Design a wearable device for health workers.

CQ:

1. Goal - improve productivity & health quotient of health workers

2. Geography - India

3. Timelines & constraints - 3 months, NA

4. What's my role and what does the company do - PM in a tech company

5. Design from scratch - Yes

Users:

1. Frontline health workers (Doctors) at medical centres -> prioritising as this relates to the goal of the query

2. Support staff

3. Patients & attendants visiting the health workers

Pain Point/Use Case -> prioritising basis urgency of need & impact

1. Before meeting the patients

   1. Not aware of who I’m going to meet, how many I’m going to meet that can help manage my schedule -> P0

   2. Not aware of the health condition of the people whom I about to visit -> P1

2. During the meeting

   1. Going through the previous reports is time taking -> P0

   2. Possibility of human error during diagnosis -> P1

3. After meeting

   1. Reminders to patients during visit -> P1

Since we are designing the wearable from scratch, it makes sense to solve for multiple pain points or use cases

Recommendations:

1. Not aware of who I’m going to meet, how many I’m going to meet that can help manage my schedule

   1. List of patients lined up with a summary of ailment, and allotted time slot to be made available on the device -> P0 (H impact, M effort)

   2. Notifications when any new booking is made so that the practitioner also has option to accept or deny the request -> (L impact, M-H effort)

2. Going through the previous reports is time taking

   1. Scan the reports to generate a summary -> H impact, M-H effort

   2. Connect with the laboratories so that for any customer in case lab report updates, the doctor gets to see the status -> M impact, H effort

   3. Eases to understand the handwriting when sometimes it's difficult to understand what's written on the prescription reports -> M-H impact, M effort

Summary of solution:

1. List of patients lined up with a summary of ailment, and allotted time slot to be made available on the device

2. Scan the reports to generate a summary

3. Eases to understand the handwriting when sometimes it's difficult to understand what's written on the prescription reports

Tradeoffs:

1. Incorrect summary may lead to incorrect diagnosis -> generate confidence level of the generated summary so that health worker also cross checks

Metrics:

1. Acquisition:

   1. #devices sold per day/week/month

2. Activation:

   1. Signup on the device

3. Engagement:

   1. DAU/WAU/MAU

4. Retention:

   1. Churn rate

   2. Reactivation of 7 day dormant users