By: Malcolm Quigley
Within the field of strategic partnerships, we come across multiple different ways in which we can deliver on our goals. Sometimes we can see a clear path forward, and other times the path is a lot more complex. It’s a bit like how we navigate city streets. In some cities, we can follow a numbered and alphabetized system, but streets in other cities are name-based. Indeed, some cities use both systems. On top of that, there are multiple ways to travel through the cities that are also based on the naming conventions. It can be very confusing and seem unstructured for anyone who is fully accustomed to one system or the other.
It is certainly the case in strategic partnerships that we sometimes have a straightforward route to the end goal, but in others, the journey toward the goal is more important due to the complexity of the goal.
Data And Public Health
A good example of this conundrum is in global public health, where data is the new gold. In this instance, the health sector is similar to a city that uses multiple different street naming conventions. There are many different data gathering systems that sometimes collect the same data, but slightly differently, while other systems receive ancillary data that seems unrelated but that when paired with direct data, can provide a true picture. An example would be migratory and weather patterns at certain times of the year as they relate to an increase in malaria cases being reported. Seeing a holistic picture helps anticipate and prepare health systems to respond.
For organizations working in global health, it becomes quite complex. Often such organizations work across the spectrum, from information campaigns to change behavior, and generate demand for health services and products through to delivery within a network of health providers and facilities. Data comes from consumers, who are multifaceted and have many different drivers and needs, to health providers of all sizes from single service to full-service. Data ranges from consumer needs, perceptions and behaviors to direct patient-centered information about health issues. As a result, there is an incredible and sometimes overwhelming amount of data that is often unstructured and decentralized. In fact, for many organizations, I’ve observed that unstructured data accounts for 80% to 90% of all internal and externally sourced data.
New Solutions
Thankfully, new technologies are emerging that can make sense of unstructured data from multiple different sources. Many health organizations want the optimization of human-computer interaction with unstructured data to enable a frontier for navigation and sensemaking—not just machine-driven analysis that leads to additional data, thus exacerbating the user’s problem.
I think the future of analytics and decision-making for more consumer-centric health care will look very different as more machine learning techniques are applied to unstructured data sets. While traditional analytics often rely on theories, ideas or at least search terms, the power of patterns is embracing a more unstructured approach. Large datasets are produced while consumers engage across digital healthcare and in traditional brick-and-mortar settings. Data from these sources can be triangulated with external data and searched for patterns the human brain would not be able to easily spot. Finding links between medical service needs is already used by the insurance sector to identify risky behavior or even clients. In my view, the consumer should benefit directly from such advanced analytics to improve consumer healthcare services.
Considerations For Successful Partnerships
Realizing the full potential of unstructured data requires collaboration and coordination between all institutions involved in the domain of public health. The first step in this direction is to acknowledge that each type of data is important and that placing human experience at the center of socio-technical system development can improve data-driven decision-making that ultimately leads to healthier lives by seeing the bigger picture.
We then need to face the real challenge of whom to partner with, as we may have to engage multiple partners that are both familiar and unfamiliar as well as partners whose technologies are not yet fully proven. As mentioned earlier, this field is exploding with lots of new organizations that fulfill one or two aspects of need. In this instance, I’ve found personal relationships and trust are key because we are all charting a way through complex data toward our end goal. Often things don’t go to plan, and the plans made will be rewritten several times with delays in deployment.
It will also be important to map out complementarities between partners based on gaps not fulfilled. Many technology partners will likely overlap in their capabilities somewhat but not fully, and the gap is what is critical. In addition, some partners’ geographical reach may be different but offer the same capabilities, and in this instance, it’s important to have clear demarcation to avoid conflicts.
Finally, a close working relationship between teams working in digital health, technology, programs and partnerships is critical when there is no clear route to the end destination.
Source: Forbes
