Design thinking methodology
In building the data collection and reporting system [3], we used the five-stage Design Thinking model proposed by Hasso-Plattner Institute of Design at Stanford [9, 10]. Prior to implementing the Design Thinking methodology, we also assessed the potential usefulness of The Cynefin Framework and the Eight Disciplines Problem Solving Process to understand which methodology would be most effective and efficient in an outbreak setting to design the data collection and reporting system [18, 19]. Design Thinking was chosen as it has documented use-cases in healthcare and public health, supports rapid prototyping, is non-linear, has a low-cost of implementation, and has a low barrier to entry requiring minimal training. Additionally, it is a collaborative methodology, which is particularly important during outbreaks and health emergencies when health systems are being pushed to their limits. This approach offers a flexible model which focuses on empathizing, defining, ideating, prototyping, and testing. Engaging end-users throughout the design thinking process is paramount to ensuring that solutions are developed to meet user needs. This is an iterative methodology that continues to adapt according to the needs of the system. Therefore, many of the stages do not need to be sequential and can be run in parallel, out of order and repeated as necessary [9, 10].
Empathizing
The design thinking process began simultaneously in Guinea, Liberia and Sierra Leone with observing, engaging, and empathizing with the current situation. This step allows for the removal of personal assumptions with a view of observing the problem through the end-user’s perspective [9, 10]. To do this, we met with Senior Leadership within the Ministries of Health and the Incident Managers to understand the challenges as it relates to the obtaining accurate and timely information and to obtain a landscape of all of the players whom would need to be involved. We then reviewed Ministry of Health and WHO daily situation reports (a document which details the number of confirmed, suspected and probable cases and deaths, and highlights the operational challenges). Next, we worked in the three countries alongside end-users and stakeholders who were collecting and analyzing the data as well as using the reports for decision making. Multiple one-on-one interviews and small workshops were carried out in Sierra Leone, Liberia, and Guinea in person and remotely via teleconference. Through these meetings, we were able to gain situational awareness and understand the unique needs that each stakeholder had in relation to data collection, management, reporting and decision making. Questions focused on understanding the current workflow, identifying bottlenecks, and the diverse end-user roles and responsibilities that the data collection and reporting system needed to support. Additional questions focused on understanding the short-term and long-term needs of the data. This information was written up after each interview and workshop and compiled into a central excel document for later review and prioritization. The key end-users and stakeholders were data collection officers, epidemiologists, information technology staff, data managers, laboratory personnel, technical experts, and senior leadership from the Sierra Leone, Liberia, and Guinea Ministries of Health.
Defining & ideating
Next, the end-user and stakeholder needs were defined (Fig. 1) and the problems were clearly identified and articulated. Through multiple brainstorming sessions, ideas were generated to address the list of needs and challenges of the end users and stakeholders. The ideas focused on features, functions and design characteristics essential to improving the data collection and reporting process and ranged from simple adjustments to the creation of complex systems. Prioritization of the ideas was based on speed, feasibility and flexibility due to the time constraints necessary to develop and roll-out the system.
Prototyping
With the information obtained during the empathizing, defining and ideating stages, it was possible to view the problem from alternative ways and to design new and appropriate solutions [9, 10]. During the prototyping phase, we worked closely with IT system engineers and computer programmers to design a solution that would fit the needs of the various end-users and stakeholders according to the required list of features and functions. The list of features included searchability, standardization of data, real-time access to data, data management including cleaning and validation capabilities, and visualization of data. The list of system capabilities included data ownership and access, security, off-line use, usability with limited internet connection, versatility of languages, flexibility and adaptability to various types of users.
Testing and redesign
A prototype of the data collection and reporting system was developed over the course of 2 months. During prototyping, the focus was on identifying the best possible solutions to address the problems and requirements identified in the earlier phases [9, 10]. Lastly, the system was tested internally by the Information Technology team and subsequently rolled-out to Guinea, Liberia and Sierra Leone. Once the system was implemented, the team had regular weekly calls to discuss operational challenges and to make necessary adjustments based on the specific needs of each country. This was an iterative process with alterations and refinements being made to the system after receiving valuable feedback from end-users [9, 10]. The system took nearly 1.5 years of iterations until it was maximized to its full potential. (Fig. 2).
Findings
From March 2014 through August 2016, the results of 256,343 specimens tested for Ebola virus disease in 47 laboratories across Guinea, Liberia and Sierra Leone were captured in the Global Ebola Laboratory database [3]. The value of the database was far reaching. It was used to orient the response at the district, national, and international levels within the three countries including generating situation reports, monitoring the epidemiological and operational situation, and providing forecasts of the epidemic [3]. It was also used to support additional Ebola-related public health interventions including the Ebola RNA persistence in semen of Ebola virus disease survivors report [20] and the Ebola National Survivors programs within each country. Further, the platform in which the Ebola data collection and reporting system was built on was adapted through end-user feedback, testing, and technology upgrades to support the 2016 Yellow Fever outbreak in Democratic Republic of Congo and Angola.