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Strengthen the openIMIS community in Asia by localizing capacity-building strategies and supplementary knowledge materials with the reality of concrete country cases
Objectives:
Continue facilitating regional knowledge sharing and networking around openIMIS;
Develop a regional capacity-building program on health financing and digital heath with openIMIS as the laboratory component; and
Expand the openIMIS community of practice in Asia by supporting open source ecosystems for national and subnational schemes.
Contact Persons:
Dr Alvin Marcelo
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Online Community Management
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ACTIVITIES
AeHIN 7th General Meeting
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The webinar recording is available at https://youtuwww.youtube.becom/watch?v=4lfv_SlXjwE&ucbcb .
Other openIMIS webinars are available at https://www.youtube.com/playlist?list=PLN7M3nT7qGnfu329R2YTiuLQV_m4J6vIO .
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openIMIS Enrollment App Testing
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Invite Lao IT staff to join the AeHIN community/mailing list
Currently 1 IT specialist working with NHIB
Currently around 5 IT staff working with Health Information Division, MOH Lao
MOH departments to meet again for the development of Lao's Digital Health Strategy (for the next five years); convergence workshop report to be published soon
openIMIS can provide support through the following channels:
Through networking facilitated by AeHIN: support discussions in defining the functionalities required by NHIB for their health insurance management system
Through knowledge resources: openIMIS wiki page, openIMIS videos
Through the openIMIS implementation catalytic fund: technical assistance for implementation including customization, user training, etc.; more details are available here
Through ILO
Suggested next steps based on the meeting:
At the country level, the Lao team defines system requirements for health insurance management
At the regional level, AeHIN facilitates inter-country discussion in defining minimum requirements for a generic health insurance information system
Meeting with Dr. Alvin and Saurav to discuss refined system requirements (wait for further details from NHIB)
openIMIS demo
Meet as a group on how to move forward
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The webinar recording is available at https://youtuwww.youtube.becom/watch?v=m0v_XcjWHLg
Other openIMIS webinars are available athttps://www.youtube.com/playlist?list=PLN7M3nT7qGnfu329R2YTiuLQV_m4J6vIO
DRG Information Systems
On October 26, 2021, Dr. Boonchai Kijsanayotin, Chair of the Asia eHealth Information Network, presented a webinar titled “DRG Information Systems: Thailand Experiences."
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The webinar recording is available at https://youtuwww.youtube.becom/watch?v=SHGAvXaTAQY
Other openIMIS webinars are available athttps://www.youtube.com/playlist?list=PLN7M3nT7qGnfu329R2YTiuLQV_m4J6vIO .
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openIMIS Community of Practice in Asia Pre-formed Panel at the Global Digital Health Forum 2021
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The recording of the webinar is available at https://youtuwww.youtube.becom/watch?v=l0c0z3zJv7g
Ms. Sovathana Suong will be presenting her second webinar in April 2022.
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Interesting information were shared by the students and opened opportunities for engagement with the students. The webinar recording may be accessed at https://youtuwww.youtube.becom/watch?v=vsKNRnNDcq4 .
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openIMIS Webinar - Why and How Thailand became a SNOMED CT Member Country
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In the webinar, he shared what the Thai Health Information Standard Development Center (THIS) has learned, researched, pursued, and endured to introduce SNOMED CT to the health IT and digital health community in Thailand, and the plan to adopt and implement SNOMED CT in Thailand for both administrative information systems (Thai DRG and health insurance information system e.g., OpenIMIS) and clinical care information system.
View the recording here: https://youtuwww.youtube.becom/watch?v=1tP-ad2Lnl4
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openIMIS Webinar - Social Protection Focusing on HEF Implementation in Cambodia
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The webinar recording may be viewed at https://youtuwww.youtube.becom/watch?v=gGJKt_9ZEL0
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Open Source Health Information Systems: Opportunities and Challenges (Part 1)
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To view the recording, please visit https://youtuwww.youtube.becom/watch?v=9tR7WUjDx7Q .
The second part of the webinar will be held on May 13 at 3 PM Manila time.
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DRG Webinar Series 2022
The webinar series aims at building up knowledge and learning experiences to participants from countries that want to pursue the universal health coverage policy with payment to acute inpatient care with diagnosis related group tools.
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Webinar recordings:
April 13, 2022 - Module 1: The core understandings on DRG and health financing policy (DRG for ALL)
May 4, 2022 - Module 2: The story of the Thai DRG development
May 25, 2022 - Module 3: The mechanisms of DRG development
June 1, 2022 - Module 4: Hospitals in the DRG Environment
June 15, 2022 -To follow
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openIMIS eLearning Course Launch
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In the first part of the session, Konstanze Lang provided a short introduction to the openIMIS initiative, the rationale behind, an overview on use cases and countries where openIMIS is implemented as well as on the community. It was followed by Daniella Majakari who gave the the introduction to the the new openIMIS e-Learning course, including the target audience, the learning objectives and different modules, and a short demo on how to navigate the course. A total of 60 participants joined the session.
To check the webinar recording, visit https://youtuwww.youtube.becom/jty7YXTX-C4
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As of June 15, 2022, the openIMIS eLearning Course titled “Introduction to openIMIS” has 200 participants. Country hubs from the openIMIS Community of Practice in Asia have been organizing feedback and reflection sections based on their experience in completing the self-paced openIMIS eLearning course.
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Current Status and Future Perspectives of OpenIMIS Implementation in Nepal
Dr. Rabindra Bista, Associate Professor at the Department of Computer Science and Engineering in Kathmandu University Nepal, gave a webinar on May 16, 2022.
In his presentation, he covered the current state-of-the-art of openIMIS implementation in Nepal, patients' perspectives of using openIMIS in Nepal, and how they can integrate openIMIS in an academic curriculum. Thirty-four attendees joined the webinar.
To view the recording, please visit https://www.youtube.com/watch?v=R2ZuTjsFLyE .
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FOSS Webinars 2022
Dr. Luis Marco Ruiz, Chair of the openEHR Education Program, presented a two-part webinar about opportunities and challenges in Open Source Health Information Systems for governments and software implementers on April 8 and May 13. Forty-one attendees participated in the first webinar and 81 attendees joined the second webinar.
Both webinars covered aspects related to the design and development of open-source health IT infrastructures that guarantee the scalability and availability of data for health delivery, research, and policy making, as well as data ownership, data accessibility, and FAIR (Findable, Accessible, Interoperable, Reusable) principles. The webinar focused on how these aspects should be approached at procurement and development stages from a free open-source software (FOSS) perspective.
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On June 10, Mr. Uwe Wahser from GIZ and Dr. Oliver Hummel from the University of Mannheim gave presentations on FOSS and Academia. Dr. Christoph Geiss, also from the University of Mannheim, joined the Q&A portion. A total of 24 participants attended the session.
Documentation and materials from the session may be accessed here.
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Webinar recordings:
Open Source Health Information Systems: Opportunities and Challenges (for Governments)
Open Source Health Information Systems: Opportunities and Challenges (for Software Implementers)
FOSS and Academia: How to integrate openIMIS in your Curriculum
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openIMIS Guest Lecture from Nepal
Mr. Nirmal Dhakal and Mr. Purushottam Sapkit gave a guest lecture to engineering students of the Institute of Engineering at Tribhuvan University on May 23, 2022. They gave a brief overview of openIMIS and Digital Health in Nepal. The students were mainly interested in status configuration, installation, as well as the existing countries implementing openIMIS and what they can learn from them.
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openIMIS-DRG Datathon 2022
The Center for Applied Research and Development (CARD) of the Institute of Engineering at Tribhuvan University, in collaboration with the openIMIS initiative, Standards and Interoperability Lab Thailand (SIL-TH), and the Asia eHealth Information Network (AeHIN), organized the openIMIS-DRG Datathon held on June 3-5.
A two-hour orientation was held on the first day of the datathon. Dragos Dobre and Eric Darchis gave an introduction to openIMIS and data models, and Dr. Boonchai Kijsanayotin, Chair of the Asia eHealth Information Network gave an introduction to DRGs and the Thai Data Model.
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Thirteen out of the 14 participating teams successfully finished and submitted their output.
On June 6, virtual badges were awarded to the students via Badgr.
Images are by Center for Applied Research and Development (CARD), Institute of Engineering, Tribhuvan University. Retrieved from https://card.ioe.edu.np/datathon-orientation-session-jun-3rd-2022/, https://card.ioe.edu.np/datathon-award-ceremony-jul-4th-2022/.
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openIMIS-DRG Datathon Awarding Ceremony 2022
On July 4, an awarding ceremony was held at Pulchowk Campus to recognize the students who won the openIMIS-DRG Datathon held from June 3 to 5, 2022.
The criteria used to score the performance and output of the groups were based on accuracy. The three winning teams namely Cartographers, Hufflepuff, and Trinity were awarded with certificates and prize money from the Institute of Engineering, Pulchowk Campus, Tribhuvan University.
Prof. Sangeeta Singh, Director of CARD-IOE; Prof. Shashidhar Ram Joshi, Dean of IOE; and Dr. Basanta Joshi, Deputy Director of CARD-IOE were present in the awarding ceremony, while Dr. Alvin Marcelo, Executive Director of AeHIN, joined the awarding virtually via Zoom.
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Prof. Basanta Joshi shared the results of the Datathon and Gaps Analysis in openIMIS and the Thai Diagnosis Related Groups (DRGs) through a webinar on July 18.
Resources:
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openIMIS talks about Opportunities on AI for Health Insurance at the AeHIN GM 2023
To discuss the potentials and challenges of using Artificial Intelligence (AI) as a tool in managing health insurance schemes, the openIMIS Initiative together with the Badan Penyelenggara Jaminan Kesehatan (BPJS -K) or Social Security Agency on Health Indonesia, openIMIS Initiative, Joint Learning Network, and World Bank, held a joint session on ‘AI for Health Insurance’ at the Asia eHealth Information Network (AeHIN) General Meeting (GM) 2023 on November 6, 2023, at JS Luwansa Hotel and Convention Center, Jakarta, Indonesia. The AeHIN GM discussed various digital health topics under the overall theme, “Ensuring Digital Health for Better Outcomes: Putting Blueprints into Practice.”
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Around 50 delegates representing the government, academe, development partners, civil society, and professional societies within and beyond South and South-East Asia participated in the ‘AI for Health Insurance’ session, which explored the aspects AI can and cannot solve in the health insurance sector. Karlina Octaviany, AI Specialist at the ‘FAIR Forward – Artificial Intelligence for All’ initiative at GIZ Indonesia, moderated the two-hour knowledge-sharing session, which discussed examples of applying AI for health insurance; presented opportunities on how AI could work with existing (health) insurance management information systems; and shared BPJS-K’s experience in adopting AI for health insurance.
Opportunities for AI in Health Insurance
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Saurav Bhattarai, Advisor and lead for the openIMIS initiative at GIZ, presented opportunities for AI in health insurance in the context of claims adjudication. He started his presentation with an example of a typical insurance claims workflow:
Claims submission: It starts with claims submission which could be done manually or digitally by health facilities.
Rules engine: A typical IT system has some rules programmed in, which generally contain simple checks to see if the health insurance policy is active, if the person is insured or not if the services are covered and applicable in the policy, and the frequency limits. Bhattarai further explained that the rules engine is classified as part of AI, even if it is rudimentary, as the computer is taking a decision about claims getting approved or rejected.
Manual evaluation: After passing the checks from the rules engine, it will undergo manual evaluation.
Payment: After claims are verified from the manual evaluation, payment will be approved. If not, a health facility will receive a response.
As an implementation example, Bhattarai showed the number of claims coming in per day to the National Health Insurance in Nepal –– from around hundreds of claims per day in 2016 to around 15,000 claims per day in 2021. The trend shows an exponential increase even up to now. Bhattarai mentioned that digital health is one of the contributing factors to this increase, which began when the National Health Insurance mandated the use of Fast Healthcare Interoperability Resource (FHIR) standards for electronic health records. This meant that more electronic health record systems (EHR) in hospitals are now compatible with the IT system used for the submission of digital claims in health insurance. Digitalization is making it easier for health facilities to submit claims; however, health insurers are finding it difficult to review the increasing number of claims. As a result, there is a huge gap between the number of claims received and the number of claims reviewed.
Bhattarai reiterated that the bottleneck lies in adjudication. The number of claims per day increases every year, while the human intervention capacity remains limited. In 2019, only 16 officers were hired to review claims. On a good day, one officer can review up to 100 claims per day. Theoretically, all available officers combined can only review up to 1, 600 claims per day out of around 15,000 claims received per day. This situation hurts healthcare delivery because when health facilities are not being paid, they cannot provide services to the population that needs them. At the moment, 30,000 claims are projected to be received per day, which means around 300 officers would be needed to manage this daily demand. Thus, automated claim categorization through AI models is needed to reduce time between claims submission and claims payment and, therefore, to increase access to health.
By adding machine learning to the same claims submission workflow, claims can now be automatically categorized. In this context, an AI engine was created for automatic classification based on manual review and the ability to flag rejected claims. If the AI engine starts rejecting claims, it will go through a manual quality assurance process so human reviewers can check if the AI is rejecting claims properly. The manual quality assurance helps the AI engine learn more, as human action is what makes the AI engine better and better. Bhattarai explained the steps followed to incorporate machine learning in the claims submission workflow:
Data gathering and preparation: Creation of an AI input data model, including sanity check on the database, processing of categorical data, and normalizing data.
Implementation of the AI algorithm: Design AI methods, model outputs, and evaluation metrics, then programming the AI model itself.
Software development: Development of the AI modules as well as program interfaces using the FHIR standard. Claims that come in the AI module are FHIR claims.
User acceptance testing: Testing of the AI modules.
On challenges encountered, Bhattarai shared that during data gathering, the challenge was that there was not a lot of data to learn from initially and that the few ‘rejected data’ did not indicate reasons for rejection. In the database, it was also observed that text fields were not standardized. During algorithm development, one of the challenges Bhattarai shared was the lack of non-numerical data. As most data were categorical, rejections were based on different types of data and visit types. Very few AI models were suited to the type of data that was present. Thus, the resulting AI model was based on extreme gradient boosting. For the development of the claims module, two data streams were used – offline and online data. Offline historical data were used for gathering data, cleaning data, and data analysis for training the AI model. The model was then applied to online data where claims were coming in before finally executing the AI model for actual analysis and execution of AI in accepting and flagging claims.
The whole research and development of the AI module presented by Bhattarai was implemented on openIMIS, an interoperable, versatile open-source software for managing health insurance systems. Not only is the software available for download, but all the logic and thought process that went into developing the AI module is free to use and modify. As openIMIS is a global good, the openIMIS Claim-AI module can be integrated into any management information system (MIS). Organizations can adopt the AI module and continue using their own MIS without needing to use the whole openIMIS technology suite. With a readily available AI module for claims, interested implementers will only need to conduct data preparation, customize, deploy and test. Bhattarai encouraged everyone to take advantage of this global public good, “In typical AeHIN fashion, when we help friends, friends will help us; this is us helping friends.”
The openIMIS AI module resources are open-source and freely available with a wider community available for support. Model design details are available here. Codes are also available via GitHub.
Questions addressed and answered by the openIMIS Initiative during Open Forum:
AI will flag anomalies and so on, has there been a regular human intervention to see whether it’s correct?
Saurav Bhattarai: Quality assurance is one of the steps presented. After there is an AI intervention, let’s say an AI flags the claims, there is a provision for a manual review of the AI action. That’s basically where the learning also happens if there is a mistake from the AI. As we go on, we still have a lot more data to process but from the data that we have, the accuracy is increasing as we go along. It takes about a year to gain half of a percentage, but that’s basically how it improves. The manual quality assurance is recommended and very much used.
Since it is an open system, what kind of dataset have you used for training? What is the FPI for this algorithm? It’s good to review them and see whether they fall under the rection category, but what about the claims that are false-positive and selected as legitimate claims?
Saurav Bhattarai: For the data, the software is available as a digital public good, when we developed it, it had to be developed for a certain use case because there is no global dataset available. The initial solving of the problem, even the model development, was done based on the dataset that we had in the implementation in Nepal - the National Health Insurance in Nepal. That data was used to develop the model, training, and everything. Right now, what is available as a digital public good is everything about that data. But, you can train the (AI) model using your own data. Right now, we’re trying to see if we can get access to some publicly available data, but that’s quite difficult. There are some developer teams that are artificially generating data. Of course, it’s not the same as real data but just for our testing purposes and showcasing purposes. But, the model itself is there. For the false positives, I am not an AI expert, but I can refer you to the resources, and I can send you the links. We did have performance issues on version 1, so this is version 2 already.
So, we have to shift gears. (For example), if a minister would be interested in AI either in MoH or insurance agency, what might be an advice you can give the minister? One or two priority things you can start with, knowing that AI might be very complex and might be difficult for them to do many things all at the same time.
Saurav Bhattarai: I don’t think we want the minister saying, ‘We should have AI.’ The minister should be saying, ‘Let’s reduce fraudulent claims.’ If the minister starts saying, ‘Let’s have AI,’ then we’re gonna have everyone running around buying anything that has the word, ‘AI’. It’s more of, ‘Let’s build capacities within the advisory team of the minister so that the minister isn't saying let's use AI.’ He’s talking about the problems in the health sector and asking for solutions to that problem. That means the digital health and the technical teams will decide how, what, and when you can use AI, or maybe 5 years later, we’ll be talking about a different technology – it won't be AI because these technologies will definitely change.
Presentation slides at the session are available here.
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openIMIS presents at the AeHIN Marketplace in Jakarta, Indonesia
AeHIN’s marketplace session made a successful comeback in this year’s AeHIN General Meeting held in Jakarta, Indonesia. The marketplace session is one of the unique parts of the AeHIN General Meeting. NGOs, international development agencies, the academe, and government partners showcase their digital health solutions implemented in collaboration with the Government/Ministry of Health, highlighting the national/provincial digital health programs in countries. openIMIS was among the 34 booth presenters at the event. Saurav Bhattarai and Nirmal Dhakal represented openIMIS and shared about openIMIS being a digital public good that supports the digital management of various processes within multiple types of health financing mechanisms.
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