Healthcare Predictive Analytics Market Size Will Surpass $30.71 Bn by 2028, The Market to Witness Huge Growth More than 22% in Next 5 Years- Arizton

CHICAGO, May 25, 2023 /PRNewswire/ -- According to Arizton's latest research report, the healthcare predictive analytics market will grow at a CAGR of 22.23% from 2022-2028.

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Browse In-Depth TOC on Healthcare Predictive Analytics Market
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Healthcare costs have surged, and the COVID-19 pandemic has accelerated the inflation of healthcare expenses worldwide. Limited access to quality healthcare due to financial constraints is prevalent in low- and middle-income countries (LMICs). COVID-19 has expedited various existing and emerging healthcare trends, particularly regarding health equity and environmental sustainability. Furthermore, healthcare executives globally are focused on shifting consumer preferences and behavior, integrating life sciences with the healthcare sector, rapidly advancing digital health technologies, introducing new talent and care delivery models, and implementing clinical innovation. Emerging technological applications like big data and advanced analytics are empowering health systems. Predictive analytics significantly improves patient management and ensures that treatments or surgical interventions are performed optimally. Likewise, the Internet of Things (IoT) and cloud computing can aid doctors in continuously monitoring a patient's vital signs.

Predictive analytics has provided its value in hospital settings and at home by preventing patients from backsliding into a need for acute care. After a hospital stay, many patients are discharged without long-term health monitoring, leaving them at risk of adverse events and hospital readmissions that could have been avoided with appropriate preventative measures. Among the frail and elderly, falls at home are particularly common and a leading cause of fatal and nonfatal injuries. It presents another opportunity for predictive analytics to transform a reactive healthcare approach into a proactive one.

Healthcare Predictive Analytics Market Report Scope

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Availability of Abundant Patient Data Encouraging Usage of Predictive Analytics
The availability of abundant patient data is encouraging the usage of predictive analytics in healthcare and boosts the growth of the healthcare predictive analytics market. Predictive analytics uses data mining, machine learning, and statistical techniques to identify patterns and trends that can help diagnose illnesses, identify risk factors, and predict health outcomes. This data can also identify high-risk patients, suggest interventions, and provide insights for improving patient care. By leveraging this data, healthcare professionals can make more informed decisions, leading to better outcomes and improved patient safety. Additionally, predictive analytics can help health organizations improve efficiency and decrease costs by reducing unnecessary testing and treatment. The availability of abundant patient data pushes healthcare organizations to adopt predictive analytics technology and use it to its fullest potential.

Predictive AI Enhancing Patient Care and Health Outcomes
Predictive AI in healthcare is an emerging field that utilizes artificial intelligence to assist healthcare providers in making informed decisions regarding patient care. By analyzing extensive data from various sources, predictive AI offers valuable insights into a patient's condition, treatment history, and other relevant information, enabling healthcare professionals to anticipate health issues and create targeted treatment plans. Integrating AI into patient care enhances diagnostic accuracy, treatment efficacy, patient outcomes, and cost reduction. Moreover, predictive AI can identify potential disease risk factors and provide early indications of health problems. AI can analyze patient records in healthcare predictive analytics to predict the probability of specific disorders and illnesses. Recent research has demonstrated AI's capability to detect challenging-to-identify and diagnose conditions, including rare genetic disorders and neurodegenerative diseases.

Predictive Analytics in Home Health
Predictive analytics in home health utilizes data-driven models and algorithms to anticipate and forecast future health outcomes and risks. These analytical techniques are commonly employed by clinicians, administrators, providers, and health plans in the home healthcare setting to identify and mitigate potential risks. By leveraging predictive analytics, healthcare providers can make more informed decisions regarding patient care, leading to improved care quality and outcomes. Predictive analytics aids in identifying patients who are likely to require higher levels of care or additional services, pinpointing individuals who may benefit from personalized treatments, evaluating the impact of interventions on treatment outcomes, and enhancing communication between providers and patients. Furthermore, predictive analytics can assess outcomes such as mortality, readmission, and healthcare costs, providing valuable insights for optimizing care delivery.

Increasing Importance of Predictive Analytics in Healthcare
The increasing importance of the healthcare predictive analytics market is projected to support the healthcare analytics industry. Predictive analytics helps healthcare practitioners identify potential health issues and take preventive action before it is too late, thus providing cost and time efficiency to healthcare providers. Data from medical sensors, clinical laboratories, pharmacy information systems, and other sources help healthcare providers deliver tailored and personalized patient care. Predictive analytics helps organizations to identify patterns and trends in large amounts of data and utilize the insights to improve operational efficiency, reduce costs and improve patient outcomes. Predictive analytics enables the healthcare provider to predict future health trends, identify at-risk patients, and formulate relevant intervention programs. The increasing use of healthcare analytics is leading to enhanced accuracy in diagnosis, improved patient outcomes, and reduced cost.

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Key Company Profiles

• Health Catalyst
• IBM
• Koninklijke Philips
• Microsoft
• Oracle
• UNITEDHEALTH GROUP
• Allscripts Healthcare
• Apixio
• CGI
• Change Healthcare
• Conduent
• COTIVITI
• Epic Systems
• Flatiron
• Forian
• FraudLens
• FRISS
• Healthcare Fraud Shield
• LexisNexis Risk Solutions
• MedeAnalytics
• Net Health
• OSP
• PotentiaMetrics
• SAS Institute
• Verisk Analytics
• Wipro

Market Segmentation 

Delivery Mode

• On-premises
• Cloud-based

Application

• Finance & Operations
• Clinical Care
• Population Health Management
• R&D

End-user

• Payers
• Healthcare Settings
• Others

Geography

• North America
• The U.S.
• Canada
• Europe
• Germany
• The U.K.
• France
• Italy
• Spain
• APAC
• China
• Japan
• India
• South Korea
• Australia
• Latin America
• Brazil
• Mexico
• Argentina
• Middle East & Africa
• Turkey
• Saudi Arabia
• South Africa

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Table of Contents

TABLE OF CONTENTS

1 RESEARCH METHODOLOGY

2 RESEARCH OBJECTIVES

3 RESEARCH PROCESS

4 SCOPE & COVERAGE

4.1 MARKET DEFINITION

4.1.1 INCLUSIONS

4.1.2 EXCLUSIONS

4.1.3 MARKET ESTIMATION CAVEATS

4.2 BASE YEAR

4.3 SCOPE OF THE STUDY

4.3.1 MARKET SEGMENTATION BY DELIVERY MODE

4.3.2 MARKET SEGMENTATION BY APPLICATION

4.3.3 MARKET SEGMENTATION BY END-USER

4.3.4 MARKET SEGMENTATION BY GEOGRAPHY

5 REPORT ASSUMPTIONS & CAVEATS

5.1 KEY CAVEATS

5.2 CURRENCY CONVERSION

5.3 MARKET DERIVATION

6 PREMIUM INSIGHTS

6.1 OVERVIEW

6.1.1 GEOGRAPHY INSIGHTS

6.1.2 DELIVERY MODE SEGMENTATION INSIGHTS

6.1.3 APPLICATION SEGMENTATION INSIGHTS

6.1.4 END-USER SEGMENTATION INSIGHTS

7 MARKET AT A GLANCE

8 INTRODUCTION

8.1 OVERVIEW

9 MARKET OPPORTUNITIES & TRENDS

9.1 PREDICTIVE AI ENHANCING PATIENT CARE AND HEALTH OUTCOMES

9.2 PREDICTIVE ANALYTICS IN HOME HEALTH

9.3 EMERGING USE OF PREDICTIVE ANALYTICS IN DISEASE MANAGEMENT

10 MARKET GROWTH ENABLERS

10.1 INCREASING IMPORTANCE OF PREDICTIVE ANALYTICS IN HEALTHCARE

10.2 DIGITAL TRANSFORMATION IN HEALTHCARE

10.3 GROWING NEED TO REDUCE HEALTHCARE COSTS USING PREDICTIVE ANALYTICS

10.4 AVAILABILITY OF ABUNDANT PATIENTS DATA ENCOURAGING USE OF PREDICTIVE ANALYTICS

11 MARKET RESTRAINTS

11.1 ETHICS AND MORAL HAZARDS IN HEALTHCARE PREDICTIVE ANALYTICS

11.2 SERIOUS PRIVACY CONSIDERATIONS

11.3 IMPACT OF TECHNOLOGY ON CARE AND LACK OF REGULATIONS

12 MARKET LANDSCAPE

12.1 MARKET OVERVIEW

12.2 MARKET SIZE & FORECAST

12.3 MARKET OPPORTUNITIES

12.3.1 MARKET BY GEOGRAPHY

12.3.2 MARKET BY DELIVERY MODE

12.3.3 MARKET BY APPLICATION

12.3.4 MARKET BY END-USER

12.4 FIVE FORCES ANALYSIS

12.4.1 THREAT OF NEW ENTRANTS

12.4.2 BARGAINING POWER OF SUPPLIERS

12.4.3 BARGAINING POWER OF BUYERS

12.4.4 THREAT OF SUBSTITUTES

12.4.5 COMPETITIVE RIVALRY

13 DELIVERY MODE

13.1 MARKET SNAPSHOT & GROWTH ENGINE

13.2 MARKET OVERVIEW

13.3 ON-PREMISES

13.3.1 MARKET OVERVIEW

13.3.2 MARKET SIZE & FORECAST

13.3.3 ON-PREMISE BY GEOGRAPHY

13.4 CLOUD-BASED

13.4.1 MARKET OVERVIEW

13.4.2 MARKET SIZE & FORECAST

13.4.3 CLOUD-BASED BY GEOGRAPHY

14 APPLICATION

14.1 MARKET SNAPSHOT & GROWTH ENGINE

14.2 MARKET OVERVIEW

14.3 FINANCE & OPERATIONS

14.3.1 MARKET OVERVIEW

14.3.2 MARKET SIZE & FORECAST

14.3.3 FINANCE & OPERATIONS BY GEOGRAPHY

14.4 CLINICAL CARE

14.4.1 MARKET OVERVIEW

14.4.2 MARKET SIZE & FORECAST

14.4.3 CLINICAL CARE BY GEOGRAPHY

14.5 POPULATION HEALTH MANAGEMENT

14.5.1 MARKET OVERVIEW

14.5.2 MARKET SIZE & FORECAST

14.5.3 POPULATION HEALTH MANAGEMENT BY GEOGRAPHY

14.6 RESEARCH & DEVELOPMENT (R&D)

14.6.1 MARKET OVERVIEW

14.6.2 MARKET SIZE & FORECAST

14.6.3 R&D BY GEOGRAPHY

15 END-USERS

15.1 MARKET SNAPSHOT & GROWTH ENGINE

15.2 MARKET OVERVIEW

15.3 PAYERS

15.3.1 MARKET OVERVIEW

15.3.2 MARKET SIZE & FORECAST

15.3.3 PAYERS BY GEOGRAPHY

15.4 HEALTHCARE SETTINGS

15.4.1 MARKET OVERVIEW

15.4.2 MARKET SIZE & FORECAST

15.4.3 HEALTHCARE SETTINGS BY GEOGRAPHY

15.5 OTHERS

15.5.1 MARKET OVERVIEW

15.5.2 MARKET SIZE & FORECAST

15.5.3 OTHERS BY GEOGRAPHY

16 GEOGRAPHY

16.1 MARKET SNAPSHOT & GROWTH ENGINE

16.2 GEOGRAPHIC OVERVIEW

17 NORTH AMERICA

17.1 MARKET OVERVIEW

17.2 MARKET SIZE & FORECAST

17.2.1 NORTH AMERICA BY DELIVERY MODE

17.2.2 NORTH AMERICA BY APPLICATION

17.2.3 NORTH AMERICA BY END-USER

17.3 KEY COUNTRIES

17.3.1 US: MARKET SIZE & FORECAST

17.3.2 CANADA: MARKET SIZE & FORECAST

18 EUROPE

18.1 MARKET OVERVIEW

18.2 MARKET SIZE & FORECAST

18.2.1 EUROPE BY DELIVERY MODE

18.2.2 EUROPE BY APPLICATION

18.2.3 EUROPE BY END-USER

18.3 KEY COUNTRIES

18.3.1 GERMANY: MARKET SIZE & FORECAST

18.3.2 UK: MARKET SIZE & FORECAST

18.3.3 FRANCE: MARKET SIZE & FORECAST

18.3.4 ITALY: MARKET SIZE & FORECAST

18.3.5 SPAIN: MARKET SIZE & FORECAST

19 APAC

19.1 MARKET OVERVIEW

19.2 MARKET SIZE & FORECAST

19.2.1 APAC BY DELIVERY MODE

19.2.2 APAC BY APPLICATION

19.2.3 APAC BY END-USER

19.3 KEY COUNTRIES

19.3.1 CHINA: MARKET SIZE & FORECAST

19.3.2 JAPAN: MARKET SIZE & FORECAST

19.3.3 INDIA: MARKET SIZE & FORECAST

19.3.4 SOUTH KOREA: MARKET SIZE & FORECAST

19.3.5 AUSTRALIA: MARKET SIZE & FORECAST

20 LATIN AMERICA

20.1 MARKET OVERVIEW

20.2 MARKET SIZE & FORECAST

20.2.1 LATIN AMERICA BY DELIVERY MODE

20.2.2 LATIN AMERICA BY APPLICATION

20.2.3 LATIN AMERICA BY END-USER

20.3 KEY COUNTRIES

20.3.1 BRAZIL: MARKET SIZE & FORECAST

20.3.2 MEXICO: MARKET SIZE & FORECAST

20.3.3 ARGENTINA: MARKET SIZE & FORECAST

21 MIDDLE EAST & AFRICA

21.1 MARKET OVERVIEW

21.2 MARKET SIZE & FORECAST

21.2.1 MIDDLE EAST & AFRICA BY DELIVERY MODE

21.2.2 MIDDLE EAST & AFRICA BY APPLICATION

21.2.3 MIDDLE EAST & AFRICA BY END-USER

21.3 KEY COUNTRIES

21.3.1 TURKEY: MARKET SIZE & FORECAST

21.3.2 SAUDI ARABIA: MARKET SIZE & FORECAST

21.3.3 SOUTH AFRICA: MARKET SIZE & FORECAST

22 COMPETITIVE LANDSCAPE

22.1 COMPETITION OVERVIEW

22.2 MARKET SHARE ANALYSIS

22.2.1 HEALTH CATALYST

22.2.2 IBM

22.2.3 KONINKLIJKE PHILIPS

22.2.4 MICROSOFT

22.2.5 ORACLE

22.2.6 UNITEDHEALTH GROUP

23 KEY COMPANY PROFILES

23.1 HEALTH CATALYST

23.1.1 BUSINESS OVERVIEW

23.1.2 PRODUCT OFFERINGS

23.1.3 KEY STRATEGIES

23.1.4 KEY STRENGTHS

23.1.5 KEY OPPORTUNITIES

23.2 IBM

23.3 KONINKLIJKE PHILIPS

23.4 MICROSOFT

23.5 ORACLE

23.6 UNITEDHEALTH GROUP

24 OTHER PROMINENT VENDORS

24.1 ALLSCRIPTS HEALTHCARE

24.1.1 BUSINESS OVERVIEW

24.1.2 PRODUCT OFFERINGS

24.2 APIXIO

24.3 CGI

24.4 CHANGE HEALTHCARE

24.5 CONDUENT

24.6 COTIVITI

24.7 EPIC SYSTEMS

24.8 FLATIRON

24.9 FORIAN

24.10 FRAUDLENS

24.11 FRISS

24.12 HEALTHCARE FRAUD SHIELD

24.13 LEXISNEXIS RISK SOLUTIONS

24.14 MEDEANALYTICS

24.15 NET HEALTH

24.16 OSP

24.17 POTENTIAMETRICS

24.18 SAS INSTITUTE

24.19 VERISK ANALYTICS

24.20 WIPRO

25 REPORT SUMMARY

25.1 KEY TAKEAWAYS

25.2 STRATEGIC RECOMMENDATIONS

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