This MedTech Startup Idea from YoStartups is to use Big Data Analytics to Predict Hospital Admissions. This model will work by using legacy data from the hospital’s patient management systems, in combination with external data-sets such as weather, public holidays and flu patterns. This will not only reduce the stress on the hospital staff, but it can also improve the overall revenue per bed in the hospital by optimizing the resources and processes for quick patient handling.
Market Definition | Big Data Analytics to Predict Hospital Admissions
There are various studies done to prove that the overcrowding of the emergency department in the hospitals can lead to adverse patient outcomes and even mortality in some cases. The examples of few studies are mentioned below;
A study by Department of Emergency Medicine, Stony Brook University, NY, USA (2011), claims that mortality rate increases with increase in hospital on-boarding time. It increased from 2.5% in patients who took less than 2 hours in hospital on-boarding to 4.5% in patients who took 12 hours or more. The studies also revealed that the length of the stay at the hospital was related to time to onboard in hospital. There are similar studies done at Emergency Department, Australian National University Medical School, Canberra Hospital Yamba Drive, Garran, ACT 2605, Australia.
All of these studies indicate a similar pattern that General Emergency Department overcrowding often impacts patient care adversely and leads to increase in patient morbidity.
This data presents a substantial opportunity for startups who can use AI and Big data analytics to predict patient admissions and use this data to help hospitals manage their emergency departments better.
Competitor Analysis | Big Data Analytics to Predict Hospital Admissions
Four of the hospitals which form the Assistance Publique-Hôpitaux de Paris (AP-HP) are using data from internal and external sources – including ten years’ worth of hospital admissions records to come up with day and hour-level predictions of the number of patients expected through the doors.
They are trialing Big Data and Machine Learning systems designed to forecast admission rates – leading to more efficient deployment of resources and better patient outcomes. The core of the analytics work involves using time series analysis techniques – looking for ways in which patterns in the data can be used to predict the admission rates at different times.
The experimental project was carried out at four of AP-HP’s facilities, with plans to eventually roll it out to all 44 facilities if it proves successful.
Pain Point & Target Audience | Big Data Analytics to Predict Hospital Admissions
The target audience for this startups would be hospitals and emergency departments who can use their legacy data to predict patient admission rates and improve overall resource management and patient care.
As per a study conducted by Department of Emergency Medicine, Boston Medical Center and Boston University School of Medicine, Boston, MA, USA, 91% of hospital ED directors report overcrowding as a problem. The hospitals are often left grappling for resources, staff, and workforce as they cannot anticipate any emergencies, and this often leads to increased stress for the team and higher boarding times for patients and adversely affects the patient care.
Value Proposition | Big Data Analytics to Predict Hospital Admissions
The value proposition for this startup idea is that it can make the hospital admissions predictable, which means that the hospital can optimize all the resource involved in the day-to-day running of the hospital like workforce, machinery, additional infrastructure, etc. When all these resources get optimized, it will directly translate into savings, quicker patient turnaround times and more revenue per bed. The hospital can also plan the staff schedules, purchases, and human resources contingency efficiently, reducing the overall stress on its employees and the system.
Business Model | Big Data Analytics to Predict Hospital Admissions
The startup will have to design an Artificial Intelligence-based platform which can analyze all the clinical inputs from the hospitals to predict hospital admission rates.
The startup can adopt two level of revenue model. Firstly it can provide its proprietary Patient Management System for which it can charge a monthly subscription fee. This service could become popular among upcoming hospitals.
Secondly, the startup can provide its’ AI-enabled application to predict patient admission rates and integrate with the existing Patient Management Systems. The startup can charge one time flat onboarding fees and a monthly subscription for hosting the hospital data on the cloud.
Way to Market | Big Data Analytics to Predict Hospital Admissions
The startup will have to develop a basic algorithm based on the principles of AI and Big Data, this algorithm when integrated into patient management systems can start learning under the supervision of a data scientist, who can further fine tune the algorithm to improve its accuracy based on the predictions it makes. Once the algorithm reaches the desired accuracy using the legacy data, the startup can develop API’s to integrate with the existing patient management systems, or it can be designed as an end to end patient management platform powered by AI.
Key Milestones | Big Data Analytics to Predict Hospital Admissions
The startup will have to develop an AI-powered software platform which can make predictions from the data uploaded to it. This platform will have to be developed within the first quarter after the inception of the startup.
The startup will have to then tie-up with one of the hospitals to get access to the available data to train the AI-based algorithm till it reaches the desired accuracy of making predictions on hospital admissions. This phase can take longer and should not be hurried, as this is the most crucial step which can make or break the startup. It would be desirable to complete this phase within the first year of inception. Once the algorithm is perfected, the startup can develop API’s to integrate with all the popular patient management systems and launch the product. The startup will have to ensure it complies with all the privacy and data protection laws governing the patient data.
Investment needed for Prototyping this MedTech Startup Idea
The startup should look to raise upto $125k from Eonify, Merantix which accelerate AI based Healthcare Startups or from Y Combinator or it can join the equity-free accelerator program from Kapsch Factory1. The startup can use open source Trusted Analytics Platform (TAP) to build its platform.
Startups should always use all the funds raised towards developing the product only; no operational expenses should be allocated from the seed funding.
Team Capability | Big Data Analytics to Predict Hospital Admissions
The proposed founding team for the startup would be two technical founders, one full stack developer who can design, develop and test the AI-powered platform, assisted by a data scientist who can design, model, train and optimize the code for the AI algorithm based on the principles of statistics and machine learning. It is also desirable to have someone from the healthcare industry, as he can expedite the onboarding of clients from the hospital and other medical centers.
Investors/Expert Take for this MedTech Startup Idea
An October report published by TM Capital shows that healthcare AI VC deal volume and funding hit a five year high in 2016, with $794 million in investments across 90 deals.p Ideas – Big Data Analytics to Predict Hospital Admissions
The cost of providing healthcare is often more than the rate of increase in GDP in all developed countries. The governments are increasing their budgets for healthcare, but most of it is usually wasted as the systems are not yet efficient to cater to the ever-growing populations. If AI can predict the demand for healthcare services accurately, length of stays at the hospitals can be cut; resources can be better managed with healthy and longer lives for everyone.
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