From sickcare to healthcare
We are a lucky generation as we have the opportunity to witness in our lifetime medical breakthroughs and pioneering research significantly changing the face of medicine which undergoes an exponential renaissance to put the patient in the center & to move from disease management (sickcare) to protecting health (healthcare).
Healthcare philosophy has been shaped in the past 2 centuries to address the needs of “sick population” instead of maintaining the wellness of the healthy population, which resulted in an increasing health expenditure in almost all countries in terms of % of GDP as the world population & life expectancy increase constantly. We still consult our doctors when we get unwell based on the visible, episodic symptoms instead of intelligibly and continuously monitoring the early, not-so-invisible signs before it becomes more serious or preventing the disease/its symptoms altogether. “Healthcare” requires a different world, where vital data can be streamed to the integrated care teams — a network of connected experts- who will know the exact moment and strategy to step in, in order to provide support, recommendations on life style changes or treatment (and funding of the services/treatment for that matter).
The question is: How are we going to create that ecosystem, which innovative technologies will support the reform and how is all this going to work? Here is an attempt to summarize current trends and reforms in medicine that will help enable better well-care management.
Integrated Care Teams (or Trans-disciplinary medicine)
According to the 2018 Global Health Care Outlook report published by Deloitte, a smart healthcare is when appropriate health care is delivered at the right time and place (even to places and people that don’t have it), where correct individuals communicate and use centrally located, protected, easily accessible patient data effectively and efficiently across the ecosystem, use technology to imperatively and accurately diagnose high/moderate/low risk individuals, treat, and deliver cost-effective care while patients themselves are informed and active in their care.
Currently none of the requirements mentioned above is optimal.Healthcare practitioners do not work collectively to offer continuous and comprehensive services across the healthcare continuum to provide preventive, predictive, acute, chronic and end-of-life care.
According to Medteryx, without new models of integrated care, escalating hospital admissions and per-capita costs will become unsustainable driven by increasing rates of obesity, chronic disease, an aging population, and rapidly increasing rates of mental illness.
NHS is moving towards integrated care system which can better understand data about local people’s health, allowing them to provide care that is tailored to individual needs.
Communication between patients, healthcare providers, hospitals, acute care facilities and caregivers must be well established and easily accessible. The siloed healthcare prevents information sharing and results in waste of resources, time and sometimes loss of human lives. Using digital support in health care is critical in breaking down the barriers. Patient “big data” should be collected and shared continuously among the “integrated care team”, who is not necessarily located in the same place, by providing their timely expertise once the big data analytics -generated by continuous monitoring of signals- is efficiently analyzed and put in perspective for meaningful conclusions and treatment strategies.
Big data analytics
According to IBM, big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes. Big data requires and provides flexible and easily expandable data storage and management solutions. Healthcare data today come in many formats such as electronic healthcare records, diagnostic tests, medical imaging etc., hence we need a harmonized, identically formatted platform combining all data points from currently used and underused/unused sources. Moreover, current hospital IT infrastructures are not able to process and analyze massive amounts of constantly refreshed, differently formatted data in real time. Big data needs an agile, flexible and quick analysis with quality assurance of the data for a reliable interpretation, which then can become meaningful for decision making.
According to McKinsey, the healthcare industry has lagged behind other industries in the use of big data. A smart society is a healthy society — and a healthy society makes good use of its big data, adds a recent report by the Center for Data Innovation (CDI), a think tank based in Washington, DC. Despite the slow adoption, level of creativity and willingness to integrate it in medicine are high, and many themes emerged from the opportunities for big data in healthcare. In this article, I would like to focus on two big ticket items:
- Improved quality and efficiency of Personalized Care by predicting the outcomes, improving treatment adherence, reducing waste of information, analyzing real time resource utilization to improve productivity. Predictive analytics, for example, can promote preventative care for heart disease and obesity. Big data is now the main pillar of evidence-based medicine, and doctors can correlate symptoms to narrow the diagnoses.
One predictive analytics tool developed at the Mayo Clinic cut the time between initial symptoms and treatment by half. It also reduced manual screening hours by 72%, allowing nurses to focus on other care duties without putting patients at risk.
Other predictive tools can prevent falls among home patients, predict asthma crises, and improve medication adherence rates for diabetics.
In 2016, Montefiore Health System launched a pilot aimed at identifying any patient at a high risk of death or intubation within 48 hours of hospitalization.
Simulations in Neonatal Intensive are Unit of Duke University Hospital challenge long-held assumptions about how to optimize patient outcomes and manage costs.
Oncology is headed in the same direction, with clinical decision support platforms like CancerLinQ that deliver personalized recommendations to providers based on their patients’ data.
Precision Medicine Initiative relies on massive collections of big data to determine the genetic roots of cancer, diabetes, autism, and other conditions.
A recent Forbes article gives an overview on how data scientists at Blue Cross Blue Shield and big data experts at Fuzzy Logix tackle the opioid crisis. Using years of insurance and pharmacy data, Fuzzy Logix analysts have been able to identify 742 risk factors that predict with a high degree of accuracy whether someone is at risk for abusing opioids.
2. Population Health management: By mapping the right data sets, it is possible to predict disease that will escalate in specific areas, making it easier to plan for treatment strategies, stocking serums and vaccines.
The National Institutes of Health as well as the Centers for Disease Control and Prevention (CDC), for example, have been using big data to predict disease pandemics, such as a defense against Ebola.
Another example is that of Asthmapolis, which has started to use inhalers with GPS-enabled trackers in order to identify asthma trends both on an individual level and looking at larger populations.
Another Forbes article details how four hospitals which are part of the Assistance Publique-Hôpitaux de Paris are trialing Big Data and machine learning systems designed to forecast admission rates with daily and hourly predictions of how many patients are expected to be at each hospital — leading to more efficient deployment of resources and better patient outcomes.
Investing in analytics infrastructure, including electronic health records (EHRs) and the Internet of Things (IoT), can help address the needs of big data. While big data is necessary it is not sufficient, as simply accumulating a large set of data is of no value if the data cannot be analyzed in a way that generates future insights that we can act upon. Raw data alone cannot lead to systematic improvement, it has to be turned into meaningful information. Institutional leadership and culture have to support improvement efforts, and clinicians and healthcare staff need the skills to analyze and apply data as stated by the National Quality Forum in a white paper.
The concept of maintaining our well-being rather than reacting to the diseases makes a lot of sense and will be normal practice within 10–15 years. The difficulty lies in changing the mindset and reforming a “healthcare” system hard-wired for siloed/fragmented care, incapable of using and making sense of big data, a system which creates unnecessary duplication and waste of resources and time, slow to adopt an innovation in a large scale such as big data. Moreover, we still need to work ardently to bring wide and equal access to innovative therapies, to ensure sustainability of healthcare which can guarantee both innovation and universal healthcare coverage, to create a world in which everyone has an equal and high chance to have a healthy and long life.
Ezgi Tasdemir #exploringfuture
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Internet of Things (IoT) or better Internet of Everything in Healthcare
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Ezgi Tasdemir, PhD is a Novartis Oncology employee. This article is created by Ezgi Tasdemir. All the views, analysis, and perspectives are fully independent and belong to the author only, they do not represent the views or opinions of Novartis or any other company or organization. The author does not receive any funding or support from Novartis or any other pharmaceutical/non-pharmaceutical company for this blog.
