Platform for perpetual clinical collaboration and innovation with patient communication using anonymized electronic health record data, clinical, and patient reported outcomes and data

A platform comprising:

• a) a server processor configured to provide a web-based data store comprising:
• i) a software module interfacing with a plurality of electronic medical records;
• and
• ii) a structured data store comprising a plurality of patient profiles, each patient profile comprising aggregated electronic medical records;
• b) a mobile processor configured to provide a mobile clinician application comprising:
• i) a software module presenting a group management interface allowing a project lead to define and edit a clinical project comprising:
• a patient profile and a plurality of clinicians including a treating physician;
• ii) a software module providing an interdisciplinary collaboration environment comprising:
• a clinician-facing messaging service, a document sharing service, a list of performed and upcoming clinical procedures, and a notification service for the plurality of clinicians and pertaining to the clinical project;
• and
• iii) a software module performing clinician engagement analytics;
• c) the server processor configured to provide a web-based patient portal comprising a software module providing a patient help center comprising:
• a patient-facing messaging service, the list of performed and upcoming clinical procedures, and a notification service for the patient and pertaining to the clinical project;
• and
• d) the server processor configured to provide an aggregated relevant decision driving engine comprising:
• i) a translation module configured to express the aggregated electronic medical records in a format that is human-readable and accessible to a native inference engine, a supervised machine learning algorithm, or both;
• ii) a notation module configured to express relationships between treatment steps to generate human-readable and human-editable node-and-arc diagrams and to transmit the diagrams as readable instructions to the native inference engine or the supervised machine learning algorithm;
• iii) an anonymization module configured to maintain separation between patient treatment or demographic records and private patient information comprising the identity of individual patients or of their treating physician, without implicitly or explicitly relying on trust in any party subject to attack by a malicious actor seeking such access, wherein the anonymization module utilizes a zero-trust-architecture distributed ledger system, via a circular queue, configured to apply an arbitrary number of cycles of decryption, authentication, encryption, and inalterable electronic tagging of incoming and outgoing packets of data based on identities of parties requesting to read or write data according to permissions assigned to each party;
• iv) the native inference engine configured to receive inputs from the translated medical records of a patient and a subset of interlinked treatment steps selected by the treating physician to generate outputs comprising predictions and probabilities;
• and
• v) the supervised machine learning algorithm configured to read and aggregate the translated medical records of multiple patients and combine them with one or more tumor registries to test hypotheses about the efficacy of potential courses of treatment for particular subsets pf patients.