International Data Corp (ICD) predicts that 30% of providers will use cognitive analytics with patient data by 2018. Tweets from HIMSS’ Big Data and Healthcare Analytics Forum this week point to the challenges that technology is presenting to hospital care delivery, but also express hope in the power of machine learning and artificial intelligence to identify trends and improve care. Machine learning was defined at the conference by Bob Rogers of Intel as “Systems that learn iteratively from data, identify patterns, [and] predict future results with minimal human intervention.”
Investors also see the opportunity to improve healthcare through artificial intelligence. CB Insights’ quarterly report found 55 rounds of funding in the first three quarters of 2016 for artificial intelligence for healthcare companies. Deals range in size from a few hundred thousand to $145M Series A round raised by iCarbonX based in China.
Turnover rates for private pay home care agencies average around 60%, according Wyatt Matas’ report Private Duty Home Care: Untapped Value. Business owners often don’t see the toll that a two-sided business model can take on their potential to scale. Without equal attention to recruiting and retaining staff as well as customers, growth is limited.
In an article that we can really relate to, Tim Mullany of Home Care News compiled 6 data points to reduce turnover and improve the efficiency of the home care aid hiring process. For example, to keep potential aides engaged (and reduce the workload for hiring staff), limit the application to one that can be completed in 30 minutes.
Another great idea to reduce employee turnover is to empower the top 10% of your workforce to mentor new hires. Matching newbies with the experienced, dedicated staff conveys an increased expectation for performance. It also rewards the top performing aides with training, financial incentives, and a leadership role.