Amira A. Pettus
DSC_0555 copy.JPG

Community Health Worker Inception Workshop


Community Health Worker Inception

While I was on the Bahmni account, our main client, a hospital named Jan Swasthya Sahyog (JSS) asked our team to perform a fitment analysis to determine if the EMR that it was using, Bahmni, could be extended to an EHR. The hospital wasn’t just a health care provider, but also acted a parallel system of governance. Using a slew of community health workers, it collected census and  individual health data, ran neonatal care program, 1000 day program, nutritional programs, and educational programs etc.)  The administration was looking for single solution for their many program needs.

A small group from our team led a 1 week inception workshop to do that, as well as to discern if the Bahmni could be utilized by community health workers in subcenters and when performing

Our challenge that week was to identify and distill the true needs of the hospital and community health workers and determine areas our team could help.

Location & Duration : Chhattisgarh, India for 1 week


Discovering the real Problem & Resetting Expectations

On the first day of our workshop, in our morning session with a subset of doctors, nurses, administrators, and community health workers, we quickly learned that the group was facing three main problems:

1   Administrators needed a way to collect big data on the communities that they supported, so that it could be used to support their white papers. These papers were then used to show the hospital's need for funds to the state government and outside organization.

2   Community health workers needed a more efficient way to collect patient data and track patient medical history in both the hospital and in subcenters. ( They were currently using a mixture of processes, as well as both physical and digital collection methods.)

3   The hospital overall needed an overhaul of their collection process for each program. Different program groups were using uniform processes to collect data and follow up with patients no matter what their condition was. So the process would be the same whether the study was for pregnancy or TB.  However it was becoming clear that each condition needed a specially designed data collection process.



Day 1 Pivoting Our Plan

1   We ran an activity to understand the goals of the hospital and the community health workers. 

2   My pair and I facilitated an exercise to  learn about the group's need and the context of the various programs that they ran. Through it we uncovered the three aforementioned insights.  


3   In the last activity of the day my team and I realized that we had to pivot our approach. We worked to get agreement and identify the group's priorities. Unresolved differences between admin and community health workers resulted in our moving forward with a much larger and ambiguous scope.  We hoped that through our research we could build consensus around a common set of needs.


Day 2 Mapping the Community Health Worker's Process

1   On the second day of our workshop we learned about the entire set of services and then worked with the individual coordinators of each program to break down each of the processes.


2   We looked for similarities, differences, and overlaps in the community health workers' approaches to the healthcare they provided, the data they collected, and how they collected it.

3   We also identified gap, inefficiencies, and pain points in their processes.  

Overall we mapped the processes for 9 chronic disease, Antenatal Clinic services like Phulwari, Early infancy and postpartum mother care programme, and census collection. 

We learned through the mapping that the indicators were being measured in an ad hoc way and not uniformly over a pre-set period of time. This meant that a larger set of data had to be gathered to answer unexpected questions every month from the administration. This set up placed a burden on the community health workers and potentially was the reason why more targeted data collection processes were not being made for each program.


Day 3 Visiting Outreach clinics

1   Our team visited two subcenters, one for ANC and the other for a children's nutritional program. The goal was to see the programs for ourselves, validate our processes mapping, and to fill in some gaps in the details.

We learned that some health workers were illiterate and processes had been instituted to accommodate them. The leaders of the programs were public health workers but a large portion of the teams were actually informally trained members of the community. 


Day 4 Ride Alongs with COmmunity Health Workers

1   We had the opportunity to go on "ride alongs" with community health workers in the villages the live ands work in.

The ones we interviewed focused on preventative health, census collection, and house hold surveys on pregnancies and nutrition.


. And they gave out non class H (non-antibiotics) and referred patients to nearest subcenter as they saw fit. They also collected and maintained data on this areas. All of the data was recorded by hand, in thick yellow books, and  were routinely collected and updated.The community health workers in this program were all women and meant regularly to discuss, share case studies of their patients, and up skill.  


Day 5 Understand how community data is analyzed and utilized

1  The last day of the workshop we observed the steps after data is collected in the field, specifically around how this data is assimilated and brought back to the main hospital for data entry into spreadsheets.

2  Our team also observed how this data helped in analysis and report creation and how it all supported decision making in the hospital and programs.


Day 6 Presented OPtions for Next Steps

1   At the end of the week our team presented the results of our findings to the original group of hospital administrators, doctors, nurses, and community health workers.

2  We facilitated a conversation about the hospital's priorities and presented options for next steps.  


Ultimately the administration decided that creating a solution for their big data collection needs was most beneficial for the hospital.  


After the Inception

After the inception, the project was put on hold for about a year and another team oversaw the solution development.