ISPOR AFRICA NETWORK FORUM

Transcription

1 ISPOR AFRICA NETWORK FORUM Website: Established in January 2014 ISPOR AFRICA NETWORK Idea was discussed at ISPOR 18 th Annual International Meeting, New Orleans, 2013 To enhance development of new initiatives and activities that promote learning and practice of PE Member Chapters: ISPOR Egypt, Ghana, Nigeria and South Africa Newly established: ISPOR Uganda Chapter Last Forum: Economic Assessment in Africa : Development of Health Economic Evaluation Reporting

2 Health Care in Africa : Can Big Data Improve Outcomes? Moderator: Anthony Waka Udezi, MPharm, PhD (ISPOR Nigeria Chapter) Speakers: Gihan Hamdy Elsisi, RPh, MSc (ISPOR Egypt Chapter) Ernest Attuquaye Quaye, BPharm, MPH (ISPOR Ghana Chapter) Jacques Snyman, MBChB, MPharmMed, MD (ISPOR South Africa Chapter) THE CHALLENGES of BIG DATA IN AFRICA Presented by Gihan Elsisi, Msc, PhD Head of Pharmacoeconomic Unit Central Administration for Pharmaceutical Affairs

3 The Challenges of Big Data Big data refers to the tools, processes and procedures allowing an organization to create, manipulate, and manage very large data sets and storage facilities. The challenges include capturing, storing, searching, sharing & analyzing the data. 5 The Challenges of Big Data 1 Privacy of data Without privacy, safety, diversity, pluralism, innovation, our basic freedoms are at risk. 2 Access & sharing of data One challenge is the reluctance of private companies and other institutions to share data about their clients and users, as well as about their own operations. Obstacles may include legal or computational considerations, a need to protect their competitiveness, a culture of secrecy, and, more broadly, the absence of the right incentive and information structures. There are also institutional and technical challenges when data is stored in places and ways that make it difficult to be accessed, transferred, etc. 6

4 The Challenges of Big Data Engaging with appropriate partners in the public and private sectors to access non public data entails putting in place non trivial legal arrangements in order to secure: (1) reliable access to data streams and (2) get access to back up data for retrospective analysis and data training purposes. 7 The Challenges of Big Data 3 Working with new data sources brings about a number of analytical challenges The challenges are intertwined and difficult to consider in isolation, but for the sake of clarity, they can be split into three distinct categories: (1) getting the picture right, i.e. summarizing the data (2) interpreting, or making sense of the data through inferences, and (3) defining and detecting anomalies.

5 The Challenges of Big Data 3 Working with new data sources brings about a number of analytical challenges (1) getting the picture right, i.e. summarizing the data Fill existing data gaps and satisfy the increased requirements: new methodologies, strengthening of existing statistical tools (2) interpreting, or making sense of the data through inferences Strengthen capacity at all levels; strengthen partnerships and coordination The Challenges of Big Data 3 Working with new data sources brings about a number of analytical challenges (3) defining and detecting anomalies The existing IT infrastructure and capabilities have to be enhanced to support the processing of high volume and high velocity data from various sources. More advanced skills for data analysis and computational informatics are necessary. The government sector will need to invest considerable efforts to establish an authorizing framework, which fosters public trust and sets out the rules for access and use of automatically generated digital data with a personal content.

6 The Challenges In Egypt The most common barriers in most Egyptian governmental hospitals: 1. Scarce resources, 2. Lack of trained experts, 3. Reluctance of patient or inability to provide reliable data", lack/reluctance of health professional in recording complete patient data which develops a poor medical records system. 4. Lack of communication and information share across different entities/health facilities. 11 Thank you Gihan Elsisi, Msc, PhD Head of Pharmacoeconomic Unit, MoH Gihan-elsisi@hotmail.com 12

7 THE STATE OF BIG DATA IN AFRICA TODAY EXPERIENCE FROM GHANA by Ernest Attuquaye Quaye (B.Pharm, MPH) Principal Pharmacist, Medical Department, Ghana Cocoa Board, ISPOR Ghana Chapter OUTLINE Setting Country Demographics Facts and Figures of Health Sector in Ghana Overview of Health Sector in Ghana Introduction Data in Ghana Health management information systems HMIS Reforms / DHIMS NHIS Conclusion

8 SETTING Country Demographics Area land sq. km Population density per sq. km 99 Literacy Rate (2010 census) 71.5% STATISTICS Total population (2012) 25,366,000 GDP 2013 estimate $/Billion 33 Gross national income per capita 1,910 (PPP international $, 2012) Life expectancy at birth m/f 61/64 (years, 2012) Probability of dying under five 72 (per live births, 2012) Probability of dying between /227 and 60 years m/f (per population, 2012) Total expenditure on health per capita (Intl $, 2012) Total expenditure on health as % of GDP (2012) SOME FACTS AND FIGURES OF HEALTH SECTOR Total Health Facility 3219 Number of OPD Attendance 19,747,839 Outpatient Attendance per 0.81 capita Number of Hospital Beds 22,164 Hospital bed utilization % 59.8 occupancy Case fatality ratio for Malaria 1.7 HIV/AIDS (Adult Prev. rate 2009) 1.8 % The Health Sector in Ghana, Facts and Figures 2010, Ghana Health Service

9 OVERVIEW OF HEALTH SECTOR

10 DATA IN GHANA Introduction Yellowing pages of Ministry of Health annual reports can be found dating back as far as Routine (Patient encounters, civil registration,) and nonroutine (surveys & demographic surveillance) methods used. Data collected using different reporting forms are sent vertically to higher levels without being analyzed or used. Collection of data is also based on the request by multi lateral agencies external to the day to day functioning of health system for up dating global health profiles. From data to decision making in health, The evolution of a health management information system, 2001, Campbell, Adjei & Heywood Health Management Information Systems (HMIS) In Ghana Reporting forms have been used to extract information from registers and tally sheets and the information used in making decisions. Comprehensive HMIS in Ghana began in 1988 as information service platform for the Ghana Health Service to capture disease burden. MCH/FP of MOH initiated MIS development in 1990, covering medical care, disease surveillance, nutrition, environmental health & health education which was implemented in 3 out of 10 regions. A more comprehensive MIS was to be developed in the future embracing management indicators such as HR, supplies and finance. From data to decision making in health, 2001, Campbell Adjei & Heywood

11 HMIS Reforms Use of HMIS was not widespread (mostly in public sector with a few in the private sector). 2 main applications (Management of clinical business process & collection and aggregation of data for reporting purposes. Review of system revealed multiple information systems that do not communicate with each other resulting in data redundancy, duplications, inconsistencies and inadequate access. In 2006, The District Health Information Management System (DHIMS) was introduced for data management and piloted in 20 districts to address challenges associated with HMIS. DHIMS software DHIMS was scaled up in 2007 to all Districts in Ghana after a successful piloting Data captured at service delivery points is inputted at the hospitals and merged at the district level and transmitted to the region and finally at the national level. Migration of DHIMS to DHIMS2 (web base) was completed in five (5) selected regions In 2011 and currently in all regions to develop capacity for timely data collection, analysis and use. At the last Health Summit (May, 2014) it was agreed to hook the teaching hospitals, private sector and quasi government health institutions onto the DHIMS 2. GHS Annual Reports, 2007,2010 & 2011

12 National Health Insurance Scheme (NHIS) NHIS was established by the National Health Insurance Act 2003, (Act 650) to provide financial access to quality basic health care for residents in Ghana. Operational in over 155 district offices across the country with a total active membership >8.8 million representing >35% of the population. Over 3,000 facilities accredited to provide services to the insured. An analysis of the NHIS in 2012 revealed huge disparities between the DHIMS and the insurance data. Biometric registration for NHIS subscribers and the issuance of biometric cards started in It is expected to free up some bandwidth for data entry and other activities with the benefits that the biometric system will bring to the NHIS are a clean membership database, thereby preserving data integrity. The NHIS thus provides another platform to harness big data for national healthcare decision making.

13 Conclusion Data cannot speak for itself - Einstein Data is required to improve health service management through optimal information support and decision making. Health Sector has a large number of different management units working and generating large amount of data. Data collected or aggregated at these units are held in separate silos. Analysis between the DHIMS data and facility data showed huge disparities bringing into question the integrity of the data. There is thus the need for surveillance officers at all levels to validate data before submission into data pool. Main challenge has been availability of health information personnel (Trained Biostatisticians) at facilities and the pace of growth in the ICT sector needed to make timely decisions. GHS & NHIS are major institutions for harnessing national data for effective healthcare decision making. THANK YOU MERCI

14 Big Data in Nigeria: The Current Situation By Anthony Waka Udezi, PhD Assistant Dean, University of Benin, Nigeria, ISPOR Nigeria Chapter Big Data Large and complex data sets Structured or unstructured Requiring large storage Difficult to analyze with current traditional data processing applications Big Data market >$200billion

15 Health Care Data Today Manual paper based hospital & pharmacy records Multiple data sets that are not linked Localized in different units Not easily accessible for research No infrastructure to monitor data and evaluate it hence can t measure burden of ill health in real time Data protection and ownership issues Lack real time disease surveillance and data monitoring Rely on few sentinel sites and modelling estimates to track the spread and prevalence of disease Investments in Big Data Telecom firms.mtn ($1billion), GLOBACOM ($1.25billion) ETISALAT and AIRTEL ($3billion) Operators have started deploying next generation data mining technologies Use huge information from subscribers behavior Aim to reduce cost and increase revenue

16 Multinational Firms Offer Big Data Microsoft Run your Enterprise event March 21, 2013 IBM client data center launched in Lagos Feb 6, 2014 IBM provides access to enhanced cloud capabilities and extensive big data and analytics resources to academics and businesses to solve local challenges However focus seems to be on banks, telecom industries, and government ORACLE is also expanding it operations in Nigeria and recently opened an Abuja office Healthcare Hackathon Launched Feb 22, 2014 Public Private sector partnership Bill and Melinda Gates Foundation, International Partnership for Innovative Healthcare Delivery (Duke University), Center for Affordable Health Technology (University of Oxford) Building a health data hub for the Nigerian Health Sector to spark development in mobile based crowd sourcing applications, predictive, surveillance and data collection tools, visual data analytics etc. Public agencies will provide data health care data from 24 states will be available Private agencies will develop innovative technologies to solve real public health challenges

17 Big Data in Nigeria: The Potentials IBM has predicted that Big Data in Nigeria will grow by 80% in 2015 propelled by proliferation of mobile phones and tablets Phone users has grown from in the year 2000 to 140million (87% penetration) Internet users = 65million: huge potential for targeted health promotion Phones can help detect disease clusters which can improve national health strategies Linking wearable devices or a glucometer to cell phones with accelerometer can produce data that can aid individualization of therapy Records of deaths, births, stock levels, travel patterns, access health records Genomic Medicine QUESTIONS, COMMENTS, FEEDBACK?

18 THANK YOU This presentation was prepared by the ISPOR Africa Network. For more information, please visit: Contact: