healthcare data harvesting

Data harvesting or data mining, in simple terms, is the process of collecting, structuring and analysing reams of data to identify patterns and draw inferences from.  

Let’s take a simple example to understand why data harvesting is critical for healthcare companies. 

When the pandemic hit in 2020, it was a virus that the world was not familiar with. And, the symptoms varied from patient to patient. Some had loss of taste and smell, some had regular flu symptoms, and some others had more severe infections which led to lung failure.  

In such a scenario, if the healthcare provider relies on a clinical decision making system, the system can use AI and machine learning techniques to analyse the varied symptoms of a range of patients and suggest the right set of medications that can treat and prevent further spread of the virus. 

A Merit expert says, “Of course, healthcare providers can’t rely entirely on these systems. These insights act as enablers for them to make quicker and more effective decisions in patient care.” 

According to a report, the big data healthcare market, which includes software and service solutions for the sector, is likely to touch USD 44.53 billion (from the current USD 20.27 billion), by 2026, growing at a CAGR of 21.75%.  

The data harvesting process in healthcare businesses 

A typical process that is followed during data harvesting is; 

  • The first step lies in collating data from various sources – like knowledge bases, patient records, clinical studies, social media channels etc. In this step, data is typically collected from structured and unstructured sources. 
  • Then, the collated data is classified and categorised based on administrative information like financial details, credit card numbers and social security numbers, or based on correlations between medicines and medical illnesses. For example, a blurred vision or fluid collection in the lungs may be associated with diabetes. Classification is based on bringing together complex data, and it makes it easier for doctors to draw better inferences. 
  • The third stage is associating the data with other data sets. For example, associating data can draw patterns on diagnosing excessive fatigue – it could be due to poor nutrition, low iron content in the body, or because of a possible onset of hypertension. 
  • The next stage is predicting a future ailment using current data – for example, data harvesting can analyse a number of data points to predict a likely heart problem in a patient, or the onset of a tumour given the current medical condition and lifestyle. 

6 Benefits of Data Harvesting in Healthcare 

Data harvesting has increased in popularity within healthcare companies in the last few years. Here’s why. 

  1. Prognosis backed by patient records and clinical studies 

Clinical decision support systems analyse data from knowledge bases, patient records and other third-party sources (like social media channels) to give users faster and clearer insights into any patterns, or patient behaviour and lifestyle they need to be aware of when delivering a prognosis. In fact, it also aids in comparing patient data with clinical studies or research they may be running. According to data by Markets & Markets, the clinical data support systems market is likely to touch USD 2.2 billion by 2027, growing at a CAGR of 7.9%. 

  1. Verifying inferences with experts in specific fields 

Not only do these AI and ML applications structure data, they also deliver inferences that healthcare providers can verify with experts before starting the treatment. For example, as part of data harvesting, these technologies can analyse X-rays and MRI reports to detect a likely abnormality, and also reduce the error that may occur from manual diagnosis. The efficiency of these technologies, in turn, translate to healthcare providers treating patients (remotely and in-person) at lesser costs. 

  1. Increasing accountability of healthcare providers to patients 

Data harvesting can foster more accountability and build a more personalised relationship between the healthcare provider and the patient. For example, using patient medical records and treatment plans, healthcare providers can set automated reminders for patients to take their medicines on time, or send reminders to schedule follow-ups as needed. 

  1. Optimise administrative processes  

It can greatly improve administrative processes in healthcare institutions. Be it a small clinic, a pharmacy chain or a large hospital, they’re typically collecting a ton of data on patient history, treatment, tests, insurance claims and such, on an everyday basis.  

With data harvesting technologies in place, healthcare administrators can process medical insurance claims more efficiently (because the data is structured and accessible easily). In fact, they can even avoid insurance fraud. Moreover, when a patient comes in for consultation, they have a clear record of their past visits, treatments and medical conditions, which can enable doctors to make more holistic, effective treatment decisions.  

  1. Increasing accessibility of healthcare and improving patient experience 

It makes healthcare accessible to all and improves patient experience. Let’s say you’re new to a city and unfamiliar with doctors or specialists in your area. Websites that use data harvesting, collect valuable information on healthcare providers in the locality, including their years of experience, specialties and reviews, and provide a list of options for patients to choose from. In a way, the power of decision-making is left to the patient. 

  1. Predictive analytics to make decisions for the future 

As an example, healthcare providers can prepare for possible seasonal flu or infections. This ensures that there are enough doctors available to handle the patient influx, the drugstore is stocked with the right medications, and the on-floor support staff is allocated according to likely demand. 

In conclusion, implementing data harvesting in healthcare can bring more efficiency in administrative processes, aid in better clinical decision making, prevent fraud, enable better resource management, provide healthcare at reasonable costs, and increase ROI as well. 

Having said that, healthcare is one sector that is highly regulated given the nature of data being collated for use. When implementing data-driven decision making in healthcare, organisations need to place governance and security at the forefront. 

Merit’s Expertise in Healthcare Data Harvesting 

Our state-of-the-art data harvesting engine collects high-volume, industry-specific data at 4 times the speed, with 30% more accuracy than normal scrapers, and at a lower cost. 

Our solutions help some of the world’s largest healthcare brands seamlessly deliver data and insights to their end customers, including: 

  • Delivering curated content from thousands of online documents or PDFs 
  • Aggregating millions of specialised, industry-specific data points 

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