Healthcare analytics describes the activities of healthcare analysis that can be undertaken as an effect of data obtained from four areas within healthcare, namely claims and cost data, pharmaceutical and R&D data, clinical data (electronic medical records) and patient behaviour and feedback data.
The healthcare system in our country is on a data-driven transformation, rendering value-based care supported by a systematic digital and analytical tactic. Till recently, as data flows in from disparate sources, it poses several challenges in data aggregation and data governance policies. Besides, the healthcare system is facing a demand for skilled data analysts, who can precisely leverage data to connect patients and drive operational efficiency.
According to Rohit Kumar, co-founder of THB and the Chief of Analytics at the Gurugram based clinical research and data analytics start-up. “Healthcare Analytics can be scrutinized diversely by different stakeholders. For instance, doctors are more concerned about clinical analytics, such as personalized treatment where data drives decision making and can hint on the next course of treatment for a particular patient suffering from an ailment, that will give optimal results. For government or organizations, it can entail collated data to categorize patterns in disease occurrence/recurrence or even treatment researches.”
Predictive analytics and machine learning in healthcare are swiftly attracting a happening discussion in healthcare analytics. Machine learning is a trending discipline with successes in many enterprises as they can jumpstart the utility of predictive analytics for improving patient care, chronic disease management, hospital administration and cater to chain efficiencies. The prospect that currently subsists for healthcare systems is to label what ‘predictive analytics’ means to them and how can it be used most effectively to make improvements.
Let’s have an intercourse on how to kick-start a career as a healthcare data analyst in India. We can surf about articles on how the field has seen a boom in US and Canada with the heave in genomic science and computational medical treatment. The foremost step is getting a masters’ degree in data analytics and gaining experience in the healthcare sector. To get a clear idea, we will ponder over the paramount practices and guidelines for diving into this up and coming sector.
• A principal challenge in healthcare analytics is unstructured data. Data comes in all types and there is a constant chance of encountering a new type. Hence, general tabular format of data may not be favourable.
• Variables have to be taken in entirety since few variables cannot offer reliable answers. So, the quantum of data is huge. That requires algorithms to be supple enough to integrate new information and infer consequently.
• Data and medical information may not always direct to the same answer, and in those instances, medical information wins. So, basic clinical knowledge needs to be kept in mind. Besides, there needs to be adequate checks and balances to impede blunders.
• As information gets outdated quickly in healthcare, one has to be in track with the updates.
The next big query is what kind of data gets top priority? According to Kumar, “In healthcare, analysts need to work with textual data combining medicine, diagnosis and the complaints. Textual analysis gets top priority and it is always better to have domain knowledge. There are no standard codinglanguage guidelines for this field, but you have to be equipped with a language that will give flexibility in digging around with bulky and unstructured data. Python is an apt choice for the job.”
And now, we have to take notes on how to get the basics right. Kumar says “The best training when working as healthcare analyst is defining the problem statement accurately and build a solution model. Generic solutions, by and large, don’t work in healthcare as the scope of error is very tiny. Inspecting the accuracy and identifying the basis of data is vital. We should discern which data is machine/system generated and which is manually entered. Privacy of data is of chief significance. Therefore, when working on analytics, we have to ensure never to utilize any ‘Personal Identifiable Information’ of a patient.”
Coming to the main Tit-bits: The emerging opportunities in Healthcare analytics and salary! “An ideal Healthcare Business Analyst candidate must be aware of medical terminologies with excellent problem-solving skills. For the position of a senior business analyst, proficiency in SAS, SQL, Hadoop and Hive is a necessity. Job responsibilities comprise of statistical techniques in customer segmentation and profiles, generating and reassessing models and ensuring technical leadership. The top companies that are on the lookout for hiring healthcare analytics are McKinsey & Company, Accenture, Philips and few start-ups like Artivatic Data Labs Private Limited and THB.”, says Kumar, and when probed on the expected salary, he continues with a hearty laugh, “While there is no exact figure on salary in healthcare, a fresher candidate with no industry experience will get a package starting at INR 6 lakhs. With a 2-year experience, the candidate may receive up to INR 10 lakh and INR 15 lakh after 5 years.”
Summing it up, we can conclude that Healthcare analytics will become a hot opportunity with a focus on improvement strategies of healthcare organizations. Historically, data analysts in healthcare systems have not had transparent roles other than sifting through lengthy report queues, and the downpour of report requests, making them busy bees. In today’s situation, analysts have traversed from gathering and collecting data to analysing it, thus taking part in expert improvement teams. The theme of work is to have a collaborative, multidisciplinary coexistence with clinicians and operational leaders to help build up the finest presentation of the data for utilization across the organization and as such helping to recognize gaps and incorporate recommended actions that help drive improved performance outcomes.