5 starting points on including Big Data and analytics to help your healthcare business stay in the game
With discussions around the overwhelming role that big data and analytics are playing and set to play in the future in the success of a business, a well-planned data driven BI strategy should be core to every business. We list five areas which can be the starting points for a business manager in healthcare industry, to orient their business towards harnessing this asset.
The industry has reached a point where it becomes imminent for stakeholders to move beyond just accepting that patient data is the new currency and move forward to defining what data means for them, how to meaningfully employ it and what can big data and analytics mean in specific for a healthcare related company? The question also arises, how to develop and implement an effective strategy to utilize data. Hereunder, we enlist five areas which could act as the steppingstones to realizing a larger company ecosystem centered around data.
HOW TO INVOLVE BIG DATA AND ANALYTICS
The companies today don’t suffer from understanding the importance of big data and analytics but the lack of a clear approach in implementing their vision. One can begin by clearly defining a business intelligence (data) strategy. As a start, some key components to a BI strategy could be:
Inducing a data proactive and data driven culture – Being data proactive comprises not just building tools and capabilities but also putting data first behind all key strategic initiatives. This can be achieved by accounting for the full lifecycle of data, from generation to information access to responsibilities for undertaking meaningful actions. Almost all businesses and professions are data-driven, then why not treat it like an asset rather than a by-product. Quoting a famous VC in the space, “If I wanted to be a doctor today, I would go to a Maths school.’’
Increasing collaboration and integration within the company – This is a problem often seen at big pharmaceutical corporations, where they have evolved inorganically over decades and with each division responsible for their own strategies, product portfolio and data management systems, leaving behind a legacy of inefficiency and non-collaboration. Additionally, we have seen Business Intelligence to be developed in a piece-meal fashion, focusing largely on smaller factors. The focus needs to shift towards forming a common Big picture. Teams and systems need to be integrated across functionalities, pooling into the same central system, producing common and measurable goals and objectives. Also important is, narrowing down the data-literacy gap within the workforce across functionalities.
Using predictive and prescriptive analytics (AI and Machine Learning) to not just monitor situations but also receive optimal real-time or near-time recommendations to tackle them – The signals are there that the technology is now ripe to not only provide insights but also provide recommendations on meaningful follow-up actions. We see several examples where companies anticipate changes in markets even before they occur and adjusts their product output, or a patient who is otherwise more likely to not remain enrolled in a therapy program, to be engaged in a way that they keep continuing with the program. Some more such examples are listed later.
Investing in network and system enhancements (outside the company) – Often the common thought is limited in the ways in which data can be utilized. The approach needs to be more holistic, involving all the key stakeholders in the value chain. For example, a company can also use its vendor´s data for increasing its own sales and client satisfaction. Companies should start thinking in terms of a systems landscape consolidation.
Implementing effective processes for Data governance and security – Companies and countries tend to operate in an environment of extremes with respect to data governance, either it is too much or too little. A balanced approach is required because user data is one of the most sensitive issues in the healthcare data management. Companies should implement effective policies guide lining how the data is collected, stored, protected and used. Further required are initiatives to prevent inappropriate use and leaks. Last but not the least is a robust quality assurance module a must have within the framework.
COSTS OF NON-PARTICIPATION/ NON-ACTION
More and more digital health solutions are propping up and biting into the moats of traditional business models. Although, most are working in different niches, the trend of increased usage and value creation by digital solutions is here to stay. These companies are bringing in new approaches to help pivot business models to more value-based compensations. Payors are looking at variable and outcome-based methods for reimbursement. To succeed in such a ‘shared-risk’ environment, a company needs to not only assess its risks and opportunities, but also determine them in real-time.
Can a traditional healthcare company afford to choose to sit out and see how it all plays out before investing in the scheme of things themself? The answer to this is becoming a louder and louder No, given that, it is not only competing with small start-ups gnawing at its market share but also major technology companies, which all have announced bold projects within the healthcare space.
Big data and analytics can be employed in various areas such as care management, costs management, records management and clinical studies, depending on the type of company employing them and their end use. The applications are broad and numerous. Below presented are some of those use cases collected from recent announcements and news:
Risk scoring for chronic and other diseases, population health: Amazon is working with PHDA on projects which can help create an individualized risk score for every cancer patient, which will help providers better predict a patient’s response to treatment. Other projects will aim to use a patient’s verbal and visual cues to diagnose and treat mental health symptoms, and to reduce medical errors by mining all data in patient medical records.
Getting ahead of patient deterioration and preventing hospital readmissions – In recent years, the US health care system’s shift toward value-based reimbursement has given home health providers cause to test all sorts of approaches aimed at reducing hospital readmission rates or preventing unnecessary trips to the emergency department. Predictive Analytics driven Telehealth program lowered ED Visits by 20%. The predictive algorithm pulled data from medical and pharmacy claims, lab results, demographic data and other information. Telephone follow-up through the care coordination program revolved around needs assessment, care planning, appointment scheduling, medication assistance and transportation coordination, along with social worker and behavioral health specialist support when necessary.
Improving results of clinical studies trials – It takes between 10 and 15 years and costs between $1.5 and $2 billion to bring a new drug to market and about half of this time and capital is dedicated to clinical trials. This is where companies such as Trials.ai are trying to fill the gap. The firm uses artificial intelligence to analyze large sets of genomic data, past clinical studies, journal articles, and other forms of research to determine how organizations can best design their studies. Then, the product improves study execution by helping with protocol adherence, patient eligibility computations, patient visit management, site performance, retention statistics, adverse event reporting, and more.
Preventing suicide and patient self-harm: Facebook started using artificial intelligence to scan people’s accounts for danger signs of imminent self-harm. “In the very first month when we started it, we had about 100 imminent-response cases,” which resulted in Facebook contacting local emergency responders to check on someone. But that rate quickly increased. “To just give you a sense of how well the technology is working and rapidly improving, in the last year we’ve had 3,500 reports,” said a Facebook executive.
Bolstering patient engagement and satisfaction: Patient engagement, leveraged by Artificial Intelligence, is a viable solution that the healthcare industry needs. There are several examples where companies have driven user engagement using tactics such as:
Engaging patients with insights that are conversational and contextual, and adjusting based on the situation to respond in real time
Teaming providers with the intelligent guidance of AI so they can provide patients with next-best actions, personalized to them
Empowering patients who want to actively participate and engage in their health with intelligent guidance and support when needed
The opportunity exists for everybody from users to enablers to middlemen to medical practitioners. The potential market for digital or digital powered solutions runs in hundreds of billions of dollars and is still in a very nascent stage. Structuring and employing a sound data-driven Business Intelligence (BI) strategy could be a start to realizing this potential.
We at R2G are constantly monitoring these industry developments and helping our clients stay ahead of the competition. For further support and information, please feel free to contact us.