Healthcare Analytics Companies

Healthcare data is complex and text heavy. Healthcare analytics software enable healthcare companies to analyze their data and derive insights.

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Compare Healthcare Analytics Companies
Results: 13

AIMultiple is data driven. Evaluate 13 products based on comprehensive, transparent and objective AIMultiple scores. For any of our scores, click the icon to learn how it is calculated based on objective data.

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97.99999999827878
100
100
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79.99999998278778
top5 , top10
top5 , top10
5star
IBM Watson Health
5.00
100%
100%
100%
= 1 review
= 20 employees
= 100,000 visitors

56.86401908764408
57.98382208661197
52.7777997124626
60
0.01626984126984127
46.78579209693305
top5 , top10
top5 , top10
3star
Ayasdi
3.00
50%
100%
100%
= 1 review
= 20 employees
= 100,000 visitors

1.38639508784431
1.3338506640437544
44.44442689669658
0
0.017261904761904763
1.859294902049307
top5 , top10
top5 , top10
Apixio
0%
100%
100%
= 1 review
= 20 employees
= 100,000 visitors

0.827485915356766
0.7336257370054101
24.44446901129145
0
0.009722222222222222
1.6722275205189685
top5 , top10
top5 , top10
Enlitic
0%
59%
100%
= 1 review
= 20 employees
= 100,000 visitors

0.6945739387900073
0.6004884110975568
20.00001052864872
0
0.01626984126984127
1.541343688022062
top5 , top10
top5 , top10
Health Fidelity
0%
100%
100%
= 1 review
= 20 employees
= 100,000 visitors

Popularity

Searches with brand name

These are the number of queries on search engines which include the brand name of the product. Compared to other product based solutions, healthcare analytics companies is more concentrated in terms of top 3 companies' share of search queries. Top 3 companies receive 92% (14% more than average) of search queries in this area.

Web Traffic

Healthcare analytics companies is a less concentrated than average solution category in terms of web traffic. Top 3 companies receive 62% (15% less than average solution category) of the online visitors on healthcare analytics companies company websites.

Maturity

Number of Employees

Median number of employees that provide healthcare analytics companies is 82 which is 18 more than the median number of employees for the average solution category.

In most cases, companies need at least 10 employees to serve other businesses with a proven tech product or service. 12 companies (36 less than average solution category) with >10 employees are offering healthcare analytics companies. Top 3 products are developed by companies with a total of 0.5-1M employees. However, all of these top 3 companies have multiple products so only a portion of this workforce is actually working on these top 3 products.

IBM
flatiron
Berg
Digital Reasoning

Learn More About Healthcare Analytics Companies

What is healthcare analytics?

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

Which types of data healthcare analytics companies rely on?

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.

Why is healthcare analytics relevant now?

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

What are its use cases?

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.

What are the benefits of working with healthcare analytics companies?

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.

How do healthcare analytics companies use AI?

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.

Why is it critical to choose the right healthcare analytics system?

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.