AFI Dashboard - Design & Development

This data dashboard was set up for the Chikitsa Sahayta Kendra project in Odisha, India.

The Chikitsa Sahayta Kendra project (Health Outreach Center) in tribal villages of Odisha is using a comprehensive care delivery model to identify, treat and manage patients with high-quality care for primary health conditions through technology. The project, implemented by Arogya Foundation of India, aims at improving geographic and financial access to health care services to the patients in low-resource setting. The intervention is implemented in 44 remote villages in Pallahara block, in Angul District, Odisha. A total of 3 community health workers called Arogya Sanyojikas are provided with mobiles and telemedicine kits to facilitate teleconsults, counselling and referrals with 3 remote doctors. These CHWs are supported by 44 community volunteers, called Arogya Sevikas, who support them in screening, follow-up and tracking patient health improvement. Learn more about the project here.

AFI dashboard consists of 3 tabs:

Output Indicators
Process Indicators
Clinical Indicators

The entire data on the dashboard can sliced & diced on the basis of:

Year
Month

The details about the various charts incorporated in the dashboard are hereby given:

Sr. No.	Tab	Chart Name	Visualization Type	Datasource	Filter Conditions	Grouping	Metrices	Additional Settings
1	Output Indicators	Dashboard Filters	Filter Box	person_attribute	voided = 0		count(person_attribute_id)	Filter Control - Year, Month
2	Output Indicators	Total Visits	Big Number	visit	voided = 0		count(visit_id)	Number format = 0.4r
3	Output Indicators	Followup Visits	Big Number	obs	voided = 0 concept_id = 163212 SUBSTRING_INDEX(SUBSTRING(LEFT(value_text,40),5),'</b>',1) = 'Follow up visit'		count(*)	Number format = 0.3s
4	Output Indicators	Hypertension screening & Followup	Big Number	obs	voided = 0 concept_id = 163212 SUBSTRING_INDEX(SUBSTRING(LEFT(value_text,50),5),'</b>',1) in ('Hypertension screening and checkup','Hypertension follow up') location_id <> 2		count(*)	Number format = 0.3s
5	Output Indicators	Visit Trend	Time Pivot	visit	voided = 0		count(visit_id)	Time Column - date_created Time Grain - Month Frequency - Year Y axis format - 0.4r Y axis label - Number of Visits X axis label - Months
6	Output Indicators	Patient Registration	Time Pivot	patient_identifier	voided = 0		count(*)	Time Column - date_created Time Grain - Month Frequency - Year Y axis format - 0.3s Y axis label - Number of Patients X axis label - Months
7	Output Indicators	Gender wise Patient Distribution	Partition	Patient_Personal		Gender	count_distinct(patient_id)	Number format = 0.4r
8	Output Indicators	Age Wise Patient Distribution	Distributed Bar	Patient_Personal		AGEGRP	count_distinct(patient_id)	Time Column - date_created Time Grain - Month Bar Values - Y, Sort Bars - Y Y axis format - 0.3s Y axis label - Number of Patients X axis label - Age Group
9	Output Indicators	Patient - Social Status	Pie	Patient_Personal	person_attribute_type_id = 11	Value	count_distinct(patient_id)	Donut - Y Label Type - Category & Percentage Legend - T Show label - Y
10	Output Indicators	Patient - Economic Status	Pie	Patient_Personal	person_attribute_type_id = 12	Value	count_distinct(patient_id)	Donut - Y Label Type - Category & Percentage Legend - T Show label - Y
11	Output Indicators	Patient - Education Level	Pie	Patient_Personal	person_attribute_type_id = 13	Value	count_distinct(patient_id)	Donut - Y Label Type - Category & Percentage Legend - T Show label - Y
12	Output Indicators	Average Patient Experience	Time Pivot	PatientExperience			SUM(Weight)/SUM(PAT_WT)	Time Column - LASTDATE Time Grain - Month Frequency - Year Y axis format - 0.3s Y axis label - Experience Score X axis label - Months
13	Output Indicators	Patient Experience Comments	Pivot Table	obs	voided = 0 concept_id = 163344	Value	count_distinct(person_id)
14	Output Indicators	Top 10 Symptoms	Distributed Bar	obs	voided = 0 concept_id = 163212	symptoms	count(symptoms)	Bar Values = Y Y axis label - Number of Patients X axis label - Symptoms
15	Output Indicators	Diagnosis Provided by Doctor	Table	obs	voided = 0 concept_id = 163219	Value	count(obs_id)
16	Output Indicators	Doctor Average TAT	Time Pivot	DoctorAvgTAT			SUM(TotalMin)+SUM(HRS)	Time Column - LastDay Time Grain - Month Frequency - Year Y axis format - 0.4r Y axis label - Turnaround Time (In HRS) X axis label - Months
17	Process Indicators	Patient Registration Trend	Big Number with trendline	patient_identifier	voided = 0		count_distinct(patient_id)	Time Column - date_created Time Grain - Month Number format - 0.3s Show Trend Line - Y
18	Process Indicators	Clinis Wise Distribution	Time Stacked Area	Patient_Personal	name <> Remote	name	count_distinct(patient_id)	X axis label - Clinic wise Patient Distribution Y axis format - 0.3s Legend - Y
19	Process Indicators	Patient Distribution - Villagewise	Treemap	AFI_Village_Data		village	sum(patients)	Number Format - 0.4r
20	Process Indicators	Consultation Time (in Mins) by Health Worker	Distributed Bar	ConsultationTimeByHW		MonYear	count(Time_Group)	Breakdown - Time_Group X axis label - Months Y axis label - Visits Legend - Y, Stacked Bars - Y, Sort Bars - Y Y axis format - 0.3s
21	Process Indicators	Patient Experience	Distributed Bar	PatientExperience		MonYear	sum(patients)	Breakdown - Value_Text X axis label - Months Y axis label - Number of Patients Legend - Y, Stacked Bars - Y, Sort Bars - Y Y axis format - 0.3s
22	Process Indicators	Hour Wise Distribution	Distributed Bar	encounter	voided = 0 encounter_type = 1	Hour	count(patient_id)	X axis label - Hour of the Day Y axis label - Number of Patients Stacked Bars - Y, Sort Bars - Y Y axis format - 0.3s
23	Process Indicators	Patient Registered Per Day	Calendar Heat	patient_identifier	voided = 0		count(patient_id)	Domain - month, Subdomain - day Time Range - Last Quarter Show Values - Y, Number Format - 0.3s Time format - %d%m%Y \| 14/01/2019 color step - 4, cell size - 30
24	Clinical Indicators	Doctor Wise Distribution	Time Stacked Area	Doctorwisedistribution	username is not null Doctor <> Dr.Smith	Doctor	count_distinct(patients)	Time Column - Last_Date Time Grain - day X axis label - Month Y axis format - 0.3s Legend - Y
25	Clinical Indicators	Doctor Patient Interaction	Distributed Bar	visit_attribute	voided = 0	MonYear	count(*)	Breakdown - value_reference X axis label - Months Y axis label - Visits Stacked Bars - Y, Legend - Y, Bar Values - Y Sort Bars - Y, Y axis format - 0.3s
26	Clinical Indicators	Percentage Patients Seen By Doctor	Pie	PercentagePatientsSeenByDoctor		Diagnosis	sum(patients)	Legend - Y, Show Labels - Y Label Type- Category & Percentage
27	Clinical Indicators	All Symptoms Distribution	Treemap	obs	voided =0 concept_id = 163212 Year <> 2017	symptoms	count_distinct(person_id)	Ratio - 1.1, Number format - 0.4r
28	Clinical Indicators	TAT Per Doctor	Table	TATPerDoctor		Year Month Doctor	max(TAT) sum(total)	Doctor not in (Dr.Smith, Dr. User)