Preliminary Findings of Study on
“Policy issues for
e-Health in Bangladesh”
P R E PA R E D B Y
I S T R AT E G Y L T D .
W I T H S U P P O RT F RO M
BANGLADESH E NTERPRISE INSTITUTE
S PONSORED BY
R O C K E F E L L E R F O U N DAT I O N
(BEI)
Background
 iStrategy and BEI were given the task to conduct the
following:


Critical analysis of the current e-Health and HIS scenario in
Bangladesh
Identify policy-level issues that need prioritization
 This presentation is based on some results of that
study
Agenda
 Proposing realistic goals for HIS and e-Health in
Bangladesh
 Policy issues that need to be addressed for attaining
those goals in the context of local realities and global
experiences
 A proposed phase-wise approach towards those goals
Purpose
 Incite discussions and debates – not to suggest that
there is only one way of looking at things
 Part of an on-going exercise to bring out the policy
issues that need attention
Setting Goals for Stronger HIS
 Moving gradually from “integrated” system thinking
to “inter-operable” system thinking
 Moving gradually from aggregated data to
individual-based data in electronic form
 Using open standards to avoid lock-in and keeping
flexibility for customization as needed
 Focusing on “requirements specifications” and
design before developing information systems
1. Managing Identities
Managing Identities
 The fundamental pre-requisite of a health
system
 Issues for MoHFW:

Health Service Provider Identity
Organization
 Individual
 Service



System user’s ID
Patient ID
 Inter-ministerial issue:
 Geo Location Code: Address and location
Managing IDs – Current Status
 Unique universally accepted IDs for:





BMDC registration no for Physicians
Drug License
Hospital License
Medical College License
License for nurses
 Issues that we don’t have unique IDs across systems
for are:






Service ID
Health Indicators ID
Diseases ID
Patient ID
System User’s ID
Risk factors ID
Potential Consequences of not having IDs
 Data from different systems cannot be aggregated
 Data can never be normalized in a single data
dictionary
 Data exchange can be very expensive and time
consuming
 Like developed countries, data can be locked in
several silos and never being used across the
systems, expensive adaptors are taking place for data
interchange
Managing IDs – global example
 Australian ID standardization
Implementation Issues
 Unique ID system for every patient in the
context of Bangladesh is a huge challenge and
will take time to be developed
 However, many of the other IDs are more
doable and can provide a basic platform for
taking HIS to next level
 Short to medium term:

IDs for health-service providers – individual and
organizational, services, geo-locations
 Long term:
 Patient ID
2. Privacy and Confidentiality
Privacy and Confidentiality
 Setting rules for ‘governance of data’ is
absolutely critical for designing an HIS


Who owns data?
Who has access to what data?
 Specially important for public-private
collaborations and data sharing
 Consent of the patient regarding use of data
Privacy and confidentiality – current status
 In practice, patient-doctor confidentiality is
maintained by doctor himself
 Scope for improvement in the Privacy Act in
Bangladesh being made more relevant for medical
field
 No rules yet for ownership and access of data
Potential consequences of not having privacy
and confidentiality rules
 Critical to designing of health systems
 Defining the role of each user of the system
 Defining access control
 Designing security standards
 Without these, system development can be
haphazard and adhoc – leading to expensive
upgrades and changes later on
 Citizens will not be comfortable in letting their
data to be digitized
Privacy and Confidentiality: global scenario
 Every e-Health policy and guideline has privacy and
confidentiality

Example: HIPAA (Health Insurance Portability and
Accountability Act)
provides federal protections for personal health information held
by different entities
 gives patients an array of rights with respect to that information.

Privacy and Confidentiality - Implementation
Issues
 We need to distinguish between individual and
aggregated data since the former is much more
sensitive
 We can start with issues around aggregated data first
 Short-to-Medium Term:

Regulations may be passed by the government regarding:



Ownership of health data
Access rights of health data
Security standards that need to be maintained by health systems
 Long Term:


Deal with sharing of individual-level data
Patient’s consent
Reporting Standardization
Reporting Standardization
 National Level Reporting
 Governmental organizations reporting to higher authority
 NGOs and private health-facilities reporting to the government
 International Level Reporting
 WHO
 Health-related donors
Reporting Standardization – current status
 National-level reporting:
 Within MoHFW






Inter-ministry


Some standards such as those proposed by HMN, UN and Paris 21
declaration are used
However, standards that can be used across systems are yet to be defined
Collected manually, aggregated and sent through Excel spreadsheets
Entered in DHIS from different districts
Some of the comments that have come from the workshops:
 Duplication in report generation
 Aggregation across departments is often not possible
 For many organizations, there is no reporting software – it is done
manually and the results entered in Excel formats
Adhoc as needed
NGO/private sector reporting to MoHFW

Adhoc as needed
 International reporting:
 According to requirements of individual donors
 Varies from project to project
 Significant scope for standardization across projects
Potential consequences of not standardizing
reports
 Aggregation is not easily possible
 For instance: Very difficult to track MDG goals effectively at
national level
 Costly and time consuming
 Expensive adapters and mapping mechanisms may
be required for aggregation
Reporting standardization – global practices
 WHO Indicator and Measurement Registry (IMR)


Central source of metadata of health-related indicators used by
WHO and other organizations
It promotes interoperability through the SDMX-HD indicator
exchange format
4. Enabling Standardized Data Entry
Enabling standardized data entry
 Standardized entry of diseases, signs and symptoms
 Standardized entry of patient data at:
 Facility-level
 Community-level
Enabling standardized data entry – current
status
 Public sector

Facility-level:
Aggregated data from record rooms at some hospitals are digitized
and sent through Excel sheets or entered in DHIS
 DHIS indicators are not standardized across systems but it has
provided a solid foundation for further work


Community-level:

Data collection is done manually and aggregated manually, which is
digitized at district levels
 NGO sector

Each NGO has their own way of inputting data – no standardization
 Private sector

2 or 3 top private hospitals are found to be using ICD-10
Enabling standardized data entry:
Implementation issues
 ICD10 (International Classification of Diseases)

Coding of diseases, signs and symptoms, abnormal findings, complaints, social
circumstances and external causes of injury or diseases
 SNOMED (Systemized Nomenclature of Medicine)



Wider coverage than just diseases, including findings, procedures,
microorganisms, pharmaceuticals etc.
Licensing involved
Less uptake than ICD10
 Short term:


Standardized digitization of aggregated data from record rooms at facilities
Standardized digitization of aggregated data coming from community level
 Mid-term:

EMR for community level intervention based on remote feedback from doctors
 Long term:

EMR at hospitals
Enabling standardized data exchange
Enabling standardized data exchange

Data may be entered in numerous ways and we cannot change
those, we cannot change legacy systems already in place

What we can do is have a standard for exchanging of data

If the standards for data entry are not followed as discussed earlier,
then aggregation will not be possible automatically
Enabling standardized data exchange – current
status
 Within MoHFW
 Different projects have their own MIS –no interchange of data
between systems
 Between private sector and government
 Adhoc as needed
 Some comments from the workshops:

Private sector is willing to send data if there is a specific format for
exchange is given to them
Scenario at Public Hospital
Hospital
Customized Software
IT Team
Dhaka Medical College
-
-
BSMMU
Lab reporting & Accounting
1-2
NITOR
n/a
2 and 2 vacancies
Kidney Diseases Hospital
n/a
6-10 people
2 vacancies
NICVD
Shaheed Suhrawardy Medical College
n/a
n/a
National Institute of Mental Health
n/a
n/a
Sir Salimullah Medical College
n/a
n/a
NIPSOM
n/a
n/a
Institute of Public Health Nutrition (IPHN)
n/a
n/a
EPI
n/a
n/a
Scenario of selected private hospitals
Hospital
Customized Software
IT Team
Lab Aid
10
Ibne-Sinha
Desktop based, Locally Build, In-House, for the use of HR, Billing, Accounting, Pharmacy, Imaging,
Pathological Data, Prescription, Medical Inventory
HR- Excel
Billing- System Networking
EMR- System Networking
Accounting- Excel
Pharmacy-S/N
Imaging- GE
Pathological Data- S/N
Medical Inventory- S/N
Locally Developed, Desktop Based HR-Bangladesh General Automation, Billing, Accounting ,
Pharmacy- Bangladesh Southtek
HR- local,In house; Billing- Local, In house; EMR- local, in house, Accounting- Foreign, ACCPAC;
Pharmacy- local, In house; Pathological- Local, in house; Medical inventory- local, Datasoft
System Bd ltd.; Admin & Investigation- local,, in house, all are desktopased
HR, billing, EMR, Accountin, Pharmacy, Pathological, Prescription, medical inventory-local,
developed by Sycraft Solution ltd.;
HR, Billing, Accounting, Pharmacy, Imaging, Pathological, Medical Inventory: local, desktop based
Apollo
HR, Billing, EMR: Foreign made, India Akhil Systems ( Desktop Based)
17
Popular
Central
Samorita
United
3-5
3-5
3-5
6-10
14
Scenario of Projects Under DGHS
National AIDS/ STD program and safe
blood transfusion
-
CRIS software (National MIS database system on piloting
phase for country wise HIV AIDS activities reporting)
National MIS on HAIS base DICs for digital reporting of DICs
services
Up to 5
Alternative Medical Care
n/a
n/a
Communicable Disease Control (CDC)
n/a
n/a
In-service Training
TMIS – To maintain training data and to evaluate field workers work
Upto 5
Human Resource Management (Health)
n/a
n/a
Sector –wide program management
(Health)
LLP Toolkit Software
n/a
TB & Leprosy Control
TB Data Management- Use for TB related Data and Generating
report
Upto 5
IFM
n/a
n/a
Quality Assurance
FMRS
n/a
Micro Nutrient Supplementation
n/a
n/a
Essential Service Department
n/a
n/a
Improved Hospital Service Management
n/a
n/a
Family Planning
SMIS
11-15
Scenario of some selected NGOs
Hospital
Customized Software
IT Team
Marie Stopes
Clinical Service Entry, PMIS; SUN Accounting for Accounts
3-5
Friebdship
ERP System- HMS, Research Tools, payrolls, leave management, Travel
record mgmt, procurement, telemedicine, Microfinance, Accts & finance
6-10
Action Aid
HR, IT and Finance
3-5
Helen Keller International
Data Collection Software
1-2
National Heart Foundation
OPD, Patient admission, cash counter, Bill, All investigation, HRM,
Accounts, General/ Medical Store, Cathlab, Pharmacy, Research, Ward/
cabin, website
1-2
Grameen Kalyan
Accounting
n/a
Sajida
Micro Finance Management, Hospital Management, Human Resource
Management, Automated Accounting System, Cheque Management,
System, Tele medicine system, Fixed Asset Management System
6-10
BRAC
Accounting, HR, IT
More than 15
Enabling standardized data exchange –
implementation issues
 SDMX-HD
 It is not about data entry
or data storage format
 SDMX-HD messages are
defined for the process of
exchanging indicator
definitions and aggregate
data and metadata
 HL7
 Also a messaging
protocol
 Much more extensive
than SDMX-HD
 Covers standardization
in different workflows in
the continuum of care –
starting from billing to
patient tracking
 Country membership
based
Enabling standardized data exchange –
implementation issues

Short term:


Standardizing the format for data exchange with respect
to indicators and IDs
Mid to long term:
Standardizing data exchange and inter-operability
 Standardization for privacy and security during data
exchange and inter-operability

Enabling standardized data exchange – global
practices
 For individual-level data, HL7 messaging format is
often used
 For aggregated data, SDMX-HD is being
increasingly used because of its simplicity
compared to HL7
 Use of software that already has SDMX-HD
standards:

OpenMRS adopted by more than 50 countries
Data Interoperability
Individual Data
Aggregated Data
Exchange Format
Content Format
HL7
ICD10/ SNOMED
SDMX-HD
IMR/ ICD10
Going beyond data exchange
 Getting data by querying into other
information systems
 Service Oriented Architecture (SOA) approach
Going beyond data exchange – current status
 In the government:
 SOA-based approach is not prevalent yet
 In the private sector:
 Sporadic instances
 Example: inter-operability within different systems of BRAC
Potential consequences of sole dependence on
data exchange
 It is not feasible for everybody to have every data.
 Systems cannot share functionality
 Redundant data storage
 Costly
 Data integrity
 Not taking advantage of “starting late”
Proposed Implementation Phases
 Phase 1: Building on already developed foundation
 Phase 2: Basic Inter-operability
 Phase 3: Advanced Inter-operability
Phase 1:Building on already developed
foundation
 Form a high level steering committee for the
following:



Identity Management of Health Service Providers, Locations
and Services.
Role Based Privacy and Confidentiality Rules (like HIPAA).
Use a terminology standard ICD10/SNOMED during data
entry before sending to the accumulation point
 Implement regulation for Data interchange
 Develop standardized formats for data interchange
 Enterprise service bus (developed by A2I)
Phase 2: Basic Inter-operability
 Digitization of record room (aggregated data)
 Implementation of ICD10, SDMX-HD
 Major private hospital and major NGOs involved in
data interchange according to standardized formats
 Shared registry of National level health information
(building on NPR)
 Implementation of privacy and security guideline
(like HIPAA)
 First steps towards EMR at health-facilities
Phase 3: Advanced Inter-operability
 Identity management of patients
 Roll out of EMR at health-facilities
 Interoperability in HIS
 SOA based- hub and spoke model
 ESB based