Data Sharing

In the current state of the health care system there is uneven access to primary care, and too many people struggling to navigate the system are receiving care in the hospital for issues that would be better dealt with in the community, and then are being readmitted to the hospital only days after leaving. To address these issues and improve efficient continuity of care, the Health Links program provides a new model of care at the clinical level in Ontario. In this model all of the patient’s health service providers in the community, including primary care, hospital, and community care, work together to create a coordinated care plan for the patient. The initial focus of Health Links is on highcost users. Health Links, and primary care as a whole, require comprehensive data analysis to effectively support patients and providers. SHIIP is a portal-based technology solution that enhances individual patient care while providing real-time feedback and summarized data to help plan care. The primary objective of SHIIP is to develop an Integrated Portal with core functionalities that will facilitate the sharing of information and enable person-centred care coordination. SHIIP aims to assists the success of Health Links by providing consistent maintenance and sharing of patient records, timely communication and collaboration between a patient’s multiple health care providers, and removing physical barriers through the virtualization of care processes. SHIIP is designed to identify and assist in the delivery of care for complex/high needs patients, and will facilitate reporting, performance monitoring and quality improvement efforts. Some of the anticipated benefits of SHIIP include: enhanced patient experience, reduced workflow duplication, improved access to information at point of care, more efficient clinical documentation, and improved health outcomes. Ultimately, SHIIP helps to improve access and quality of healthcare, and consequently health equity, especially for complex/high-needs patients.

Objective

To describe how the South Eastern Integrated Information Portal (SHIIP) will support the Health Links program with the delivery of care for patients, by facilitating reporting, performance monitoring and quality improvement efforts. The portal-based technology that SHIIP uses to integrate all of a patient’s clinical care information into summarized data and to provide real-time feedback will also be explained.

NBIC integrates, analyzes, and shares national biosurveillance information provided from capabilities distributed across public and private sectors. The integration of information enables early warning and shared situational awareness of nationally significant biological events to inform critical decisions directing response and recovery efforts.

The 2014-2015 HPAI H5 outbreak in the U.S. was the largest HPAI outbreak in the country’s history and resulted in the culling of millions of domestic birds and significant economic losses through loss wages, direct production losses, cost of recovery, consumer price increases, and trade restrictions.

NBIC worked closely with liaisons from USDA/APHIS and DOI/ NWHC over the course of the outbreak to integrate information from both agencies and open source reporting into reports and data sets providing early and sustained shared situational awareness to over 1400 federal, state, and local authorities.

Objective

The National Biosurveillance Integration Center (NBIC) coordinated information sharing with the U.S. Department of Agriculture (USDA/APHIS) and the Department of Interior (DOI/ NWHC) to integrate information and provide shared situational awareness of the 2014-2015 Highly Pathogenic Avian Influenza (HPAI) outbreak in the U.S. across all levels of government.

Sharing public health (PH) data and practices among PH authorities enhances epidemiological capacities and expands situational awareness at multiple levels. Ease of data sharing through the BioSense application, now part of the National Syndromic Surveillance Program (NSSP), and the increased use of SyS nationwide have provided opportunities for region-level sharing of SyS data. In addition, there is a need to build workforce competence in SyS given powerful new information technology that can improve surveillance system capacities. Peer-to-peer learning builds the relationships and trust among individuals and organizations that are required for inter jurisdictional data sharing.

Objective

Promote interjurisdictional syndromic surveillance (SyS) data sharing practices with a training model that engages participants in collaborative learning.

The outbreaks of Severe Acute Respiratory Syndrome (SARS) in 2003, influenza A (H1N1) in 2009 and Ebola in 2014 have shown increasingly that infectious diseases can spread globally in a short timeframe, affecting both high- and low-income countries. Taking action to mitigate the impact of future crises relies on sharing public health surveillance data across national borders in an efficient and effective way. However, data users, particularly in high-income countries, often use surveillance data, particularly from low- and middle-income countries, with little or no benefit to the data generator. As Indonesia’s refusal to share influenza virus sequences during the 2006 H5N1 outbreak illustrates, this imbalance increases reluctance to share and jeopardizes the global good that can be achieved. In order to share public health surveillance data internationally in an equitable way, technical, political, ethical, and legal issues need to be addressed. The Centre on Global Health Security at Chatham House is producing guidance that will address both the policy and technical issues with the aim of establishing new norms so that data can be shared in an open, transparent and equitable way.

Objective

To address both the policy and technical issues of sharing public health surveillance data across national borders with the aim of establishing new norms so that data can be shared in an open, transparent and equitable way.

The introduction of electronic health systems has led to easier collation, compilation, and analysis of data as well as easier access. For data to be put to be impactful use, it must be shared both for research and decision making purposes. Data sharing and release should neither compromise privacy nor lead to wrong conclusions. The need to share information that guides policies and decision making should be balanced with the need for the data to be reliable. The aim was to produce a data release policy to be used as a baseline tool to guide the practice of data release and sharing across programs and with outside requesters.

Objective

To describe the process of producing a universal data release policy for use by different programs in a state health department. 

Most countries do not report national notifiable disease data in a machine-readable format. Data are often in the form of a file that contains text, tables and graphs summarizing weekly or monthly disease counts. This presents a problem when information is needed for more data intensive approaches to epidemiology, biosurveillance and public health as exemplified by the Biosurveillance Ecosystem (BSVE). While most nations do likely store their data in a machine-readable format, the governments are often hesitant to share data openly for a variety of reasons that include technical, political, economic, and motivational issues. For example, an attempt by LANL to obtain a weekly version of openly available monthly data, reported by the Australian government, resulted in an onerous bureaucratic reply. The obstacles to obtaining data included: paperwork to request data from each of the Australian states and territories, a long delay to obtain data (up to 3 months) and extensive limitations on the data’s use that prohibit collaboration and sharing. This type of experience when attempting to contact public health departments or ministries of health for data is not uncommon. A survey conducted by LANL of notifiable disease data reporting in 52 countries identified only 10 as being machine-readable and 42 being reported in pdf files on a regular basis. Within the 42 nations that report in pdf files, 32 report in a structured, tabular format and 10 in a non-structured way. As a result, LANL has developed a tool-Epi Archive (formerly known as EPIC)-to automatically and continuously collect global notifiable disease data and make it readily accesible.

Objective

LANL has built a software program that automatically collects global notifiable disease data—particularly data stored in files—and makes it available and shareable within the Biosurveillance Ecosystem (BSVE) as a new data source. This will improve the prediction and early warning of disease events and other applications.