Health Analytics and Data Mining

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Data mining holds great potential for the healthcare industry to be capable of health systems to systematically use data and analytics to identify inefficiencies and best practices that improve care and reduce costs. One client is a health system trying to succeed in risk-based contracts while still performing well under the fee-for-service reimbursement model. The transition to value-based purchasing is slow one. Until the flip is switched all the way, health systems have to design processes that enable them to straddle both models. This client is using data mining to lower its census for patients under risk contracts, while at the same time keeping its patient volume steady for patients not included in these contracts. 

The healthcare industry is overflowing with examples of how mathematical and statistical data mining is required to address pressing business cases in the clinical, financial, and operational environments.  Some of these uses cases include:

  • Identifying unnecessary utilization of high-cost services such as imaging tests or emergency department use
  • Understanding patient flow through a clinic or call volume to an after-hours nursing hotline
  • Tracking the prescription rate of a certain opioid by provider
  • Tallying the number of patients in a given population with a diabetes diagnosis
  • Measuring provider performance on a given process measure

The growing healthcare industry is generating a large volume of useful data on patient demographics, treatment plans, payment, and insurance coverage attracting the attention of clinicians and scientists alike. Use of human-generated data is predominant considering the wide adoption of Electronic Medical Record in clinical care. However, analytics based on website and social media data has been increasing in recent years. Lack of prescriptive analytics in practice and integration of domain expert knowledge in the decision-making process highlighted the necessity of future research.

Data Mining is regulated by HIPAA covered healthcare facilities and therefore maintains electronic health records with a dazzling array of patient data. The traditional methods of analyzing and processing huge data sets of accumulated information at the hospital through EDI transactions is becoming more acute and complex and therefore, necessitates applying methodologies along with technologies in healthcare to recommend for the best treatment procedures. The Big Data Analytics Companies are working steadfastly in AI for Healthcare to develop mobile app solutions and enable medical consultation to become more accessible even to the layman level.

  • Evaluate treatment progress
  • Use predictive analytics to recommend medicine
  • Enhance the level of medical services
  • Manage out-bound patients efficiently

Typically, Data mining processes have the ability to discover the hidden knowledge present within the collection of medical data and then identify the patient’s illness with great accuracy. This procedure includes many steps of working through iterative and interactive data sequences for adjudging the major symptoms of infectious disease and then treats the patient well.

Data Selection

The original data in abundance have to be considered here for knowledge discovery and then form in the target data prospects for data sciences.

Data Pre-processing

The healthcare data need to be cleaned through applying stringent strategies and ensure the dataset is kept ready for preparing the time-sequence information.

Data Transformation

These datasets can even be reduced and projected on time sequencing plots to find the discrete and invariant aspects of health related data sciences at great precision.

Data Mining

Data Mining involves the extract of data patterns using complex methods, tasks and algorithms. It benefits doctors to easily decipher the most enchanting data pattern that facilitates in producing the results appropriately.

Data Interpretation or Evaluation

This is a feedback given by the user to reinvigorate the extract knowledge prevailing in the mined data patterns.

Data Mining Techniques in Healthcare

Data Mining Techniques can create the association rules formidably and then discover the significant relationships present within the collection of healthcare data. There are only a few no of data mining parameters readily available for understanding the patient data and this includes:

  • Sequence or Path Analysis
  • Classification
  • Clustering
  • Forecasting

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