clinical performance. A stakeholder tension analysis is included with proposed tension resolutions using stakeholder saliency to guide prioritization. Addressing these stakeholder needs could facilitate broader implementation, improve early diagnosis, and support emerging therapeutic interventions for AD, thus reshaping the diagnostic landscape for this increasingly prevalent disease.

Name thesis:degree_name

Master

Department dc:contributor.department

System Design and Management Program.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Peralta Walker, Stephanie Christine

Advisors dc:contributor.advisor

• Rhodes, Donna H.
• Rubin, Joan S.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/159103

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/159103