Healthcare analytics companies provide insights into hospital management, diagnosis and patient outcomes by collecting and analyzing healthcare data.

Using healthcare analytics, organizations can improve their patient care decisions, services, and existing procedures. Healthcare analytics relies on the following data:

• cost and claims data so that insurance companies can optimize their policies and their pricing
• research and development data help organizations find innovative treatments
• clinical data that contain patient outcomes

Electronic health records (EHR) are the most important source of clinical data. They are a collection of patient and population health information in a digital format. Physicians use EHR to monitor patient care. EHR is a broader term that includes data on administrative and demographic information, diagnosis, treatment, prescription drugs, laboratory tests, billing, scheduling, claims etc.

Healthcare data is complex. The amount of healthcare data and potential benefits of leveraging healthcare data is increasing. These all make using advanced analytics in healthcare a valuable yet challenging activity.

Healthcare data is complex since it comes from a variety of sources and has to comply with government regulations. According to Seagate , the healthcare industry will have the most CAGR in the amount of data created, captured, and replicated between 2018-2025. The report highlights that the datasphere growth rate of the healthcare industry is 36% which is higher than manufacturing, financial services, media and entertainment industries. Therefore healthcare analytics has the challenge of interpreting a large volume of structured and unstructured data into insights that help enhance medical treatments while increasing the efficiency of healthcare services

Healthcare

• Reducing readmission costs: Due to the Hospital Readmissions Reduction Program (HRRP), patients don't need to pay for services if they have to be readmitted to the hospital. By implementing healthcare analytics, healthcare providers can gain accurate insights regarding the patient’s health for more effective decision making. Therefore healthcare providers can avoid readmissions.
• Reducing administrative cost: As in any other industry, analytics solutions can identify inefficiencies and help reduce costs.
• Supply Chain: Like every other industry, healthcare organizations can also benefit from predictive analytics tools to forecast supply-demand needs. According to Navigant survey, hospitals can preserve up to $9.9 million per year (approximately 17.8% of total cost) in supply chain costs if they use data analytics for their Economic Order Quantity(EOQ) and stocking decisions.
• Improving patient care: Healthcare analytics turns clinical information into actionable intelligence to support evidence-based decisions and improve patients’ care. For example, Kaiser Permanente, a large hospital chain in the US, implemented the HealthConnect system that facilitated data sharing across facilities. This system improved cardiovascular disease outcomes and saved $1 billion from reduced office visits and lab tests according to McKinsey.

Pharma R&D

• Drug Discovery: There is a vast amount of data sets of patents, scientific publications, and clinical trial data. With this data, researchers can identify unknown information in clinical trials that can potentially enhance the drug discovery process. For example,
  • Project Data Sphere is an initiative that pharmaceutical companies share their data about cancer patients so that researchers can access the data to enrich clinical trials.
  • Mayo Clinic launched its clinical data analytics platform to use insights derived from data to enhance healthcare and accelerate drug discovery. The platform uses AI and machine learning models to analyze large amounts of data sets.
• Predicting Patients’ Responses: Healthcare providers can use predictive analysis tools to predict outcomes of medicines. They make correlations between clinical notes and patients' data such as genome structure, symptoms, habits, historical diseases.

Insurance

• Risk Scoring: By using predictive modeling while performing healthcare analytics, insurance companies can give risk scores for each patient based on lab testing, biometric data, claims data, patient-generated health data. Therefore insurance companies can ask for a price that is dependent on the risk score of the patient.

Healthcare analytics can have a positive impact on both healthcare providers and the pharmaceutical industry. We've written about the benefits that healthcare companies achieve with healthcare analytics before, feel free to check it out.

According to an Accenture report, growth in the AI healthcare market is expected to reach $6.6 billion by 2021 with a CAGR of 40%. AI can be used to both automate the process of analytics and make analytics solutions more effective thanks to advanced analytics techniques that rely on machine learning. Potential impacts of AI on healthcare analytics are as follows:

• Natural Language Processing (NLP) can extract useful information from doctors’ notes, patients’ prior histories and related research papers. Amazons Comprehend Medical is already in use to extract data such as medical condition, medication, dosage from a variety of sources like doctors’ notes, clinical trial reports, and patient health records.
• Using insighs from millions of medical outcomes, machine learning techniques can be used to diagnose patients, suggest treatments and check prescriptions for potential errors.
• AI powered IoT devices can help individuals self assess their health conditions. Sensoria provides smart wearables (e.g. socks, heart rate monitors) that helps runners improve form and performance and to speed up recovery times after an injury. Another IoT example is AliveCor which provides personal health analysis. The device detects atrial fibrillation, bradycardia, tachycardia or normal heart rhythm. Detected issues can be shared with the user’s doctor.

Healthcare analytics vendors should be able to overcome the following problems that organization may experience:

• Data federation issues such as data silos
• Data quality issues such as inconsistent or variable definitions
• Analytics challenges

To learn more about these issues, check our article.