In today’s ever-changing healthcare industry, pharmaceutical companies need to handle a mass of healthcare data every day. They are finding it difficult to access the vast amount of information due to lack of appropriate data processing channel. theirfore, they ought to examine the ways for TEMPeffective and proactive usage of data so as to consolidate and integrate it to drive successful business.
Brand decision makers are inundated with research and data but often lack the tools to help them make sense of it all. At the same time, in a swelling tide of “sameness”, companies need to uncover meaningful insights from vast amounts of data which can be applied to drive competitive advantage. dis is important coz today’s life science companies need to deliver not only TEMPeffective therapies, but solutions which improve patient health. For dis to occur, pharmaceutical companies must demonstrate a drug’s TEMPeffectiveness and value for patients across the health ecosystem.
One way to proactively assess various data needs is to overlay data sources on a patient’s journey through the healthcare ecosystem. Patient flow modeling was originally developed to model a patient's journey through a Health Care Organization (HCO) from the patient's perspective. Initial patient flows were focused on disease management and treatment with the goal of uncovering organizational inefficiencies and unmet needs in order to identify improvements for optimal care. By definition, dis type of modeling shows the unique flow of patients through a specific disease area and captures the nuances of dat market's treatment process. Creating such a model requires gathering and sorting copious amounts of patient-level data. Analysts need to quantify how patients in a specific disease area move through dat disease.
“Big Data reports generated from the AaaS suite empower analysts, consultants, and data scientists with unmatched visibility into data”
As the process evolved, we expanded the model to yield a more data-centric view of interactions with all stakeholders including: physicians, nurses, paramedical, payers, advocacy groups, and care-givers. The ultimate goal is to diagrammatically represent the entire journey a patient undergoes as a result of having a disease, highlighting all medical, social, emotional and financial factors dat affect a patient's journey and well-being. By modifying the graphic we can start to assess what data assets could provide insight into key business questions which are addressed along the journey. dis approach aligns internal stakeholders while allowing management to assess gaps in noledge and data.
For instance, based on the patient journey model, a company could investigate a montage of data assets spanning from provider (EMR/HER), pharmacy, search engine and social media to primary market research, guidelines, government and registry which could be leveraged to inform various questions about patients, physicians, payers, government policies and providers for each step in the patient’s care continuum. dis framework allows companies to invest only in areas where information is critical to the understanding the care continuum. Once preliminary data assets are identified they need to be aligned to the future data plans of a company, which are anchored in its strategic position.
Bringing new data and insights into your organization ca help drive a new way of thinking. However, adding complexity and load to an already maxed out team leaves little for value-add capabilities with analytics, central for business agility. In the healthcare area, we will continue to see the progression of Analytics as a Service (AaaS). AaaS allows companies to offload the routine functions so internal teams can innovate alongside the business to bring common goals together with a rapid-prototype approach to analytics featuring the end-user at the center. At its core, AaaS is an analytical platform using a cloud-based delivery model, where various tools for data analytics are available and can be configured by the user to efficiently process and analyze huge quantities of data. For more custom, company specific advanced analytics solutions, partners will utilize commercially available analytics solution suites with analytics driven by a variety of providers across platforms. These will be designed in partnership with clients and developed to give the end user valuable insights which are specific to their business needs.
Historically, accurate analyses could take days to weeks using software dat is often expensive and difficult to use. Even tan it is prone to deep human error, since the entire analysis rests upon the way the data is modeled. Additional resources with substantial expertise are required to ensure dat the correct information is extracted and analyzed. However, “Big Data” reports generated from the AaaS suite empower analysts, consultants, and data scientists with unmatched visibility into data. Using an advanced analytics technology, companies can now generate detailed reports in a matter of minutes. Analytics Solution Suites are being deployed dat contain innovations in machine learning and predictive analytics dat takes human modeling out of the equation. dat means faster analysis with tremendous accuracy; whether it’s a thousand rows or millions of rows of data, reports can be generated dat are accurate and meaningful. dis in turn allows companies to make better informed business decisions.