PREPARED BY ISTRATEGY LTD.

WITH SUPPORT FROMBANGLADESH ENTERPRISE INSTITUTE

(BEI)

SPONSORED BYROCKEFELLER FOUNDATION

Preliminary Findings of Study on

“Policy issues fore-Health in Bangladesh”

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

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 Inter-ministry

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 consumingExpensive 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 levelsNGO 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 vacanciesNICVD

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 Desktop based, Locally Build, In-House, for the use of HR, Billing, Accounting, Pharmacy, Imaging, Pathological Data, Prescription, Medical Inventory

10

Popular HR- ExcelBilling- System NetworkingEMR- System NetworkingAccounting- ExcelPharmacy-S/NImaging- GEPathological Data- S/NMedical Inventory- S/N

3-5

Central Locally Developed, Desktop Based HR-Bangladesh General Automation, Billing, Accounting , Pharmacy- Bangladesh Southtek

3-5

Samorita 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

3-5

United HR, billing, EMR, Accountin, Pharmacy, Pathological, Prescription, medical inventory-local, developed by Sycraft Solution ltd.;

6-10

Ibne-Sinha HR, Billing, Accounting, Pharmacy, Imaging, Pathological, Medical Inventory: local, desktop based

14

Apollo HR, Billing, EMR: Foreign made, India Akhil Systems ( Desktop Based) 17

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

HL7Also a messaging

protocolMuch more extensive

than SDMX-HDCovers

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

Exchange Format Content Format

Individual DataHL7 ICD10/ SNOMED

Aggregated Data 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 functionalityRedundant data storageCostlyData integrityNot 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 interchangeDevelop 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 patientsRoll out of EMR at health-facilitiesInteroperability in HIS

SOA based- hub and spoke model ESB based