By Abhinav Shashank, CEO and co-founder, Innovaccer.
Once while I was scrolling through the news feed on my phone, there was one specific line that really made me wonder: “There’s a 40 percent chance of gusty and blustery winds today.” Statements such as this one strongly influence people’s behavior, as they are based on evidence or data findings from years of surveying, studying, and analyzing past trends and occurrences. However, my question is “Why are we not able to make such claims in healthcare- even today?”
Can we predict the vulnerabilities a patient might face in the future or the current health risks a population segment faces?
Is risk scoring the answer we have been looking for?
Almost all kinds of care organizations have some risk scoring methodology to target care interventions. With quality, costs, and patient experience taking the center stage in healthcare, care organizations need to stratify patients based on their need for immediate intervention.
The need of the hour is to address high-risk issues that impact large groups of patients and ensure that these needs are met in a timely fashion. Often, frequent fliers among high-risk patients come into the emergency department as if it’s their second home.
What if we take the method of risk scoring to a whole new level?
Traditionally, providers and health systems have relied on claims-based risk models, such as CMS-HCC, ACG and DxCG, which were built to forecast the risk of populations/sub-populations but not for individual patients. Hence, these models give an accurate prediction of the average risk of the population but exhibit very poor accuracy if used to predict risk for individual patients.
Although risk scoring has turned out to be a key factor in addressing the needs of the patient population, this method cannot provide all the important insights that are needed to drive necessary interventions. Since healthcare already has the right data from sources such as EHRs, claims, labs, pharmacy, social determinants of health (SDoH) and others, can we predict the future cost of care instead of just stating the risk score of the patient?
The right machine learning-driven approach to predict the future cost of care for patients
It all starts with the right data. The first step is to integrate the data from multiple sources- whether it is clinical or non-clinical data, such as SDoH. The data from these sources can allow us to use the comprehensive patient’s data for multiple predictive models to predict future health cost with greater accuracy.
Healthcare technology is advancing quickly and this is precisely why executives need to be aware of all new technologies that can make their healthcare organisation more efficient and more impactful. This may seem difficult – staying on top of things and implementing new technologies always is, but it brings immense benefits and great results. While many technology advancements come with all that fame that is often not necessary, it can make patient satisfaction better. It can also improve cost savings and this is really important for the future of your organisation.
So, in this spirit, here are some of the most amazing tech advancements that can help your healthcare organisation become better and take another step towards the future.
Blockchain can make interoperability ai reality. You can solve many problems between healthcare organisations and it’s a solution that healthcare industry has been looking for for many years. It can decentralize the record systems and have multiple locations that can be shared with more stakeholders. This will help the healthcare system immensely and it can operate within different stakeholders in the healthcare systems. Instead of having a single client database, you can include both clinical and financial data on one server and in an independent, transparent database.
“Blockchain technology can share data in a safe system and put the clients and their needs at the center of the attention. Still, healthcare industry is a decade away from implementing blockchain in a meaningful way,”says Ingrid Fulton, a tech editor at Draft beyond and ResearchPapersUK.
Artificial intelligence can help with better oncology. Veterans Affairs is helping with this as a part of their precision oncology program which supports patients that have stage 4 cancer and that have tried all other methods of getting better. They are using AI to help use cancer data in the treatment of these patients. They are also veteran.
They treat more than 3.5 percent of patients in the US and this is the largest group of patients with cancer within any healthcare groups. This includes veterans from rural areas where it has been hard for them to implement better technology, especially something of this value.
Technology is the new creed that has literally touched almost every aspect of our life. Be it communication, traveling, or exercising, we are always interacting with technology. However, healthcare has always been considered a very conservative area in terms of technology deployment. This is because, in its very nature, healthcare mainly deals with human life which calls for utmost precaution. But the emergence of machine learning and artificial intelligence has sparked innovation and a myriad of solutions that are already working in the healthcare industry.
At the forefront of this growth are Android-powered smartphone devices. It’s estimated that 88 percent of all the devices sold in the last quarter of 2018 were all powered by Android. It shouldn’t then come as a surprise that companies are looking to hire Android developers to build health-care related apps.
But what does the future hold for tech solutions in the health industry? In this article, we are going to look at the trends in healthcare to look out for in 2019 and a few examples of apps for healthcare.
Artificial intelligence and machine learning are getting increasingly sophisticated to the extent of surpassing human capability and the potential for these two technologies in the healthcare ecosystem are huge.
One of the biggest potential benefits of AI in 2019 is helping people to stay healthy without consulting a doctor, or at least do it less often. Coupled with the Internet of Medical Things (IoT), AI is already being used to develop consumer health apps that proactively show patients how to stay healthy.
Moreover, AI is increasingly being used by healthcare professionals to gain deep insights and better understand of routine patterns occurring in patients. With these deeper insights, the caregivers are able to give better diagnosis, guidance, and support to the patients. For instance, the American Cancer Society is already using AI to detect cancer at the initial stages with 99 percent accuracy.
Product development is another area that AI and machine learning are being used. R&D in the medical field can be painstakingly slow and costly given that hundreds of variables need to interact with each other. Today, medical researchers are using AI to safely explore biological and chemical interactions of drugs using the discovery process and clinical data.
Another area you can get artificial intelligence in healthcare is through workflow optimization. It helps automate repetitive tasks such as routine paperwork, patient scheduling, and time-folio entry.
Wearables and Augmented Reality
I do think that a significant portion of the population of developed countries, and eventually all countries, will have AR experiences every day, almost like eating three meals a day. It will become that much a part of you.” — Tim Cook at the 2016 Utah Tech Tour – source.
Virtual wearables and augmented reality devices are other emerging healthcare trends proposing to make significant advances in the healthcare space in terms of diagnosis and medical education.
On one side of the scale, virtual reality superimposes a patient in an artificially created surrounding, whereas, augmented reality helps generate layered images to real like objects. As a result, these technologies are and will continue being used by emergency response services providers to relay critical first aid information before the first responders arrive at the hospital.
In the prevention and diagnostics front, VR/AR has allowed medical care providers to create and manipulate different camera colors to reflect or replicate pre-existing effects in their databases.
But perhaps, the biggest impact of VR can be seen in 3D reconstructions of human organs. This has proven important especially when surgeons need to re-create the exact size and positioning of human organs before conducting complicated surgeries. Having the same exact replica of human organs give surgeons the know-how on how to deal with particular organs no matter how small they are.
In terms of medical education, both VW and AR have been great tools in transforming the way students learn. Surgeons are able to rehearse surgery procedures using dummies quicker and without having to use actual human bodies.
The internet age has brought along profound changes in the telemedicine landscape. In the earlier years, telemedicine was strictly limited to doctor and nurse consultation. However, the proliferation of smart mobile devices that are capable of transmitting high-quality videos has opened up avenues for virtual healthcare services from specialists to patients straight in their homes. This is especially paramount in remote areas where doctors can’t easily reach.
Artificial intelligence has the potential to revolutionize all fields, and healthcare isn’t exempted.
This technology, which involves machine and deep learning, enables computers to gain the capacity to better understand and process complex forms of data. Essentially, they would have the ability to learn through examples.
When implemented correctly, it’s a development that comes with many possibilities, especially in a data-driven field like healthcare. Machine learning has the potential to improve patient care, provide faster service and diagnoses, and generally provide a better experience for both healthcare providers and patients.
Anyone involved in healthcare (which basically means everyone) can stand to gain from learning more about how AI might affect the industry.
Guest post by Dan Hickman, chief technology officer, ProModel.
With six in 10 U.S. hospitals functioning at operational capacity, patient flow optimization provides one of the most cost-effective ways to increase a hospital’s bottom line.
Around 6 a.m. every day, hospital-wide “huddles” occur to discuss and determine a collective understanding of the state of operations. Most of these huddles take less than an hour and provide hospital and departmental leaders a snapshot of census status and expected discharges.
But hospitals are complex, dynamic systems. By 7 a.m. a flood of patients could hit the ED, affecting everything from staffing to the census, and carefully crafted plans disintegrate.
Consider the current state of patient flow at most hospitals.
Most health systems today have a reactionary approach to admit, transfer and discharge (ADT), patient flow, census, and staffing. Moreover, there is no way of accurately predicting future patient flows to right-size staffing and optimize workflows.
Discharge processes are open loop, resulting in costly delays. Most hospital staff use spreadsheets stating the number of discharges planned for the next 48 hours. However, there is no way to look at patient census with diagnosis codes tied to the typical length of stay.
The current state of patient flow results in multiple problems:
For many hospitals, the length of stay and cost per case metrics exceed CMS value-based care efficiency measures affecting reimbursements and the bottom line.
The daily reality of hospital staff revolves around logistics — the timely and accurate flow of patients coupled with staff, equipment, and facilities needed to accommodate and provide care within the hospital. Yet most hospitals lack the tools to define, visualize, predict and optimize the logistical flow of real-time needs into the near-term future.
Compounding the problem, many patients cite time spent ‘waiting’ as an issue affecting their experience, and ultimately patient satisfaction and the hospital’s HCAHPS scores.
Hospitals are really good at examining what’s happened to a patient in the past. The staff knows where they’ve been, but they haven’t taken the next leap, which other industries have, at projecting out where they think patients will “flow” during their stay and how the next 24 to 48 hours could affect the status and the census. There are parallels with other highly complex industries where accuracy and logistical management are critical to safety and success. One example — air traffic control.