How resource allocation in healthcare used to be and how it will be
Healthcare. Resource allocation is the distribution of resources – usually financial – among competing groups of people or programs. When we talk about allocation of funds for healthcare, we need to consider three distinct levels of decision-making.
How it used to be
- Seek health services when feeling ill
- Consumer sorts through different care options
- Data is then captured to confirm the diagnosis
How it will be
- HealthData is captured via medical-grade wearables
- Care option reaches out if there is an anomaly
- Provider already has the historical dataset of relevant biomarkers and genetic predisposition
We are still in the early days, but adoption is accelerating fast. Remember, in 1999, the first car manufacturers added voice interfaces to enhance the product experience. In 2011 first voice assistants were built for the smartphone experience. And in 2014, the first device was created to give a dedicated home to a Voice Assistant. What used to be an access channel turned out to be a form factor – and a milestone to ubiquitous computing.
According to voicebot.ai research, 166 Mio use voice search on their smartphone, wherefrom 48,3 Mio use voice search daily. 129,7 Mio use voice search in the car, wherefrom 29,7 Mio use voice search daily. 87,7 Mio use voice search on a smart speaker, wherefrom 43,7 Mio use voice search daily.
Voice Communication is not linear
Why Wearables and Data Management Platforms are so important
Sensors will be crucial for service authorization. Either as two-factor authentification for users and secure administration rights. And sensors will trigger automated services. Sensors will be on wearables will be placed at any touchpoint where consumers interface with the health system. E.g. data from wearables can teach an AI to spot signs of diabetes. Finally, we can adopt the principles from Predictive Yield Management Tool like Rapt / Atlas Solution / Microsoft Advertising.
Improve impact by:
- Improving control and providing visibility into impact dynamics
- Providing ability to monitor and enforce performance
- Incorporating data-driven analytics functionality into planning
- Improving inventory utilization
Activate yield analytics to:
- Analyze pricing and impact by initiative, product, and patient to inform decisions and drive impact
- Optimized initiatives based on market conditions
- Drive accountability for performance
- Predict and report efficiency, understand impact and profile trends, and model composition
Machine Learning (ML) and Natural Language Processing (NLP) are the next frontier in proactive healthcare
Voice Services are easy to use and rech users with limited technical understanding. Here is how two major health systems are using Alexa in healthcare today as well as their predictions for the future of voice assistants.
Maryam Gholami is the vice president of product at Providence St. Joseph Health, a hospital system right in Amazon’s backyard. Her team created an Alexa skill that lets patients book appointments at nearby clinics. It’s an early effort in what Gholami sees as a long-term need for the hospital.
“We see voice having massive impacts on healthcare in the future, both for consumers and caregivers,” she said. “Consumers are buying all these voice devices, whether it’s Alexa or a Google device.
So it’s important for us to start early, learn about the consumer’s behavior, and perfect that experience. It doesn’t happen overnight.”
- Understand the Eco-System
- Your Go-to-market Strategy
- Think ahead — way ahead
We are serious about data ethics. You should be too. We have watched with concern the rise of poorly considered ‘data personalisation’ and data shadowing of individuals. We believe that aggregating data around individual persons, without controls on how that might be used in the future, is a dark path to walk. That is why we have always ensured that the data we collect and hold is aggregated around the expressed individual needs, not the individual humans who expressed them. This ensures that the data can only be used to meet those needs, and never to manipulate those people who need them. No personally identifiable information is held within the set, nor can such be reverse engineered out through aggregation with other datasources.
Read: What is data ethics?
Make a plan
Create a kind of statement of the future in terms of what you’d like to achieve. Divide what lies ahead into long-, medium-, and short-term goals, and budget your time by drawing up a road map to provide a visual reminder that will keep you on course and chart your success.
- Start with an annual plan that accounts for long-range projects and establishes specific dates for completion.
- Mark them all on your calendar, then revisit them each month to see what you want to accomplish, monitor your progress, and determine the next steps for getting things done.
- Update your calendar from there. At the start of each week, build out your plan for the next seven days. List out all that you want to accomplish, referring back to your annual and monthly goals. Break these weekly goals into daily objectives to compile a “to-do” list for the week.
- Each day, refer back to your weekly goals and build out your day by starting with high-priority tasks and moving on from there.