Meeting Report
Regional Meeting on Maximizing the Potential of Health Insurance Data Systems to Support Universal Health Coverage
1–2 December 2015 Manila, Philippines
WORLD HEALTH ORGANIZATION REGIONAL OFFICE FOR THE WESTERN PACIFIC RS/2015/GE/60(PHL) English only
MEETING REPORT
REGIONAL MEETING ON MAXIMIZING THE POTENTIAL OF HEALTH INSURANCE DATA SYSTEMS TO SUPPORT UNIVERSAL HEALTH COVERAGE
Convened by: WORLD HEALTH ORGANIZATION REGIONAL OFFICE FOR THE WESTERN PACIFIC
Manila, Philippines 1–2 December 2015
Not for sale Printed and distributed by: World Health Organization Regional Office for the Western Pacific Manila, Philippines February 2016
NOTE
The views expressed in this report are those of the participants of the Regional Meeting on Maximizing the Potential of Health Insurance Data Systems to Support Universal Health Coverage and do not necessarily reflect the policies of the conveners.
This report has been prepared by the World Health Organization Regional Office for the Western Pacific for Member States in the Region and for those who participated in the Regional Meeting on Maximizing the Potential of Health Insurance Data Systems to Support Universal Health Coverage in Manila, Philippines from 1 to 2 December 2015.
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CONTENTS ABBREVIATIONS ................................................................................................................................ 4 SUMMARY ............................................................................................................................................ 5 1. INTRODUCTION .............................................................................................................................. 6 1.1 Meeting organization..................................................................................................................... 6 1.2 Meeting objectives ........................................................................................................................ 6 2. PROCEEDINGS ................................................................................................................................. 6 2.1 Opening session............................................................................................................................. 6 2.2 Plenary session 1: setting the scene ............................................................................................... 6 2.2.1 Current and potential data from national health insurance systems: role in informing public health policies and programmes ............................................................... 6 2.2.2 Experience of data systems of Medicaid and Medicare in the United States of America ...... 7 2.3 Plenary session 2: status of data systems of national health insurance agencies .......................... 8 2.3.1 Content and scope of health insurance data in the Republic of Korea ................................... 8 2.3.2 Status of data systems of Australia's National Health Insurance Agency .............................. 8 2.3.3 Achieving UHC: lessons from Thailand................................................................................. 8 2.3.4 Status of the data system of the New Rural Cooperative Medical Scheme in China ............. 9 2.3.5 Health Insurance in Viet Nam: content and scope of data ...................................................... 9 2.4 Group work 1: streamlining the scope, collection and analysis of data from national health insurance agencies: identification of best practices............................................................................. 9 2.5 Plenary session 4: strategies, initiatives and challenges in powering data systems using IT solutions to improve accessibility, timeliness and quality ................................................................ 12 2.5.1 IT solutions in health insurance data in the Republic of Korea ............................................ 12 2.5.2 Technology to improve the efficiency of the health system in Australia ............................. 12 2.5.3 Achieving UHC in Thailand: lessons for health insurance IT .............................................. 13 2.5.4 IT support for the National Health Insurance Program in the Philippines ........................... 13 2.5.5 Health information construction in China ............................................................................ 14 2.5.6 IT application for health insurance in Viet Nam .................................................................. 14 2.5.7 IT solutions in health insurance in Japan .............................................................................. 15 2.5.8 Status of data systems in Mongolia ...................................................................................... 15 2.6 Group work 2: strategic and optimal use of IT to power data systems for better quality, efficiency and analytics: identification of the best practices ............................................................. 17 2.7 Plenary session 6: synthesis, recommendations and closing session .......................................... 19 3. CONCLUSIONS AND RECOMMENDATIONS ........................................................................... 20 3.1 Conclusions ................................................................................................................................. 20 3.2 Recommendations for national health insurance agencies .......................................................... 20 ANNEXES ............................................................................................................................................ 21 Annex 1. Meeting programme........................................................................................................... 21 Annex 2. List of participants ............................................................................................................. 24 KEYWORDS: Insurance, Health / Health information systems / Universal coverage
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ABBREVIATIONS CMS EDW EHR EMR ICD ICT IT NDB NHID NHIS NHSO NRCMS SHI UC UHC VSS Centers for Medicare and Medicaid Services Enterprise Data Warehouse Electronic Health Records Electronic Medical Records International Classification of Diseases Information and Communication Technology Information Technology National Data Base National Health Insurance Database National Nealth Insurance Systems National Health Security Office New Rural Cooperative Medical Scheme Social Health Insurance Universal Coverage Universal Health Coverage Viet Nam Social Security
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SUMMARY The Regional Meeting on Maximizing the Potential of Health Insurance Data Systems to Support Universal Health Coverage was convened in Manila, Philippines on 1–2 December 2015. Excluding the Secretariat, 16 participants from seven Member States attended. The objectives of the meeting were: 1) to share experiences: a) in developing and structuring data systems of national health insurance agencies and their interactions with overall health information systems; b) in accessing and utilizing data from national health insurance agencies to inform healthcare practice; and 2) to identify best practices and develop recommendations for the consideration of national health insurance agencies to maximize the value of their data systems. The national health insurance systems (NHIS) of countries in Western Pacific Region are at different stages of development with variable provider payment mechanisms, benefit packages and institutional mandates across countries. Their overall information technology (IT) infrastructure and use of IT solutions to support their NHIS are also in various stages of development. The use of IT in NHIS is affected by organizational structures, provider payment mechanisms and mandates of health insurance agencies across countries. Various areas were discussed to enhance the efficiency of collecting data through NHIS, maximizing the potential use these data to inform policy, and designing IT solutions to support national health insurance processes. Participants made the following recommendations for national health insurance agencies. National health insurance agencies may consider: 1) increasing awareness and visibility of NHIS data systems as a source of data to inform broader health systems policy issues, including quality of care, health-care costs and monitoring of epidemiological situations; 2) Proactively linking and optimizing interoperability, and triangulation of data from traditional health information systems and NHIS sources for mutual benefit; 3) standardizing data (including metadata) with respect to data fields and using international data standards (e.g. ICD-10 for disease diagnosis); 4) identifying opportunities for linkages and optimization of EMR/EHR application development and e-claim systems development; 5) developing transparent institutional, policy and regulatory frameworks based on a consultative process to underpin data sharing and access to other stakeholders. The NHIS should also share information on metadata to potential users; 6) engaging in long-term planning and investment to develop and design IT infrastructure, accompanied by change management and people skill development, to support NHIS; and 7) creating communities of practice.
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1. INTRODUCTION 1.1 Meeting organization The meeting was convened by the Health Intelligence and Innovation unit of the Division of Health Systems, WHO Regional Office for the Western Pacific. Dr Manju Rani, Senior Technical Officer, Evidence for Health Systems, was the responsible officer of the WHO Secretariat. Moderators and rapporteurs were selected from among the participants. The meeting programme is available at Annex 1. Excluding the WHO Secretariat, there were 16 participants from seven (7) Member States (Australia, China, Japan, Mongolia, the Philippines, the Republic of Korea and Viet Nam).The list of participants is available at Annex 2. 1.2 Meeting objectives The objectives of the meeting were: 1) to share experiences: a) in developing and structuring data systems of national health insurance agencies and their interactions with overall health information systems; b) in accessing and utilizing data from national health insurance agencies to inform healthcare practice; and 2) to identify best practices and develop recommendations for the consideration of national health insurance agencies to maximize the value of their data systems. 2. PROCEEDINGS 2.1 Opening session Dr Xu Ke, Coordinator for Health Policy and Financing, Division of Health Systems, WHO Regional Office for the Western Pacific, welcomed the participants on behalf of Dr Shin Young-soo, WHO Regional Director for the Western Pacific. She acknowledged the “near-universal” coverage of social health insurance (SHI) in several Member States as their key strategy to achieve UHC. With rapid expansion of health insurance, large-scale data generated by the national health insurance agencies has the potential to provide insights to health policy-making and programme planning. Similarities in core processes of these agencies may lend to cross-country learning, while issues around access, availability, privacy and confidentiality need to be sorted out collectively. Dr Xu expressed hope that the two-day meeting would identify practical strategies to facilitate the use of health insurance data to support UHC. Dr Rani presented the meeting objectives and stressed that the meeting would focus on sharing country experiences in structuring national health insurance data systems, the interaction of these systems with overall national health information systems, and the accessibility and use of these data to inform health policies and programmes. Expected outcomes included actionable recommendations for national health insurance agencies to consider in maximizing the use of their data systems. 2.2 Plenary session 1: setting the scene 2.2.1 Current and potential data from national health insurance systems: role in informing public health policies and programmes
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Conceptually, four primary data pools can generate big data in health care. Clinical data are generated at each patient-provider interaction. The growth of mobile data and social media generate enormous amounts of data on behaviour and sentiments of patients that may allow tracking of disease pathways, help in early detection of disease outbreaks or trends in lifestyle and nutritional habits. Clinical trials may lend insights into a drug’s therapeutic mechanisms of action or their side-effects and toxicity. The near universal expansion of SHI (e.g. in China, the Philippines and Viet Nam) may provide substantial insights on the use, quality and cost of health care. "Big data" has volume (requires scalable storage), variety (aggregation of many types of data: structured and unstructured, from multiple sources), and velocity (continuous additions to data with real or near-real-time processing). Health insurance data provide real-time data on enrolment and claims. This provides substantial information on demographic and socio-economic characteristics of enrolees as well as data on diagnosis, medication, quality and cost of care. It also has huge potential for links with other databases. Health insurance data has the potential to link inputs to health care with health outcomes. Several factors differentiate SHI data from traditional health information systems: SHI data capture both private and public provision of care; can be tracked over time; and can triangulate with other traditional data sources like disease surveillance systems. SHI data generates big data that can be analysed to inform overall health policy and planning. 2.2.2 Experience of data systems of Medicaid and Medicare in the United States of America Centers for Medicare and Medicaid Services (CMS) is the largest single payer for health-care services in the United States of America, expected to serve over 125 million people in 2016. With the new reform focus, CMS will improve the ways providers are incentivized, care is delivered and information is distributed. CMS data can provide insights to make the health system more transparent, affordable and accountable. All health system stakeholders can benefit from a vibrant health data ecosystem. CMS is employing advanced analytics to create actionable information, routinely and safely sharing data with various actors to drive health-care quality and efficiency. The United States Government has committed to greater data transparency and CMS has made data sharing efficient through its Data Navigator. Legal authorities have also supported this by authorizing CMS to release data to specific entities while placing restrictions on the type of data that can be disclosed and to whom. CMS must continue to balance multiple competing interests regarding data release practices to protect the beneficiaries of CMS. To date, CMS data sharing has facilitated care coordination, performance evaluation of providers and fuelled research for health-care innovation. Discussion points: • The Government does not spend much in data sharing but CMS is a self-funding institution that raises revenues from sales of CMS data. There are also intangible benefits derived from data sharing in terms of groundbreaking research and health-care delivery insights that result in better care at a reduced cost. While CMS data only captures the data on Medicare and Medicaid beneficiaries, there are mechanisms that compare CMS with non-CMS. Health Care Cost Institution (http://www.healthcostinstitute.org/about) has assembled claims databases from all health insurers including CMS. Also, many state-based databases incorporate CMS and private insurance databases. As CMS only covers specific populations under Medicare and Medicaid, the data between CMS and private health insurers may be different. CMS has established metadata standards that are a model of transparency and data in providing data and data analysis training. The Department of Health and Human Services has mandated all entities under e Health Insurance Portability and Accountability Act (1996) to transition from the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD 9-CM) to the tenth revision (ICD 10-CM). The process has been smooth and analytics should be available soon.
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Standard measures for provider performance are developed through consensus but the debate can last for a long time. Agreed measures are few and provide dubious information. Alternative provider performance measures are designed to encourage innovation in the performance measurement field.
2.3 Plenary session 2: status of data systems of national health insurance agencies 2.3.1 Content and scope of health insurance data in the Republic of Korea The NHIS is a compulsory single insurer covering 97.2% of the population. Over 90% of medical providers are in the private sector and the provider payment system includes Diagnosis-related Group (DRG), fee for service and per diem. Generally, patients pay 5–20% of medical expenses and 100% for non-essential services. The NHIS manages eligibility, collecting premiums, paying reimbursement and managing service quality. The National Health Insurance Database (NHID) is constructed along these core business processes. This database contains beneficiary information; medical services and health screening that are integrated into NHID through individual ID linkage. From NHID, analytical variables can be defined and made available to inform public policies, monitoring of noncommunicable disease and clinical practice guidelines but not for private profit. Integrating data from services for individual health promotion, health screening, medical management and rehabilitation can facilitate epidemiological studies that help prioritize public health programmes. About 2% of a sample cohort from NHID are provided for free for public health research. Moving forward, there are plans to integrate NHID with other public health data, such as electronic medical records (EMR), climate and pollution and spatial network data. 2.3.2 Status of data systems of Australia's National Health Insurance Agency Different data are collected and managed by various institutions. The largest database is the Medicare enrolment database which contains information on around 23.4 million people (including 618 533 new enrolments). This is supported by the Consumer Directory, which contains all Medicare customer records. Other sources of data include the claims database and the National Health Services Directory, which allows health professionals and consumers access to reliable and consistent information about health services. Data are collected through various mechanisms: (1) general Practice clinical data are collected by doctors through specialized software; (2) Australia Bureau of Statistics Patient Experience Survey is conducted annually to collect data on access and barriers to a range of healthcare services; (3) Bettering the Evaluation and Care of Health (BEACH) surveys 1000 general practitioners for 100 of their consults each year; states and territories collect data on hospital, outpatient and emergency services; and, (4) Medicare Australia Statistics include Medicare and pharmaceutical benefits schedules, childhood immunization and organ donor registers and incentive payments, which are available to the public. Release of the above data is dependent on the purpose, anonymity checks, consent from identified entities, secrecy provisions and privacy. Linking inputs and outputs and analysing variations in clinical practice are possible with available data. Finally, linking databases can be tremendously useful in policy-making. 2.3.3 Achieving UHC: lessons from Thailand The Universal Coverage (UC) policy of Thailand follows the National Health Insurance Framework that is guided by the national policy on target or prioritized population. At the beginning of National Health Security Office (NHSO), the benefit package, the formulary provider rates, eligibility requirements, provider policy engagement and premium setting were laid out. These policies provided the rules for the fundamental system including beneficiary management, provider management, premium collection, claims management and accounting. Using feedback and change management, data from fundamental systems are used to analyse programme performance in terms of care management, benefit utilization and provider quality management. The analysis of programme performance can then inform UC policies. The core businesses in UHC include strategic planning, health-service provider registration, beneficiary enrolment, fund management, health service quality control and consumer protection. The data from NHSO were used to process provider payments, facilitate e-financial tracking, pharmaceutical tracking and management (e.g. making available 8
antivenin and antidotes), collaboration with other agencies in ensuring availability of essential medical products, improving quality of care, providing information to UC beneficiaries and informing annual budget planning, among others. A unique citizen smart card has been established to consolidate a citizen profile, including utilization of health services, screening data and disease registries. 2.3.4 Status of the data system of the New Rural Cooperative Medical Scheme in China The New Rural Cooperative Medical Scheme (NRCMS) is one of the three basic medical insurance schemes in China. The NRCMS provided coverage for 736 million rural residents and migrant workers in 2014. The data collected and stored by NRCMS agencies at the county level includes enrolment, medical services and claim information. NRCMS agencies at the county level are required to report some index regularly to NRCMS agencies at the provincial level, who then summarize and report relative index to the national NRCMS agency, the Center for China Cooperative Medical Scheme. Data collection and reporting of NRCMS are regulated by the Statistical Investigation System of NRCMS as authorized by National Bureau of Statistics. The county level analysis of NRCMS data includes medical utilization and medical expenses which become the basis for policy adjustment and negotiation with health-care providers. The national NRCMS publishes some index through the National Health Statistical Yearbook and Health Development Statistical Communiqué, which are open to the public. Challenges in data management include quality control of raw data submitted by county agencies and the connection between the NRCMS information system and hospital information systems, the regional health information platform, urban medical insurance information system, among others. There is also limited analysis of raw data by clinical and academic researchers because the raw data is owned by NRCMS at the county level. 2.3.5 Health Insurance in Viet Nam: content and scope of data The Ministry of Health develops health insurance policies while Viet Nam Social Security (VSS) implements national social insurance policies. Medical claims review is done to manage health insurance programmes. Claims are paper-based so analysis is done manually. An IT management system for VSS is being developed. Discussion points: • The collection and data management of SHI varies across countries. Some countries have one mandated institution to do big data analytics (Republic of Korea, Thailand, Viet Nam) while in other countries, various agencies are responsible (Australia, China) which makes national or comparative (e.g. geographic and demographic) analysis of health-care utilization or cost difficult (China). • The purpose of SHI analysis depends on the level of maturity of the health insurance system. The analyses range from claims analysis to guide health insurance operations (China, Viet Nam) to improving access and quality of services (Australia, Republic of Korea, Thailand). • Data security and privacy are key issues in data sharing. To address these, stringent requirements are established before health insurance data are shared (Australia), limiting data sharing by research purpose (the Republic of Korea) or by data ownership (China) and using technology to anonymize individual data (the Republic of Korea, Thailand). • Data linkage between health insurance data and other health databases can be done through unique individual ID number (Republic of Korea, Thailand), semantics or both (Australia). These are in various levels of development across countries. 2.4 Group work 1: streamlining the scope, collection and analysis of data from national health insurance agencies: identification of best practices
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Participants were divided into two groups to share experiences using semi-structed questions to guide discussions. Group one focused on countries that have a single health insurance system: Mongolia, the Philippines, the Republic of Korea and Viet Nam. The group was facilitated by Dr Jong Heon Park of the Republic of Korea. Group two countries had multiple SHI schemes: Australia, China, Japan and Thailand. Group two discussion was facilitated by Mr Ian Crettenden of Australia. Semi-structured questions were used to guide the group discussions. Key points from group one: • The potential utility of health insurance data systems in validating or supplementing other data. In the Republic of Korea, the cancer registry is collected by the National Cancer Center. NHIS are not yet connected because of national policy on personal privacy protection. Infectious diseases are reported to Korea Centers for Disease Control and Prevention and these data are not linked to health insurance data. Patient need to agree in order for treatment/claims data to be shared. In other countries, disease surveillance is done by the Ministry of Health not by SHI agency. Some specific information are required for cancer registry but not in claims data. Similarly, civil registry is not mandated to collect information beyond civil registration data. In the Philippines, although the databases of the Department of Health collects reports on infectious diseases (e.g. pneumonia or diarrhoea), PhilHealth increasingly validates hospital claims on these diseases to check for fraudulent cases. Good practices in improving the visibility and accessibility of social insurance data. Most countries in the group are in the early stages of integrating different databases. In the Republic of Korea, plans to connect EMR and claims data are in the consultation stage. In Mongolia, more individual data will be collected under the national insurance law this will be facilitated by individual ID card with chip. The Republic of Korea shares social insurance data for researches but not to the private sector. Identify missed opportunities for additional data collection. The group suggests a minimum set of data to be collected from individual beneficiary of health insurance to avoid burdening the system. For multi-agency use, there should be common standard in terminologies, definitions, coding and procedures for converting data from manual forms. Assess strengths and limitations of health insurance data systems to inform overall health policies and programmes: national health insurance agencies verify claims to validate information, to ensure quality care and prevent fraud. In the Republic of Korea, HIRA compares the diagnosis with prescribed medicines and does drug utilization review. The claims review is done in two steps: 1) electronically; and 2) peer review. In Viet Nam, providers should follow the Clinical Practice Guidelines (CPGs) and non-compliance may lead to non-payment of the claim. However, more than 17 000 services do not have practice guidelines. Claims review is done on sampled claims. In Mongolia, peer review is used in claims review since there are not yet recommended CPGs. In the Philippines, claims review is done: 1) electronically by looking at excessive claims of particular conditions (e.g. pneumonia); and 2) by peer review through the national Quality Assurance Committee. Areas for collaboration include: (1) sstandardization of enrollment format across countries, identifying variables for administrative as well as research use; (2) measurement of quality of care by diagnosis; (3) technology and good practices in data warehousing/data management system; (4) good practices in capturing data in remote and hard-to-reach areas; and (5) good practices in open data sharing and how to establish the system.
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Key points from group two: • Potential utility of health insurance data in validating or supplementing other data systems: some national health systems are not well-linked to health insurance systems due to vertical programmes which have developed their own health information systems. Australia only has administrative data. Clinical information and diagnoses are not available. In Thailand and Japan, diagnosis information is available. However, at the data source level, disease coding 10
should be accurate if it is intended to generate evidence and inform policy. National health insurance systems may also assess their ability to collect additional data with reasonable quality to ensure credibility and confidence in health insurance data. • Usefulness of unique patient ID in analysing health insurance data: in Japan, patients are free to choose their health-care providers, so if a patient visits multiple facilities, they will have more than one patient ID. China uses the citizen’s unique civil registration ID that is lifelong and connected nation-wide. But the databases for each of the three national health insurance schemes are collected and housed separately, which poses a challenge when a person crosses over from one insurance scheme to another. • Data sharing across agencies and sectors: in Japan, analysis of claims data is used by the Government to suppress health costs and for health research. Allowing the private sector to use data is being studied in the context of ensuring data privacy. Similarly, in China, different ministries do not share data, citing privacy as a concern as there are rules for data sharing between agencies. In Thailand having a robust set of insurance beneficiary data at the national level is useful to link multiple databases. As a country's information and data landscape grows in size and complexity, linking databases together rather than merging them might be more efficient. Thailand recommends a body to maintain the comprehensive list of beneficiaries under the insurance scheme and to set up data-sharing standards to link multiple databases. • Improving access and visibility of data: Australia suggested creating an independent national agency setting national standards for data, overseeing data collection, and analysing and reporting of health insurance database. Policies on information accessibility and transparency can be guided by an agency's steering committee, guided by participation from researchers and members of the public. Data privacy policies need to be balanced with each country’s desire for access, visibility and linking of its health databases. • Missed opportunities. Various factors in data architecture and management lead to missed opportunities, ranging from long claims processing time (Japan) to separate databases and separate agencies managing them (Australia) and limited health business understanding of IT staff to ensure suitable design of IT systems . A nationally designed health informatics system that automatically captures both clinical and administrative data can reduce the administrative cost of data submission and allow for timely analysis (the Republic of Korea). Moreover, there is opportunity to involve private health-care providers when they are required to submit health data for insurance claims. • Good practices in analysis: data triangulation between insurance reported data (with the risk of up-coding and over-reporting) and disease surveillance systems (usually underreported) can provide opportunity for establishing the true incidence of diseases. National health insurance data and expenditures can also be used to forecast future clinical needs and projected costs in countries with ageing populations. The health insurance data system can generate information on the practice and prescribing behaviour of health-care providers. • Assess strengths and limitations. Health insurance systems are good at counting billable items, for example counting the number of episodes/activity that is paid by the system. However, there are limitations in encoding data where some activities might be combined in a single activity/payment. In addition, demands from policy-makers for more information from the health insurance database can increase the burden of collection and increase payments to providers. • Potential collaboration across countries: using international health informatics (e.g. ICD, SNOMED) and pharmaceutical standards can foster collaboration on big data analysis and health system studies. Countries can also collaborate on the design and implementation of data privacy guidelines, taking into account national privacy regulations and laws.
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2.5 Plenary session 4: strategies, initiatives and challenges in powering data systems using IT solutions to improve accessibility, timeliness and quality 2.5.1 IT solutions in health insurance data in the Republic of Korea There are five fields in NHIS information and communication technology (ICT): (1) health insurance system, (2) collection integration system, (3) long-term care system, (4) customer care support system, and (5) management support system. Through the Health Insurance Act, the ministries of interior, health and welfare, justice, national defense, and patriots and veterans affairs provide the individual registration information to the NHIS through the National Administration Network Database system. This system was launched 20 years ago. After receiving these public data, NHIS link individual member, qualification, business place, and long-term care qualification information to the Eligibility Management System. To collect premiums, individual income, tax and pension information are connected in the NHIS database. With these data connections, personal premiums can be calculated and the insured notified through letters. As for payment and medical service quality management, claim data, review and assessment data are provided by medical institutions and HIRA, which is computerized almost 99.9% of the time. The Electronic Data Interchange claim system between medical institutions and HIRA was developed by a private company in 1996. Medical institutions and HIRA have both paid a commission to that company. However, HIRA developed its own claim system in 2011. As of June 2013, 87% of medical institutions are using HIRA's system. Similarly, the EDI system between workplaces and NHIS was developed by a private company but NHIS developed its own claim system. Over 90% of workplaces are now using the NHIS EDI system. The Republic of Korea has implemented an identification registration number from birth, so it is easy to link data from other sources. Using this, the NHIS can trace health records from birth to death. Plans are being prepared to integrate NHIS data with other public health data, such as EMR, climate and pollution and spatial network data. Once integrated, the NHIS can produce high-quality data, improve data availability, link with new industry, develop high-value services and provide outreach public services. 2.5.2 Technology to improve the efficiency of the health system in Australia Medicare is Australia’s universal health-care system, providing access to free or subsidised health care, with options to also choose private health services. Medicare has three pillars: (1) medicare benefits schedule (MBS) that gives all Australians access to health services with priority according to clinical need (http://www.mbsonline.gov.au/internet/mbsonline/publishing.nsf/Content/Home); (2) pharmaceutical benefits schedule (PBS) that provides Australians with important medicines at an affordable price (http://www.pbs.gov.au/pbs/home). Use and linking of these two databases are governed by strict privacy guidelines (https://www.comlaw.gov.au/Details/F2008L00706/d3789f13363d-49af-8736-d77be9ad76f0) and requests to link must demonstrate the public interest; (3) national health reform (NHR) is mainly for establishment of DRG system, providing basis for activity-based funding to create the national efficient price. The key components of Enterprise Data Warehouse (EDW) include Informatica (which also provide uploading data and provides submission portal for hospitals), COGNOS (for reporting for and within the department), SAS (for data mining, forecasting and analytics) and ESRI (for geospatial information) softwares. These three software tools help in leveraging the best value for the data warehouse. In addition to MBS and PBS, EDW also contains other data from private health insurance, hearing services, cancer screening register, grants administration, service quality surveys (e.g. hand hygiene, medication charts), detecting medicine adverse events and corporate data (human resources and finance). Finally, a governance structure for data stewardship has been established to define the hierarchy of authority in managing, updating and sharing data in EDW. Major projects include Digital health, which contains the electronic summary of patient health records for individuals and health-care providers; the National Health Cost Data Collection that captures hospital activity data to develop the national efficient price; and the Australian Institute of Health and 12
Welfare Mental Health Portal. Future projects include the Multi Agency Data Integration Project to link data for health insurance, census, taxation and social security to inform public policies; EDW Secure Access Portal that will provide access to EDW to the public; adverse drug reactions for analytics on registry of drug adverse events in conjunction with PBS data; and making 10% sample of MBS and PBS data publicly available at data.gov.au. 2.5.3 Achieving UHC in Thailand: lessons for health insurance IT Universal Coverage (UC) providers have to be registered and approved for quality assurance before becoming contracting units under the UC scheme. Every provider is assigned a unique hospital code related to their bank account number in order to be paid electronically. Health service providers are given a five-digit running ID code that is generated by Ministry of Public Health at the time of registration. This system is used in provider enrolment, beneficiary enrolment and payment via the online banking system. Previously, UC beneficiaries could sign up to any health-care provider to get their UC card, which transmits their data to NHSO through the provincial health centre, causing redundancies in the data and resulting in up to 12% errors (40 000 to 50 000 records) every month. The current design is that membership data is connected to several government databases including Social Security Systems, and Civil Servant Medical Benefit Scheme, the Immigration Department, the Ministry of Interior and from NHSO registration data. It took a while to establish real-time validation and use enterprise application integration. The error now is zero and the coverage rate is close to 100%. The smart card is now used to authenticate the person carrying the card at the point of care. This time the UC card becomes the national ID card. The updating of the person’s information retrieves from two databases: from the Ministry of Interior for personal data and from NHSO eligibility data. Along with implementing IT systems to support the NHIS, change management for stakeholders is needed. Despite high facility-based deliveries in Thailand, about 7% of births were not registered. This necessitates linking the birth data from hospital delivery rooms to the Ministry of Interior's database. The data can then be prepared for issuing the national ID and birth certificate. The birth defect registry was created in 2012. To capture congenital anomalies among older populations, data with matched ICD codes that begin with “Q” (i.e. congenital anomaly) from inpatient and outpatient services are retrieved to enhance the birth defect registry. Claims payment is submitted online but payment comes from different sources and is transferred in tranches, which may cause confusion. Processing time has reduced and payment is singular. Hospitals can view the real time processing of their claims. A system was developed to track purchase orders of health facilities for medicines, orphan drugs and vaccines. Facilities can track the delivery process for the logistics that they need. Audit is done electronically as all data are in the NHSO database. NHSO has also established a call centre information system for beneficiaries and providers. Initially, most of the calls were complaints but over time more than 98% of the calls are requests for more information. 2.5.4 IT support for the National Health Insurance Program in the Philippines PhilHealth has established its Operations Management Information System (OMIS) that captures data from national health insurance core processes: member enrolment, premium collection, accreditation of health providers and payment of benefits. This system is connected to several information systems that inform financial, administrative, executive and legal services management functions. These systems are either being enhanced or in development. The system captures demographic data (sex, date of birth, residence), social characteristics (workplace type, ethnicity, etc.), health status (disability status) and health behaviours (smoking, alcohol, etc). Additional information at the primary care facility level includes physical examination (blood pressure, body mass index), laboratory tests (e.g. blood sugar, cholesterol level) and first line 13
outpatient medicines that are included in the primary care benefit (PCB) package. In in-patient care, diagnosis is captured using ICD-10 coding. Use of services (inpatient/emergency/intensive care episodes, length of stay, medical procedures (both diagnostic and treatment), information on drugs prescribed, etc.) can be extracted if the facility is submitting electronic claims. While possible analyses are still limited, PhilHealth monitors the trend of claims data to identify possible fraudulent claims and to assess the quality of services. These have to be validated through health facility visits and domiciliary investigations. Individual patient data captured through EMR by health facilities that are providing PCB are stored in a common repository, which the Department of Health can access for analysis and surveillance. For inpatient services, PhilHealth may provide aggregated data through reports for payment and monitoring. The Philippine Health Information Exchange is being developed to facilitate exchanges between health-care providers and between PhilHealth and the Department of Health. Efforts in data warehousing are supported by strong business management sponsors with the vision of potential impact of data warehousing. A joint business partnership has been initiated to establish the health sector’s enterprise architecture and do gap analysis. The analytic culture is being strengthened in performance and fraud monitoring and financial sustainability. Technical feasibility is achieved through hiring of an expert, capability-building and outsourcing some system development. 2.5.5 Health information construction in China The health system reform in China aims to establish a basic medical health system for rural and urban residents and to deepen the reform in pharmaceutical and health-care systems. A key strategy in achieving this goal is construction of the health information architecture to support the expansion of medical insurance, improve the drug system and health facilities operations, facilitate public hospital reforms and incorporate reforms in related fields. This work build on stage one of the reform (1980s– 1990s), which established the business system and stage one (2003), which enhanced the public health information system. In 2007, national direct reporting of health statistics was established and 90 000 health facilities started to submit monitoring data on monthly, quarterly and yearly bases. EMR, a hospital information system, a regional health information platform and the national reservation for diagnostic service platform were also established. Capacity for comprehensive health management was built by mobilizing multidisciplinary teams and using technology for timely and quality care. The design of health information under the 12th Five-Year Plan consolidates data from various health services, the Electronic Health record (EHR) and Electronic Medical Record (EMR) databases and reporting from the national, provincial and prefecture levels into the regional health information platform. It also lays out the national deployment of a telemedicine information system. The resident health card consolidated four functions: resident identification, storage for basic health information, cross-institution and cross-regional treatment and payment and financial applications. The resident health card is used for each step in health care up to payment, across every stage of life, for all types of health care at all levels of facility. The information blueprint supports the ideal of “health security for all”, considering the development of new technology to facilitate delivery of services from primary care to hospital care. 2.5.6 IT application for health insurance in Viet Nam Health-care providers in Viet Nam do not use IT tools to capture health services data or to manage their facilities. There are no links to health insurance or sharing of EMR. There is also no national health or health insurance database. Some in-house IT applications are used. These lack standardization and do not facilitate data linkage among stakeholders. IT applications used by healthcare providers are developed by different private companies. In April 2015, data linkage has been piloted at four levels in six provinces. In June 2014, the Health Minister temporarily issued a coding system to pilot in management of health-care services and health 14
insurance payments. The VSS and the Ministry of Health are jointly implementing the pilot linkage and electronic claims review of health-care cost in three provinces. The vision of health insurance IT infrastructure is to reduce the time of health insurance cost balance procedures from 75 to 40 days and the processing time for direct payments from 60 to 40 days. From 2016, favourable conditions will be created to simplify health insurance operations and by 2018 IT linkages should be established between the VSS system and health-care provider systems to simplify administrative procedures, improve the effectiveness of health insurance claims review, and management and usage of funds. The development of the IT application plan (2016–2020) moves towards a system that is centralized, integrated and a single health insurance data centre to ensure linkages in volume, variety and velocity. There will be back-up and disaster recovery systems. The plan also includes the issuance of a unique ID number for members, continuing administrative reform to include electronic transaction and reengineering business processes, nationwide implementation of a coding system for health-care management and health insurance payments, and strengthening VSS to implement data linkage, electronic claims and review of health services at all levels. 2.5.7 IT solutions in health insurance in Japan SHI business processes in Japan are IT-enabled, including enrolment of beneficiaries, claim submission and processing, premium collection, health facility accreditation and management of patient records (i.e. linkage with facility-based EMR/EHR). However, other potential IT uses remain. These include validating the patient’s eligibility to use health services under health insurance, shorten the claims processing and payment times, and claims audit. Although the data from National Data Base (NDB) of Japan NDB are encrypted using a hash function, it is assumed that data are sensitive so users should take ultimate care in handling. The personal information protection law was revised recently to provide that clinical history is considered a very sensitive data which requires users to guarantee the opt-out system for human subjects. The law also puts some restrictions to connect these data with NDB in the future, even if a new personal ID is developed and applied to various health-care data. Health-care data should be used more efficiently and Japan is looking for the best way to make use of the data from various viewpoints. NDB management is fully supported by the Government and every five years the NDB system is drastically changed. NDB provides for data storage, where data were first encoded in CSV format. Since the form of NDB was required to follow old-style paper-based claims, there were a lot of omissions in the data and NDB was far from the ideal data warehousing. From 2015, the Ministry of Health, Labour and Welfare started to store NDB data in two ways; using the CSV version and the Data Warehouse (DWH) style, where other information such as diagnosis is integrated. Through DWH style data, users can get health outcomes via business intelligence interface. However, the accuracy of health insurance claims data is often questioned, and the plausibility of picking-up representative information from other complex data should be discussed more. 2.5.8 Status of data systems in Mongolia Similar to other countries, the SHI data flow follows the business processes of the health insurance programme, from beneficiary enrolment to payment/collection of premium contributions, utilization and payment of health services. In claims processing, the health insurance inspectors check claims electronically using the following criteria: (HIS): eligibility (insured or not), if more than three outpatient visits/specialty cabinet for a month, if diagnostic tests are more than 55000 MNT for a month, if the claimed amount is within the monthly budget of the hospital, if any duplication of inpatient service and if within annual allowance (1.8 million MNT). At the facility level, the health insurance inspectors or doctors check claims using the Health insurance Expenditure Research programme for readmission within three months, patient is discharged within three days, if surgery 15
was done (high cost DRGs) and the cost weight of DRG. Based on the foregoing criteria, the patient’s medical history is checked manually to see if the diagnosis relates to the health insurance benefits package; if tests match diagnosis; if treatment matches diagnosis; and the doctor's final note of treatment result. The beneficiary’s eligibility is checked online at the health facility using EMD3. Mongolia is working towards better integration of Health Information Organization Software with Health Info 3 and using the data centre for hosting, storage and data exchanges. There are also plans to introduce a health insurance smart card and to integrate the database in accordance with revised law. Health Information Department is planning to introduce DHIS2 software for quality indicators with the support of international experts. Social Insurance General Office of Mongolia faces the following challenges: (1) lack of capacity (human resources, infrastructure, finance) and software system of health information to introduce smart card and integrated database, (2) no unified instruction to check claims, especially relating to quality of care; (3) weak information systems to check quality of care and control fraud; and (4) there are no best practice guidelines within the programme, so they need to refer to external texts. There is also no way to record the reasons for their decisions within the system. Discussion points: • The total investment in IT infrastructure is not available in most countries since systems have been developed over time. For example, in the Republic of Korea, when ICT was integrated in 2006, all the databases were integrated in one data warehouse in a stepwise manner towards building the National Administration Network Database system where the eligibility of each beneficiary is managed. The Republic of Korea spent US$ 20 million to upgrade the Benefit system alone. The Philippines sets an annual budget for its IT system, which includes capital outlay and operating expenses, which is usually checked against another government agencies of comparable business process. Similarly, Mongolia sets budget of US$ 30 million for IT for three years. There is common understanding that while countries set budgets for ICT, the cost escalates with updates in technology. The eligibility of beneficiaries must be updated when health-care providers submit claims through HIRA EDI. If there are discrepancies between the claim and the eligibility status and after HIRA has informed the provider yet the claim has not been changed, then HIRA will inform NHIS and the provider will not be paid. In Thailand, authentication of eligibility is done at the point of care using the beneficiary’s smart card since the membership database is integrated with other government databases. The Republic of Korea's experience in setting up ICT for health insurance provides lessons for countries that are starting their IT systems. Countries like Viet Nam can design the system for the national insurance agency and consider links to the civil registration system. For multi-agency integration, the Australian Bureau of Statistics is used as the linking agency while the Department of Health provides the insurance data. Unfortunately, there is no national ID system in Australia that will allow integration of civil registry with the health insurance data. In Thailand and China, the health insurance card is linked to other ministries’ databases and has become the national ID card. In Australia, the digital health (EMR/EHR), sensitive health information can only be shared to a health-care provider if the patient allows it. There are also mobile apps for citizens to access their data. Similarly, Japan has legislated that patients may opt-out from sharing medical records. Work is underway in China, the Philippines and the Republic of Korea, in connecting EMR/EHR with other databases. In many countries, review of claims is two-step process: 1) using IT applications to look at the trends and consistency between medical condition and diagnosis, and between diagnosis 16
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and procedures/treatment; and 2) manual review of claims, with peer review in the Philippines and the Republic of Korea. In Australia, adverse drug reaction is self-reported by health-care providers and pharmaceuticals but this is a continuing regulatory space. • Establishment of a minimum data set is considered critical in building the health insurance database. In Australia, the minimum dataset for PBS and MBS was established in consultation with stakeholders but there is a continuing feedback loop. To check the quality of data, about 80% of work is used to validate rather than encode the data into the data warehouse system. For additional information needed for analysis, either aggregate data computation is used or data linkage between disease registry and MBS and PBS is established. While other countries may feel the need to collect additional data, they should be mindful of the burden of encoding data. Big data collection forms may burden health facilities, leading to higher service costs. Analysis that would require additional information to the minimum dataset may be done as a research project.
2.6 Group work 2: strategic and optimal use of IT to power data systems for better quality, efficiency and analytics: identification of the best practices Participants were divided into two groups, similarly to the first group work activity. Group one was facilitated by Mr Bobby Crisostomo of the Philippines. Group two was facilitated by Mrs Netnapis Suchonwanich of Thailand. Key points from group one: • Good practices in using IT for the NHIS include: o o establishing the minimum dataset to avoid over-burdening the system. This can be done by incrementally digitizing health data; standardizing terminologies, definitions and coding procedures for converting the data to facilitate multi-agency use of health insurance data. For example, in Australia, if the data quality is questionable; the report is returned for corrective action. Data validation process is necessary to ensure data quality. For claims data in the Republic of Korea, there is a review process to compare diagnosis with prescribed drugs. In Japan, inconsistencies with paper claims are corrected when they are digitized; establishing efficient way of collecting data by adding controls to the system to minimize errors e.g. drop-down menu, standardizing measurements (e.g. use of metric system), using zip code when typing beneficiary’s address, unique identifier or use of smart card for beneficiaries and use of QR code or bar code. incentivizing providers to use the health insurance agency IT system by paying claims faster (the Republic of Korea); and establishing data access policies. For example, Australia has a hierarchy of authorities to access health insurance data.
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Challenges in using IT systems: difficulty of electronically linking departments (Viet Nam) for policy development if e-signature has not been legalized. Setting up EMR is country specific, depending on the payment system, capacity to invest in IT system and acceptability of the new system to users considering internet availability in remote areas. Areas where IT applications will add maximum value include: claims processing for faster payment; medical audit (standards of care, identifying outliers); efficient data encoding and storage since recording/typing the same data will not be done multiple times for the same patient; eligibility checking; efficient provider and beneficiary registration. Potential collaboration with WHO Regional Office for the Western Pacific and across countries includes capacity-building and learning in-depth the good practices identified in other countries (e.g. Viet Nam visiting the Republic of Korea to observe the IT system for 17
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health insurance business processes or Thailand to better understand their DRG), providing comparison of country’s big data: scope, quality, analysis and networking to solve problems. Key points from group two: • Reflections on good practices or lessons learnt across the countries. National ID and social security ID can be linked to create a unique patient identifier in the NHIS. Thailand has a system of retrieving and using data from the health insurance database to supplement other databases (e.g. birth defect registry) to allow for more comprehensive analytics and evidence generation. Australia and Japan do not do have a national personal ID system. Standardizing data parameters and formats, through establishment of meta-data guidelines and standards, can be useful. Finally, national agencies can develop data entry and collection programmes for health-care providers to ensure standardization of health informatics tools and make it easier and less costly for providers to start submitting data (e.g. the Republic of Korea). Key challenges faced in digitizing the management information systems. In countries where the health system is fragmented, programmes and databases can be linked through established meta-data standards. In China different provinces use different informatics programmes. This makes it difficult to implement data collection and make related policy changes. This can be mitigated by governments taking a more active role in developing the health informatics landscape. Many countries also lack a national strategic plan and investment in health IT. For example, in China the Government is not prioritizing local health IT developments. In some circumstances, it may be difficult for policy-makers and IT developers to agree on development directions, especially if monopolies or oligopolies exist. In addition, frequent turnover in policy-makers may pose a threat to sustained and strategic development of the health IT system. Many countries voiced concerns about data and information privacy causing significant delays and obstacles for expanding developments in health information systems and databases. Areas where IT solutions will add maximum value include: (1) digitization of services that can improve access to health services through digitized means; (2) IT solutions that can reduce processing time and the rate of administrative/clinical errors (e.g. billing, medical prescription); (3) developing mobile apps for timely health information for populations can lead to more empowerment and more user-friendly engagement. These mobile apps can also be used to make appointments with health-care providers (China used the term "Cloud Hospital"); (4) digitized data facilitates secondary use of data from health information databases for research and evidence generation; (5) consider IT solutions for teaching and education purposes in health sector. These IT solutions and programmes can help overcome geographical limitations, add value for information collection and sharing, and better enable care continuity across providers. Reflections on the costs of developing IT solutions. Australia shared that smaller IT projects with shorter timelines can help manage risks and be more agile. Thailand used the term "spiral developments". This is especially pertinent due to the fast moving nature of IT. Small projects still need to be guided by an overarching framework or strategic plan. A key strategic decision for countries and institutions to consider is whether to develop in-house IT solutions and programmes or buy off-the-shelf programmes and pay subscription fees. For countries requiring non-English programmes, in-house development or partnering with local IT companies is more likely. Risks include increasing subscription fees, escalating costs in implementation of a new programme (for debugging, customization, training of local staff) and changing management programmes. Areas for potential collaboration across countries include: use of health information/insurance databases for public health research and generation of evidence for policy; development of regional guidelines for safe and ethical uses of health databases; cross-country sharing of experiences to promote learning and innovation; and making available national standards for health insurance database for countries to use as reference. 18
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2.7 Plenary session 6: synthesis, recommendations and closing session Areas were explored to enhance the efficiency of collecting data through NHIS, maximize the potential use these data to inform policy-making by linking with other public databases and design IT applications for efficient health insurance business processes. The national health insurance data systems, though not yet included as part of traditional NHIS, offer substantial promise to provide data on births, deaths, disease incidence, service utilization and other health system parameters. These data are generated as part of NHIS basic business processes: enrolment of beneficiaries and claims reimbursement. The origin and development of other components of national health information system e.g. disease surveillance systems, disease specific registries, civil and vital registration systems, and other reporting systems precede the development of NHIS, and being pursued by different stakeholders in isolation. Considering volume, velocity and variety, NHIS data offer advantages, especially in countries with near universal enrolment and provider choice, in terms of wide coverage, capturing data from public and private service providers (a major issue with other data sources that primarily collect data from public providers) and longitudinal data about a patient with unique ID. Use of standards in recording data in NHIS: inadequate use of standards or use of varying standards in recording the data in claims systems, may limit the utility of the data and may deter linkage of different data sources. Efforts to develop EMR/EHR for NHIS: efforts to develop EMR/EHR and e-claim systems for NHIS are being pursued separately, missing opportunities for integration and optimization. Obstacles to full integration include reluctance on the part of health-care providers to share clinical data and other privacy issues. Accessibility and utilization of NHIS data: the accessibility and use of data from national health insurance agencies should be ensured to maximize the returns. However, privacy, confidentiality and risk aversion are the major concerns identified in sharing the data. Use of IT solutions to power NHIS business process and data systems: countries are at different stages of overall IT infrastructure development and use of IT solutions to support their NHIS. IT infrastructure and design are affected by organizational structures, provider payment mechanisms and mandates of health insurance agencies across countries. Collaboration and sharing: potential areas for collaboration include: • • • • sharing experiences in linking databases within the NHIS and with Ministry of Health and other agencies systems; collaboration in the development of IT solutions and applications; collaboration in designing and implementing data privacy guidelines, taking into account national privacy laws; sharing experiences in identifying a minimum dataset, particularly the data fields collected as part of different business processes (enrolment, claims processing, and provider/health facility accreditation). co-learning in key analytics being generated by various countries; and experience in creative use of data to answer questions around health-care costs, provider payment mechanisms, quality of care and adherence to health-care protocols.
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3. CONCLUSIONS AND RECOMMENDATIONS 3.1 Conclusions The NHIS of countries in Western Pacific Region are at different stages of development with variable provider payment mechanisms, benefit packages and institutional mandates across countries. IT infrastructure and use of IT solutions to support NHIS are also in various stages of development and maturity across countries. The use of IT in NHIS is affected by organizational structures, provider payment mechanisms and mandates of health insurance agencies. Various areas were discussed to enhance the efficiency of collecting data through NHIS, maximizing the potential use of these data to inform policy, and designing IT solutions to support national health insurance processes. 3.2 Recommendations for national health insurance agencies National health insurance agencies may consider: 1) increasing awareness and visibility of NHIS data systems as a source of data to inform broader health systems policy issues, including quality of care, health-care costs and monitoring of epidemiological situations; 2) Proactively linking and optimizing interoperability, and triangulation of data from traditional health information systems and NHIS sources for mutual benefit; 3) standardizing data (including metadata) with respect to data fields and using international data standards (e.g. ICD-10 for disease diagnosis); 4) identifying opportunities for linkages and optimization of EMR/EHR application development and e-claim systems development; 5) developing transparent institutional, policy and regulatory frameworks based on a consultative process to underpin data sharing and access to other stakeholders. The NHIS should also share information on metadata to potential users; 6) engaging in long-term planning and investment to develop and design IT infrastructure, accompanied by change management and people skill development, to support NHIS; and 7) creating communities of practice.
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ANNEXES Annex 1. Meeting programme Day 1 Tuesday, 1 December 2015 Time 08:00– 08:30 08:30 – 09:00 Title Registration Opening Session: Welcome Remarks Dr Xu Ke, Coordinator ,Health Policy and Financing, on behalf of Dr Shin Young-Soo, Regional Director, WPRO Dr Manju Rani, Responsible Technical Officer, WPRO Moderator: Dr Tevfik Bedirhan Ustun Dr. Manju Rani, Senior Technical Officer, Evidence for Health Systems, WPRO, WHO Dr Niall Brennan, Chief Data Officer, Centers for Medicare and Medicaid Services, USA Presenter
Self-introduction of participants Administrative announcements/ Objectives and Outline of the Regional Meeting 09:00 – 09:35 Plenary Session 1: Setting the Scene Setting the Scene: 'Current and potential data from National Health Insurance systems: Role in informing public health policies and programmes Data systems of Medicaid and Medicare in USA: What has been the experience: (Skype presentation) 09:35 – 09:45 0945: – 10:15 Group photo (at the lawn) Coffee Break and Poster session: Know the Institutions Philippine Health Insurance Corporation National Health Insurance Service (Republic of Korea) Viet Nam Social Security (Viet Nam) Plenary Session 2: Current status of Data Systems of National Health Insurance Agencies • Content and scope of Korean Health Insurance Data
Ms Barbara de Guzman Dr Jong Heon Park Ms Phung Thi Mai Oanh Moderator: Mrs Netnapis Suchonwanich Dr Jong Heon Park, Senior Researcher Fellow / Big Data Steering Department, NHSO Ian Crettenden, Assistant Secretary , Health Analytics Branch, Australian DOH Mrs Netnapis Suchonwanich, Deputy Secretary General NHSO Ms Zhao Dong Hui, Associate Researcher, Center for China's Cooperative Medical Scheme
10:15 – 12:30
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Current Status of Data Systems of the National Health Insurance Agency ( Australia)
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Achieving Universal Health Coverage lesson learned from Thailand
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The current status of Data System of NRCMS in China
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• 12:30 – 13:30 13:30 – 15:00
Health Insurance in Viet Nam: Content and Scope of Data Lunch Break
Ms Phung Thi Mai Oanh, Deputy Head, International Cooperation Department, VSS
15:00 – 15:30
Group Work 1: (Conference Room 212 and 210) Streamlining the scope, collection and analysis of data from national health insurance agencies: identification of best practices for way forward recommendations. Group 1: Mongolia, Philippines, Republic of Korea, Viet Nam (Room 212) Group 2: Australia, China, Japan, Thailand (Room 210) Coffee Break and Poster session: Know the Institutions New Rural Cooperative Medical Scheme (China) Health Insurance Scheme (Japan) Plenary session 3: Reporting back from Group work 1 and Conclusions for the day Group 1: Mongolia, Philippines, Republic of Korea, Viet Nam Group 2: Australia, China, Japan, Thailand Reception hosted by the Regional Director (at Lower Lounge, Conference Hall)
Facilitator: Dr Jong Heon Park (Republic of Korea) Facilitator: Mr. Ian Crettenden (Australia)
15:30 – 17:00 15:30 – 17:00
Ms Zhao Dong Hui, Dr Genta Kato Moderators: Dr Ute Schumann and Mrs Netnapis Suchonwanich Rapporteur: Ms Michelle Avelino (Access Health) Rapporteur: Dr Nicky Antonius (Australia)
17:30 – 18:30
Day 2 Wednesday, 2 December 2015 Time 08:30 – 10:00 Title Plenary Session 4: Strategies and initiatives to power data systems using IT solutions to improve accessibility, timeliness and quality of data systems and the challenges • Use of IT Solutions of Korean Health Insurance Data Presenter Moderators: Dr Nicky Antonius and Ms Pornpimol Sirimai
Dr Jong Heon Park, Senior Researcher Fellow / Big Data Steering Department, NHSO Dr Nicky Antonius A/g Assistant Secretary, Director Reporting & Analytics Australian DOH Mrs Netnapis Suchonwanich, Deputy Secretary General, NHSO Mr Bobby Crisostomo, Division Chief, ICT Planning, Policy and Standards Division, PHIC
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Technology to Improve Efficiency of Health System
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Achieving Universal Health Coverage lesson learned from Thailand
• 10:00–
The Philippine Health Insurance Data Systems
Coffee Break and 22
10:30
10:30 – 12:00
Poster session: Know the Institutions Social Insurance General Office (Mongolia) National Health Security Office (Thailand) Plenary Session 4: continued: Country Presentation: Strategies and initiatives to power data systems using IT solutions to improve accessibility, timeliness and quality of data systems and the challenges • Health Information Construction of China
Mrs Gantsetseg Tsend-Ayush Ms Pornpimol Sirimai Moderators: Dr Nicky Antonius and Ms Pornpimol Sirimai
Dr Dai Mingfeng, Assistant Researcher Centre of Health Statistics & Information, NHFPC Ms Phung Thi Mai Oanh, VSS/ Mr Vu Huy Diep, Official, Health Insurance Department, MOH Dr Genta Kato, Associate Professor, Center of Health Reimbursement, Kyoto University Hospital Mrs Gantsetseg Tsend-Ayush, Head, Policy Implementation and Planning Division, Health Insurance Department, SIGO
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Health Insurance in Viet Nam: IT Application
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Use of IT Solutions in Japan
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Current status of data systems in Mongolia
12:00 – 13:00 13:00 – 15:00
Lunch Break Group Work 2: (Conference Room 212 and 210) Strategic and optimal use of IT to power data systems for better quality, efficiency, and analytics : identification of the best practices for way forward recommendations Group 1: Republic of Korea, Philippines, Viet Nam, Mongolia (Room # 212) Group 2 : Australia, China, Japan, Thailand (Room # 210) Coffee Break and Poster session: Know the Institutions Australia’s Universal Health Coverage (Medicare) Mr. Ian Crettenden/ Dr Nicky Antonius Moderator: Mr. Ian Crettenden Rapporteur: Ms Barbara de Guzman (Philippines) Rapporteur: Dr Genta Kato (Japan) Moderator: Dr Manju Rani, Responsible Technical Officer
Facilitator: Mr Bobby Crisostomo (Philippines) Dr Genta Kato (Japan)
15:00 – 15:30
15:30 – 16;30
Plenary session 5: Reporting back from Group work 2 and Conclusions for the day Group 1: Mongolia, Philippines, Republic of Korea, Viet Nam Group 2: Australia, China, Japan, Thailand Plenary Session 6: Synthesis, recommendations and way forward Closing Session
16:30 – 17:00
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Annex 2. List of participants 1. PARTICIPANTS Mr Ian CRETTENDEN, Assistant Secretary, Health Analytics Branch, Australian Government Department of Health, GPO Box 9848 Canberra, Australia ACT 2601 ian.crettenden@health.gov.au Dr Nicky Antonius, A/g Assistant Secretary, Department of Health, PO Box 9848, Canberra City, Australia, Nicky.Antonius@health.gov.au Mr TIAN Dong Yue Principal Staff Member Department of Planning and Information, National Health and Family Planning Commission, No. 14 Zhichunlu, Haidian District, Beijing China 100191 Tel.: 8610 68792929, Fax: 8610 68792929, tiandy@nhfpc.gov.cn Dr ZHANG Xi Fan, Principal Staff Member, Department of Primary Health, National Health and Family Planning Commission No. 14 Zhichunlu, Haidian District Beijing China 100191 Tel.: 8610 62030651, Fax: 8610 62030651, xfzh_2008@163.com Dr DAI Mingfeng, Assistant Researcher Centre of Health Statistics and Information, National Health and Family Planning Commission, No. 1, Nanlu Xizhimenwai Xicheng District, Beijing, China 100044 Tel.: 8610 68791975, Fax: 8610 68792478, 18810321396@163.com Ms ZHAO Dong Hui, Associate Researcher, Center for China's Cooperative Medical Scheme, No. 38, Xueyuan Road, Haidian District, Beijing China100191 Tel.: 8610 82805250 Fax: 8610 82805250 rdzdh@126.com Dr Genta KATO, Associate Professor, Center of Health Reimbursement, Kyoto University Hospital, 54 Kawaharacho, Syogoin, Sakyu-ku, Kyoto City, Japan 606-8507 qq9f8hn9@kuhp.kyoto-u.ac.jp Mrs GANTSETSEG Tsend-Ayush, Head, Policy Implementation and Planning Division, Health Insurance Department Social Insurance General Office 3rd Khoroo, Chingeltei District, 13/1 Ulaanbaatar, Mongolia 15160 Tel.: 976 99293440 tsganaad@yahoo.com Ms MANDAA Gerelmaa, Senior Specialist, Social Insurance Policy Implementation Department Social Insurance General Office, Baga Toiruu 13/1, Khuulichid Street, Chingeltei District Ulaanbaatar, Mongolia 15160 Tel.: 99185814 Fax: 976 11 328030 gerel_nd@yahoo.com Ms Barbara DE GUZMAN, Senior Program Officer, Health Policy Development and Planning Bureau, Department of Health, 2nd Floor, Bldg 3, San Lazaro Compound, Rizal Avenue, Sta. Cruz Manila, Philippines 1003 Tel.: 639 989977375, mikeedeguzman@gmail.com Mr Bobby CRISOSTOMO, Division Chief, Division Chief, ICT Planning, Policy and Standards Division, Philippine Health Insurance Corporation, 709 Citystate Centre, Shaw Boulevard, Pasig City, Philippines, Tel.: 6376447, bobby_crisostomo@yahoo.com Dr Jong Heon PARK, Senior Research Fellow, Big Data Steering Department, National Health Insurance Service, (07276) 23, Yeongdeungpo-ro 3 gil, Yeongdeungpo-gu, Seoul, Republic of Korea Tel.: 822 3270 9699 parkjh@nhis.or.kr Mr Byeongsung KIM, Manager, Big Data Steering Department, National Health Insurance Service, (08028) 202-501, Omok-ro, Yangcheon-gu, Seoul, Republic of Korea, Tel.: 822 3270 9768 kbs@nhis.or.kr
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Ms Mi Sun PARK, Senior Staff Member, Big Data Steering Department, National Health Insurance Service, (01705) 62, Nowon-ro 28 gil, Nowon-gu, Seoul, Republic of Korea, Tel.: 822 3270 9697 misunpark@nhis.or.kr Ms PHUNG Thi Mai Oanh, Deputy Head , General Issues-ASEAN Division, International Cooperation Department, Viet Nam Social Security, 7 Trang Thi Street, Hanoi, Viet Nam Tel.: 844 39361776, Fax: 844 39361779, ptmoanh@vss.gov.vn Mr VU HUY Diep, Official, Health Insurance Department, Ministry of Health, 138A Giang Badinh Hanoi, Viet Nam, huydiep73@gmail.com
2. TEMPORARY ADVISERS Mrs Netnapis SUCHONWANICH, Deputy Secretary General, National Health Security Office 88/39 Tiwanon 14 Road, Taradkwan, Muang District, Nonthaburi 11000, Thailand netnapis.s@nhso.go.th Ms Pornpimol SIRIMAI, IT Project Manager, National Health Security Office, The Government Complex Building B, 2-4 Floor, 120 Moo 3 Chaengwattana Road, Lak Si District, Bangkok 10210 Thailand, Tel.: 668 969 6502, pornpimol.s@nhso.go.th Mr Niall J. BRENNAN (participating by skype) , Chief Data Officer, Centers for Medicare and Medicaid Services, Washington DC, United States of America, Tel.: 01 202-690-6627 E-mail: Niall.Brennan@cms.hhs.gov
3. CONSULTANT Dr Leizel LAGRADA, Consultant (APW), 24 Jade Manor Bethlehem Street, Merville Park Paranaque City 1709, Philippines, leizel.presentations@gmail.com
4. OBSERVERS Ms Michelle AVELINO, Research Associate, ACCESS Health International Philippines, (Joint Learning Network for Universal Health Coverage Secretariat), michelle.avelino@accessh.org Dr Ute SCHUMANN, Team Leader, Health Policy Planning & Financing Expert, EU Philippine Health Sector Reform Contract, BIHC, Department of Health, Bldg 3, Manila, Philippines, Tel: 2552296, Ute.Schumann@epos.de Dr Beatriz ZURITA, Technical expert with Philhealth, Health Policy Planning & Financing Expert, EU Philippine Health Sector Reform Contract, 30/F Tower II, RCBC Plaza, 6819 Ayala Ave. cor. Gil Puyat, Makati City, 1200, Philippines, Tel.: 63 2 859-5137, Beatriz.Zurita@epos.de Dr Antonis MALAGARDIS, Program Director, Regulatory Framework Promotion of Pro-Poor Insurance Markets in Asia (RFPI Asia), RFPI Office, Insurance Commission Complex, 1071 UN Avenue, Ermita, Manila, Philippines, Tel.: 63 2 353 1044-45, antonis.malagardis@giz.de Mr Dante PORTULA, Senior Advisor for Regional Policy, RFPI Office, Insurance Commission Complex, 1071 UN Avenue, Ermita, Manila, Philippines, Tel.: 63 2 353 1044-45, dante.portula@giz.de
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5. SECRETARIAT Dr Manju RANI (Responsible Officer), Senior Technical Officer, Evidence for Health Systems,WHO Regional Office for the Western Pacific, Manila, Philippines, ranim@wpro.who.int Dr XU Ke, Coordinator, Health Policy and Financing, Division of Health Systems, WHO Regional Office for Western Pacific, Manila, Philippines, Tel: 632 528 9808, Fax: 632 521 1036 E-mail: xuk@wpro.who.int Dr Clive TAN, Technical Officer (Hospital and Clinical Services), Integrated Service Delivery, Division of Health Systems, WHO Regional Office for Western Pacific, Manila, Philippines Tel: 632 528 9898, Fax: 632 521 1036, E-mail: tanc@wpro.who.int Dr Tevfik Bedirhan USTUN, Coordinator, Classifications, Terminology & Standards, Health Statistics and Information Systems, World Health Organization, 1211 Geneve 27 Switzerland Tel.: 41 22 791 3609 ustunb@who.int
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