In the words of W. Edwards Deming, “Every system is perfectly designed to achieve the results it gets.” This is one of the most agreeable statements in the healthcare industry, and the most certain way to change health care is to change how the system works, improve quality and contain costs. To guide these improvements in healthcare space, we need to learn from the past, predict future, and quantify the concept of ‘good care.’
Data analytics is the current buzz word in the healthcare industry, offering valuable insights into managing patient care, reducing the cost of care services and achieving better health outcomes. Today, we are gradually grasping the novelty of data analytics and the art of leveraging it to deliver effective, quality care.
The Role of Good Data
In the past with traditional technology, finding gaps in care wasn’t as easy as it is today. Consequently, quantifying the quality of care being delivered became possible and we noticed a stride towards evidence-based care. Due to this, clinical data reporting has now become an important factor, more so than ever to improve the health of the population and make an impact on a population level, and data plays an important role to realize this. “Good data’ is accurate, reliable and consistent. It is all about planning carefully, setting goals and learning from the trend and has these roles:
According to a study, data analytics have the potential to save U.S. healthcare more than $300 billion, and two-third of this would be cutting down national healthcare expenditure by 8%!
How Can Data Create A Value Chain in Healthcare?
Almost every industry today is data-oriented. Data has found its way into every industry and business, regardless of their scale. As for healthcare, the volume of data is expected to grow dramatically in the coming years. Healthcare reimbursement models are transitioning to value-based care, and data will be a major cornerstone in clinical processes and Population Health Management (PHM). In 2011, the data related to U.S. healthcare was 150 exabytes, and soon it will reach yottabyte (1024GB).
Data can be used in the following six ways to create a value chain in healthcare:
1.) Systematically collecting and analyzing data
Healthcare data ranges from clinical data to financial data and contains data from almost every operational sphere – vital medical history, treatments, claims, management and billings. Storing and analyzing this data in a sophisticated way can help providers gain meaningful insights – insights that could help an organization work on pain points.
2.) Making information flow transparent
Healthcare data is significantly valuable, and it can be pulled to extract significant information – it is of paramount importance that an organization is capable of creating hierarchical access for various members of the organization to keep the data properly administered. The decision maker of the organizations should have a transparent view of what is going on in the organization.
3.) Understanding population health trends
Holistic patient data can help clinicians better understand their patients and find the answer to critical questions like how many patients have multiple chronic conditions or how many patients need to immediate intervention or how many patients were readmitted within 30 days of discharge? Answers to these questions help clinicians plan the right kind of care for them.
4.) Case specific patient-centric care plans
Data, combined with advanced analytics, can help narrow the problem down to the root level and therefore, help clinicians develop precise, tailor-made care plans for their patients and provide them with the right care at the right time, the first time.
5.) Preventive care
Intimating physicians and health coaches well in advance about their patients’ health is one of the many things that can be done with data. Along with this, it can help providers to remind patients to keep up with their healthy lifestyle and track their health.
How data helps in improving quality of care
Rising costs of healthcare is one of the biggest challenges the industry is facing, but unfortunately, the outcomes are hardly in line with the expenditure. And data can be used to counter this in broadly three fields:
Challenges in Leveraging Data
Even after many advancements in the field of data and focus on quality health care, there are still some challenges lurking when it comes to using data to improve the quality of healthcare.
1.) Enormous amount of data
In 2012, the amount of healthcare data was estimated to be 150 exabytes and is poised to double in the coming decade. Only the whopping amount of data to be stored, processed and analyzed is a major challenge in itself.
2.) Data security and reliability
It is a concern among patients that their data – particularly, genetic data – may be accessed in some way that is against us. Unfortunately, their concerns are not baseless; data security is still a vulnerable aspect.
Data interoperability continues to be a major challenge in the development of health data systems. Interoperability is a must for a reliable and secure exchange of information to facilitate coordination and collaboration between providers.
4.) Shortage of analysts
According to a report, there will be a shortage of data scientists who would be able to derive meaning out of big data in the coming years. By 2018, U.S. could face a shortage of at least 140,000 people who have the required analytical skills of big data to make a decision.
The Road Ahead
The healthcare sector is set for great gains with the use of data. The global health IT solutions market is poised to grow up to $228.79 billion by 2020, and U.S. healthcare would be a major contributor. On narrowing it down, the biggest driving force behind this growth is the development of data and data-driven technologies.
Access to data is critical. Proper analysis and structuring of data are even more critical. The future of millions is bright if we completely capture the full potential of data and profit from it, but the promise of reinforcing change and driving improvement using good data holds true.
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