Have you ever spent hours in a doctor's office or emergency room, waiting to be seen? Or how about being placed on hold while you wait to speak to a nurse about a new prescription?

People living with chronic disease frequently have these experiences as they struggle to manage their health.

Doctors want to provide the best care possible but they are handicapped by a system where it takes weeks to schedule an appointment.

In the US, there are over 133 million people with a chronic disease. And the problem will get worse. That number is predicted to grow to 157 million by 2020.


Introducing CoCoCare, providing collaborative continuous care managed by a medical care team on Cisco Spark.

CoCoCare improves patient care by giving the individual immediate access to a medical care team. Patients communicate with the care team through Facebook Messenger, while their care team manages the conversation in private spaces on Cisco Spark. This increases engagement and efficiency in care.

CoCoCare is designed for chronic disease patient and their physicians. Chronic disease patients require constant monitoring of their disease and medications. CoCoCare allows the chronic disease practice to more efficiently and effectively handle large numbers of patients.

Cisco Spark meets HIPAA privacy and security requirements through end to end encryption.

CoCocare improves patient care, increases patient engagement, reduces costs while improving the physician’s bottom line.


  1. Patients are able to connect with their medical care team through Facebook Messenger.
  2. Our bot sends the message to the medical care team private room on Cisco Spark.
  3. The medical team reads and responds to patient's message
  4. Human in the loop/augmented AI provides safety to patients by including human intelligence instead of just a bot as in most products
  5. Doctors become more efficient through the application of chatbots, augmented intelligence and Cisco Spark
  6. End to end encryption provides security and privacy meeting HIPAA requirements.
  7. Incorporates various health tech APIs to assist medical teams in performing evaluations

Why is it Unique

CoCoCare is not the typical diagnosis chatbot that identifies warts or the best treatment for a cold. A typical chatbot is not suitable for the complex management of chronic disease. CoCoCare uses a human in the loop approach. This augmented intelligence approach ensures patient safety by including medical professionals in the evaluation and resolution of patient's questions and requests.

Business model

CoCoCare is addressing a large opportunity by serving the most expensive patients with the greatest unmet need: Those with chronic conditions.

In the US alone, chronic diseases affect approximately 133 million people, representing more that 40% of the total population of the country. These numbers are projected to grow to an estimated 157 million, with 81 million having multiple conditions according to National Center for Health Statistics, Centers for Disease Control and Prevention . Many chronic disease patients are Medicare or Medicaid beneficiaries.

Under new 2017 CMS chronic care management codes, Patients with two or more chronic conditions are eligible for CMS' chronic care management (CCM) program. The included services are:

  • Use of a Certified Electronic Health Record (EHR)

  • Continuity of Care with Designated Care Team Member

  • Comprehensive Care Management and Care Planning

  • Transitional Care Management

  • Coordination with Home- and Community-Based Clinical Service Providers

  • 24/7 Access to Address Urgent Needs

  • Enhanced Communication (for example, email)

The following is a case example based on a medical group focused on chronic disease management under the new CMS CCM program:

  1. Group of 16 physicians and 9 APPs
  2. Patient panel of 30,000 Medicare Beneficiaries
  3. 30% are eligible beneficiaries
  4. Under the new rules, the practice generates an additional $409,000 a month ($4.9 million annually)
  5. CocoCare earns a subscription based on each patient

Product Walk through

Our patient is experiencing severe chronic pain. She has questions about whether she should increase her medication. Our patient goes to the CoCoCare Facebook page She sends a message to CoCoCare

P: I have severe chronic pain with my rheumatoid arthritis.

CocoCare bot creates a new space for the patient, P, in Cisco Spark. Any member of the medical care team can join the space. Dr. O joins the room. The CoCoCare Bot has automatically inserts the patient’s electronic health record information. In this instance, we have information pulled from Facebook about our patient as a stand in for the EHR. Dr. O reads the message patient sent through Facebook Messenger and sees the patient has rheumatoid arthritis and severe chronic pain.

Using the Infermedica Api, CoCoCare bot provides an analysis of the patient’s condition.

The patient sends a new message asking a question about medication.

Patient: Should I do a prednisone burst to stop this flareup?

Using the Medline Plus Api, CoCoCare provides information on the medication Prednisone. The doctor responds with advice about how to stop the flareup.

Dr. O: T, go ahead with the burst as per your last prescription. Then follow with lidocaine patches. Please let me know if you still have a flareup on Friday.

CoCoCare bot sends the message to patient on Facebook Messenger. P sends a new message thanking the doctor. CoCoCare provides the patient with peace of mind and clear medical advice thus averting a trip to the emergency room while providing additional revenue to the physician for chronic care management.


Checking the service out live requires access to the CocoCare Cisco Spark account. Contact us for an invitation and further instructions.

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posted an update

The project is progressing quickly. Our Facebook integration now provides an interactive survey within Messenger using Facebook's new Chat Extension API. Patients can invoke the symptom survey from Messenger to conveniently report treatment outcomes back to their medical care team on Cisco Spark. See the Github repo for substantial advances to the code.

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