Computing and Global Health Lecture 3 Last mile data collection and Tracking. Winter 2015 Richard Anderson

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Transcription:

Computing and Global Health Lecture 3 Last mile data collection and Tracking Winter 2015 Richard Anderson 1/21/2015 University of Washington, Winter 2015 1

Today s topics Readings and assignments Cold chain assignment review HISP Case study Ghana Last mile data reporting Tracking vs. Surveillance Electronic Registers Challenges 1/21/2015 University of Washington, Winter 2015 2

Readings and Assignments Homework 2 Requirements for aggregating facility reports Readings DHIS2 Tracker, Saugene Generic Software Systems Child Health Information Services Biometrics papers 1/21/2015 University of Washington, Winter 2015 3

Assignment 3 DHIS2 Assignment Questions 1/21/2015 to fahadp@cs University of Washington, Winter 2015 4

Cold chain data reporting Distribution of countries Burden of Disease Cold chain reporting Design a system for reporting up time of all refrigerators National surveillance problem Indicator was identified Challenges in getting data, transmitting data, interpreting data 1/21/2015 University of Washington, Winter 2015 5

1/21/2015 University of Washington, Winter 2015 6

Cold chain data reporting Automated reporting linked to server Real time temperature monitoring Reporting on temperature loggers Reporting of status in monthly report Link to existing structures Monthly immunization reporting Refrigerator repair District immunization management 1/21/2015 University of Washington, Winter 2015 7

Surveillance summary Aggregate data to evaluate the strength of the health system or to meet external requirements Indicators Data challenges Integrated vs. Parallel reporting DHIS2 1/21/2015 University of Washington, Winter 2015 8

HISP Case Study Ghana 1/21/2015 University of Washington, Winter 2015 9

HISP Case studies 1/21/2015 University of Washington, Winter 2015 10

Health Information Systems Challenges Collection of irrelevant data Poor data quality Poor timeliness of reporting Parallel and duplicate data collection Low information usage and poor feedback Donor driven reporting Lack of requested data elements in national reporting Development of parallel reporting systems 1/21/2015 University of Washington, Winter 2015 11

DHIMS 2007: Roll out of District Health Information Management System 2008: Health Metrics Network (HMN), framework for integrated HIS 2011: Implementation of DHIMS2 in DHIS2 1/21/2015 University of Washington, Winter 2015 12

DHIMS2 vs. DHIMS Centralization of expertise Greater expertise needed, but can be centralize Improved data flow and reporting speed Increased access to information No longer restricted to a local database Consistent national deployment Avoid inconsistent development in different areas Substantial capacity development 1/21/2015 University of Washington, Winter 2015 13

Why Open Source? OpenMRS Open Data Kit DHIS2 Open LMIS... 1/21/2015 University of Washington, Winter 2015 14

Last mile data reporting Collecting data from health facilities Issues Limits on infrastructure Technical background of data reporters Incentives of data reporters Ownership of technology Model for data collecting 1/21/2015 University of Washington, Winter 2015 15

Internet Must be considered as an option Challenges of maintaining a computer at remote sites Need to support online/offline data entry 1/21/2015 University of Washington, Winter 2015 16

Feature phone Java phones to run applications Interest in the technology has declined Projects generally targeted a small range of models as portability of applications a challenge Feature phones retain some market share as multimedia phones Series of mobile phone applications based on XForms 1/21/2015 University of Washington, Winter 2015 17

Smart phone / ODK Growing interest in utilizing Smart Phones Cost and programmability drive interest in Android Open Data Kit University of Washington developed system for data collection on mobile phones Forms based application running on Phone Back end system for aggregating submissions Goal to make it easy for organizations to deploy survey tools on smart phones Example: IHME deployment of verbal autopsy tool Common approach, collect data on a tablet, and sync data by wifi when back in the office 1/21/2015 University of Washington, Winter 2015 18

SMS Data submission by raw text messages, interpreted by server In many cases, it can be assumed everyone has access to an SMS phone Challenges if a large amount of data is required FT B23 SL P10D35 1/21/2015 University of Washington, Winter 2015 19

SMS Wheel Attempt to simplify SMS reporting by giving a job aid to convert data into a numeric code with a checksum 1/21/2015 University of Washington, Winter 2015 20

Paper to Digital Scan paper forms Allows entry on paper (which is easier) Reduces manual entry More later... 1/21/2015 University of Washington, Winter 2015 21

Device ownership Personally owned versus provided devices Computers generally facility devices Mobile phones Personal Cheaper to the project Incentives to keep charged Heterogeneous Must support lowest common denominator Provided Can be costly Substantial effort to manage Security risks Training Allow uniform deployment environment Can be a mismatch with target users Potential for cross project utilization 1/21/2015 University of Washington, Winter 2015 22

Who collects the data Health workers Dedicated data collectors Derived or automatically collected 1/21/2015 University of Washington, Winter 2015 23

Health Information Systems challenge: Generating a Master Facility List MFL list of all health facilities in the country Facility ID (Primary key) Classification by services Best case: Kenya http://www.ehealth.or.ke/facilities/ 1/21/2015 University of Washington, Winter 2015 24

Challenges in building MFL List all public health facilities Determine which ones are active Identify new facilities Resolve duplicate names Determine other types of facilities to include Private, Faith based Establish unique ID codes Central administration of list 1/21/2015 University of Washington, Winter 2015 25

Laos Facility List, MOH vs NIP 0803001 80301 Phoulaeng ພ ແລ ງ Phuleng 0803002 80302 Thasouang ທ າ ຊ ວງ Thasuang 0803003 80303 KhokAek ຄ ກ ແອກ Kockeak 0803004 80304 Napoung ນາ ປ ງ Napung 0803005 80305 Namsib ນ າ ສບ Namsip 0803006 80306 Ban Harn ຫານ Han 0804001 80401 BanThong ບ ານ ທອງ Banthong 0804002 80402 HouaiGneui ຫ ວຍ ເງຍ Huaunhuen 0804003 80403 NaNhang ນາ ຍາງ Nanhang 0804004 80404 Pnagbong ປາງ ບ ງ Pangbong 0804005 80405 Phadaeng ຜາ ແດງ Phadeng 0804006 80406 Houaipheuang ຫ ວຍເຜ ງ Haupheug 0805001 80501 Phoulane ປາກ ເປ ດ Pakpet 0805002 80502 Parkpet Dong 0805003 80503 Dong Homso 0805004 80504 Holmxai Huamueng 0805005 80505 Houameuang Huana 0805006 80506 Houana Huaunhouck 0805007 80507 HouaiYourk Phulan (Thonhkang) 0806001 80601 Naxing ນາ ຊງ Nasing 0806002 80602 Nakhaem ນາ ແຄມ Nakhem 0806003 80603 Phadam ຜາ ດາ Phadam 0806004 80604 Navaen ນາ ແວນ /(ນາ ສາພ ນ) Naven/Nasamphan 0806005 80605 Pholthong ໂພນ ທອງ Phonthong 0806006 80606 PholsaArd ໂພນສະ ອາດ Phonsaaat 0806007 80607 Pongvang ປ ງ ວາງ Pongvang 0806008 80608 Holmxai ໂຮມໄຊ(ນ າງມ) Homsay/Namnhuem 1/21/2015 University of Washington, Winter 2015 26

Registers What are registers Surveillance vs. Tracking vs. Medical Records 1/21/2015 University of Washington, Winter 2015 27

Register definitions class ImmunizationRecord { int UniqueID; String Name; Date BirthDate; ImmunizationData immunizations; } ImmunizationRecord[ ] immunizationregister; 1/21/2015 University of Washington, Winter 2015 28

Immunization cards 1/21/2015 University of Washington, Winter 2015 29

Immunization Routine immunization Track immunizations received and dates of immunization 1/21/2015 University of Washington, Winter 2015 30

Infectious Disease Tuberculosis Processes established for identification and treatment Strict regimen of treatment Two months of Isoniazid, Rifampicin, Pyrazinamide, Ethambutol Four months of Isoniazid, Rifampicin Testing at completion TB Record Testing dates Medication 1/21/2015 University of Washington, Winter 2015 31

Case tracking Identification of carriers of specific diseases Malaria (for complete eradication) Measles (exposure tracking) Acute Flaccid Paralysis (AFP) 1/21/2015 University of Washington, Winter 2015 32

Maternal Health Tracking mothers through pregnancy Registration of pregnant women Antenatal care visits 1/21/2015 University of Washington, Winter 2015 33

Health use cases Surveillance More accurate than reporting events Better estimates of coverage Tracing defaulters Disease elimination Care and program planning Reporting Reminders Formalizing care Coordination of care across providers 1/21/2015 University of Washington, Winter 2015 34

Challenges Unique identifier Biometrics Name resolution On-line, off-line mode Undocumented people Conflict zones Privacy 1/21/2015 University of Washington, Winter 2015 35

5bd99967-9f94-4995-8b4c-dbf7ef23da72 How do we track people National or patient ID How are IDs assigned Alternate IDs Facebook, email, mobile number Mother s name Name Name and birthdate Name and birthdate and village Name and birthdate and village and father s name Name and birthdate and village and father s name and fathers village 1/21/2015 University of Washington, Winter 2015 36

Patient ID Generate health ID Provide to patient on paper or a smart card 1/21/2015 University of Washington, Winter 2015 37

Biometrics Some large initiatives based on biometrics Finger prints, Iris Finger prints are challenging for young children 1/21/2015 University of Washington, Winter 2015 38

Name resolution Additional challenges in the developing world Lack of records Spelling of names Imprecise dates 1/21/2015 University of Washington, Winter 2015 39

On-line, off-line access Standard synchronization problems In practice this is much harder than it should be 1/21/2015 University of Washington, Winter 2015 40

Undocumented people Clearly, this is a complicated, political issue Delivery of services to people without official status Maintain separate registration Alternate means of identification 1/21/2015 University of Washington, Winter 2015 41

Register/Tracker Implementations Many stand alone implementation Simple database backend Extract information from a medical record system Extension of DHIS2 Tracker is a new data model 1/21/2015 University of Washington, Winter 2015 42