Reflection on the Learning Launch tool for Design Thinking

Challenge

Emergency Departments in general are usually very busy sections within hospitals and in South

Africa especially, both in the private sector and the public sector, these departments are plagued by

process flow disruptions and interruptions that can potentially impact clinical care. This has been

potentiated by the CoVID-19 pandemic. One of the main problems that create bottlenecks in patient

flow is data exchange between key health care workers in the patient care pathway. All systems of

care, especially in the private sector have information technology systems but unfortunately, these

systems do not communicate with each other.

My company is a healthcare technology start-up that is developing a mobile health (mHealth)

technology platform that is patient driven and which brings all of the systems together with the

patient-side application being the hub for all the other systems to communicate. We have

conceptualized a Minimum Viable Product (MVP) thus far and are taking this component to the

design and implementation stage but were wondering if this would indeed provide an increased

value for the patient in their pathway of care.

My team and I hypothesized that our platform would indeed create value, not only for the

patient, but for the other healthcare workers in the service delivery process. We held several key

assumptions that supported this hypothesis. The system would be scalable if it was effective and

efficient as an integrative technology tool to link data systems together as it had the following

capabilities: an ability to handle big data using secure and dependable cloud storage services,

algorithmic systems that utilized a narrow artificial intelligence approach to information processing,

and a connectivity that relied on the Internet of Things (IoT) to link all other systems together

through this central patient-driven platform.

The second assumption was that it was executable, being built on cross-platform mobile models

that had minimal reliance on high-ended technology, with most of the back-end data processing

handled by cloud based servers. The third assumption was that it was dependable, again because it

was base on low-code approaches to platform development, and reliant on proprietary cloud based

storage solutions that offered as close to 95.5% to 99.9% up-time, depending on the package of

services purchased by our company. Lastly, it assumed that these fundamental, yet rigorous and

comprehensive features, offered an extensive value offering for the main clients, who were the

patients.

We elected to test this hypothesis of client acceptance using a small experimental approach,

prioritizing execution and dependability as our key deliverables for the MVP.

Selection

We considered that the Learning Launch toolkit in the Design Thinking arsenal would be our

best approach to trialing our MVP platform with a test group of patients in a real Emergency

Department scenario. As the MVP was already past conceptualization, tools such as visualization

story-telling and mind-mapping would not offer us the best understanding of what our clients felt

about our product. We had already do intensive market research prior to conceptualization and had

obtained a vast amount of pre-development data about what the client (patient) felt were the

problems when they presented for emergency care. The central theme in all of this was that there

seemed to be long periods of time when they were left alone, unsure of what was going on.

Secondary analysis of these time periods through independent time-motion studies conducted by

our internal market research team showed that a large amount of time was spent either providing

information or retrieving information, which detracted from patient interaction, even if not for

direct clinical care and this was noticed by the client-patient who flagged this as a negative on their

entire patient experience.

Experimentation then would provide us with direct information on whether our platform would

streamline the data exchange process and if this would directly improve patient flow management

and indirectly improve the client-patient experience.

Application

We pre-loaded the platform onto several mobile devices and linked them all to the local wifi

network at a private emergency department. We then linked them all to our local server which

would emulate the cloud system that we would be using. We provided scribes for the clinician so

that his primary clinical records would not be neglected so that he could also use our platform to

record clinical encounters. We then also linked these devices to the databases of the local hospital.

We were unable to link these devices and the platforms to the radiology and laboratory systems, as

we were unable to get access from the various IT departments concerned.

Patients presenting to the Emergency Department would then have simulated pre-loading their

demographic and background information into the platform and the present for a consultation. The

clinician would record relevant information into the platform, the system then synching with our

local server to update this information.

Insight

We obtained much information about the acceptance of our platform as well as its utility to

improve data handling and information exchange. Our data flow mapping processes had been

confirmed by the experimentation conducted and we were optimistic about its further development

into a fully-fledged proof of concept (POC) version. The system initially met with some resistance

from patients who were anxious about personal data handling and confidentiality, but when we

briefed them while they were awaiting triage in the Emergency Department, we had a 100%

enrollment for the experiment. The clinician on duty in the single-doctor cover Emergency

Department was also keen to try the platform out, as he was fully aware of the delays that a paper-

based or unconnected computer based system caused.

We believe that the experimental approach that we had chosen was the best from a design

thinking methodology perspective but was obviously guided by the stage of the product

development that we were at already. We cannot believe that another tool would have been

beneficial for us so far down this product life cycle.

Approach

The experimental approach for us was ideal to understand the implementation, utility and

barriers to acceptance of our MVP prior to full product design and development. The other tools

available for a design thinker such as visualization and mind-mapping may have been extremely

beneficial had we know about these before-hand. We would have found them useful when we

applied them to our design and development process. However, despite this, we believe that we

have analyzed the what is, what if, what wows and what works methodology satisfactorily to

develop a product that we are extremely enthusiastic about.

