LinkFactor

Healthcare platform that brings patients and healthcare providers closer together

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Inspiration

We would like to build a healthcare platform that bring patients and healthcare providers closer together through utilizing modernized approaches to track and record symptom monitoring while harnessing the potential of real-world data.

What it does

LinkFactor utilizes a system that applies a very standards compliant implementation of the FHIR Questionnaires to track and record data about patients. Patients are asked for consent to share data and to be recontacted in the future in the survey form.

Further, LinkFactor provides real time virtual assistant chatbot that asks patients about their symptoms, records the data, and provides advice for the symptoms. There is also virtual meeting room service with video calling for connecting the patients with doctors for consultation and symptoms tracking.

How we built it

  • We built LinkFactor using Medplum to get data from patients, clinicians, staff and other stakeholders using custom forms. Forms go by questionnaires to collect data from a user in a standard way. Medplum supports collecting form data in a standards compliant way and using it to enforce QA or ensure conformance. Forms can be completed by practitioners when they meet patients. The Medplum Forms implementation is a very standards compliant implementation of the FHIR Questionnaires.
  • We built video meeting features using HTML, CSS, Javascript and Agora.
  • We built the AI Chatbot using AWS AI. Claude (LLM) allowed us to analyze text inputs of the users. The combination of Claude (LLM) and AWS AI provides a comprehensive and secure platform for web development.
  • We built the website using HTML, CSS, and Javascript

Consent & Recontact

Ask patients for consent to share data and recontact for research in data questionnaire form and before using the services

Patient's Json Record Example

{ "id": "b8378513-e0b1-4c51-aaa0-eb8cecb3e517", "meta": { "profile": [ "http://hl7.org/fhir/us/core/StructureDefinition/us-core-patient" ], "versionId": "0ddf2c30-b540-400d-b6a9-130d54f6fa7e", "lastUpdated": "2024-03-14T17:04:34.593Z", "author": { "reference": "Practitioner/cc2a80c7-8e8d-4990-b0b6-66197e5b6277", "display": "Mark Black" }, "project": "ea574d83-ea34-47c2-abf3-d839a909af77", "compartment": [ { "reference": "Project/ea574d83-ea34-47c2-abf3-d839a909af77" }, { "reference": "Patient/b8378513-e0b1-4c51-aaa0-eb8cecb3e517" } ] }, "text": { "status": "generated", "div": "

Generated by Synthea.Version identifier: v2.6.1-3-g50f4f58f\n . Person seed: 4286870567281389426 Population seed: 3 " }, "extension": [ { "extension": [ { "url": "ombCategory", "valueCoding": { "system": "urn:oid:2.16.840.1.113883.6.238", "code": "2106-3", "display": "White" } }, { "url": "text", "valueString": "White" } ], "url": "http://hl7.org/fhir/us/core/StructureDefinition/us-core-race" }, { "extension": [ { "url": "ombCategory", "valueCoding": { "system": "urn:oid:2.16.840.1.113883.6.238", "code": "2186-5", "display": "Non Hispanic or Latino" } }, { "url": "text", "valueString": "Non Hispanic or Latino" } ], "url": "http://hl7.org/fhir/us/core/StructureDefinition/us-core-ethnicity" }, { "url": "http://hl7.org/fhir/StructureDefinition/patient-mothersMaidenName", "valueString": "Christen366 Murray856" }, { "url": "http://hl7.org/fhir/us/core/StructureDefinition/us-core-birthsex", "valueCode": "M" }, { "url": "http://hl7.org/fhir/StructureDefinition/patient-birthPlace", "valueAddress": { "city": "Springfield", "state": "Massachusetts", "country": "US" } }, { "url": "http://hl7.org/fhir/us/core/StructureDefinition/us-core-genderIdentity", "valueCodeableConcept": { "coding": [ { "system": "http://snomed.info/sct", "code": "446151000124109", "display": "Identifies as male gender (finding)" } ] } } ], "identifier": [ { "system": "https://github.com/synthetichealth/synthea", "value": "e91975f5-9445-c11f-cabf-c3c6dae161f2" }, { "type": { "coding": [ { "system": "http://terminology.hl7.org/CodeSystem/v2-0203", "code": "MR", "display": "Medical Record Number" } ], "text": "Medical Record Number" }, "system": "http://hospital.smarthealthit.org", "value": "e91975f5-9445-c11f-cabf-c3c6dae161f2" }, { "type": { "coding": [ { "system": "http://terminology.hl7.org/CodeSystem/v2-0203", "code": "SS", "display": "Social Security Number" } ], "text": "Social Security Number" }, "system": "http://hl7.org/fhir/sid/us-ssn", "value": "999-61-9797" }, { "type": { "coding": [ { "system": "http://terminology.hl7.org/CodeSystem/v2-0203", "code": "DL", "display": "Driver's License" } ], "text": "Driver's License" }, "system": "urn:oid:2.16.840.1.113883.4.3.25", "value": "S99940283" } ], "name": [ { "use": "official", "family": "Ritchie586", "given": [ "Dustin31" ], "prefix": [ "Mr." ] }, { "use": "old", "family": "Ritchie586", "given": [ "John43" ], "prefix": [ "Mr." ], "period": { "start": "1940-09-05", "end": "1962-04-30" } } ], "telecom": [ { "system": "phone", "value": "555-770-2787", "use": "home" } ], "gender": "male", "birthDate": "1940-09-05", "address": [ { "extension": [ { "extension": [ { "url": "latitude", "valueDecimal": 42.1343534042923 }, { "url": "longitude", "valueDecimal": -72.67217549422628 } ], "url": "http://hl7.org/fhir/StructureDefinition/geolocation" } ], "line": [ "599 Schowalter Promenade" ], "city": "West Springfield", "state": "MA", "postalCode": "01089", "country": "US", "period": { "start": "1940-09-05" } } ], "maritalStatus": { "coding": [ { "system": "http://terminology.hl7.org/CodeSystem/v3-MaritalStatus", "code": "M", "display": "M" } ], "text": "M" }, "multipleBirthBoolean": false, "communication": [ { "language": { "coding": [ { "system": "urn:ietf:bcp:47", "code": "en-US", "display": "English" } ], "text": "English" } } ], "resourceType": "Patient" }

Challenges we ran into

We had challenges to put everything together to meet the deadline.

Accomplishments that we're proud of

We were able to build forms that are FHIR standards. We were able to set a system in Medplum that collects data from patients in FHIR compliance. We were able to build the video meeting service as well as AI Chatbot. We were able to develop a process to ask patient's for their consent to share data.

What we learned

We learnt about FHIR standards and the way health care app should be developed. We learnt to build video meeting using Agora as well as AI Chatbot using Claude LLM.

What's next for LinkFactor

  • We would like to improve the security of the web page and the system that stores data from users.
  • We want to add more functions that allow doctors and patients closer together such as real time chat with doctors
  • We want to offer game like dashboard that encourages patients to fill info

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