[email protected] +44 20 8123 2220 (UK) +1 732 587 5005 (US) Contact Us | FAQ |

Computer Assisted Coding: Market Shares, Strategies, and Forecasts, Worldwide, 2016 to 2022

March 2017 | 299 pages | ID: C245536F23AEN
WinterGreen Research

US$ 4,200.00

E-mail Delivery (PDF)

Download PDF Leaflet

Accepted cards
Wire Transfer
Checkout Later
Need Help? Ask a Question
Next generation Computer Assisted Coding of medical information is able to leverage natural language software technology to support some automation of the billing process and use of analytics to achieve higher quality patient outcomes. The study has 299 pages and 110 tables and figures.
Computer assisted coding of medical information uses natural language solutions to link the physician notes in an electronic patient record to the codes used for billing Medicare, Medicaid, and private insurance companies.
Natural language processing is used determine the links to codes. 88% of the coding can occur automatically without human review. Computer assisted coding is used in all parts of the healthcare delivery system. The coding systems work well to implement automated coding process.
Physicians think about patient conditions in terms of words. Software is configured to achieve working with physicians who are more comfortable describing a patient treatment in words than codes. The electronic patient record, created using physician dictation, is used to form the base for the coding. Natural language solutions implement computer coding to identify key words and patterns of language. The physician dictation can be done using regular language that the software recognizes and translates into billing codes.
Properly designed natural language processing (NLP) solutions do not require physicians to change the way they work. They can dictate in a free-flowing fashion, consistent with the way they think, and are not limited to structured inputs that may or may not fully capture the unique circumstances of each patient encounter.
Matching codes generated from physician notes to standard treatment protocols promises to improve health care delivery. Accompanying that type of physician patient management against best practice promises to revolutionize health care delivery. The ability to further check as to whether the recommendations for follow up made by radiologists and matching the commendations with the actual follow up heralds’ significant promise of vastly improved health care delivery.
Computer assisted coding applications depend on the development of production quality natural language processing (NLP)-based computer assisted coding applications. This requires a process-driven approach to software development and quality assurance.
A well-defined software engineering process consists of requirements analysis, preliminary design, detailed design, implementation, unit testing, system testing and deployment. NLP complex technology defines the key features of a computer assisted coding (CAC) application.
Automation of process will revolutionize health care delivery. In addition to automating the insurance, billing, and transaction systems, streamlined care delivery is an added benefit. The ability to look at workflow and compare actual care to best practice is fundamental to automated business process.
The ability to link diagnostic patient information to treatment regimes and drug prescriptions is central to improving medical care delivery. Once a physician can see what conditions need to be followed, and see that appropriate care has been prescribed 100% of the time, care delivery improves dramatically. Diagnosis of conditions using radiology frequently results in detection of events that need follow-up.
“Growing acceptance of computer assisted coding for physician offices represents a shift to cloud computing and billing by the procedure coded. Because SaaS based CAC provides an improvement over current coding techniques the value is being recognized. Administrators are realizing the benefits to quality of care. Patients feel better after robotic surgery and the surgeries are more likely to be successful.”
The worldwide market for Computer Assisted Coding is $2.8 billion in 2016, anticipated to reach $5.1 billion by 2023. The complete report provides a comprehensive analysis of Computer Assisted Coding in different categories, illustrating the diversity of software market segments. A complete procedure analysis is done, looking at numbers of procedures and doing penetration analysis.
Major health plans report a smooth transition to ICD-10. This is due to rigorous testing for six years. ICD-10 has had a positive impact on reimbursement. ICD-10 coding system requires use of 72,000 procedure codes and 68,000 CM codes, as opposed to the 4,000 and 14,000 in the ICD-9 system. Managing high volume of codes requires automation. Healthcare providers and payers use complex coding systems, which drives demand for technologically advanced CAC systems.
The market for computer-assisted coding grows because it provides management of workflow process value by encouraging increasing efficiency in care delivery. By making more granular demarcation of diagnoses and care provided for each diagnosis, greater visibility into the care delivery system is provided. Greater visibility brings more ability to adapt the system to successful treatments.
COMPUTER ASSISTED CODING EXECUTIVE SUMMARY

  Medical Best Practice Linking
  CAC for Coders
  Computer Assisted Coding Best Practice
  Coding Solutions
  Physician Computer Assisted Coding Services
Natural Language Computer Assisted Coding Market Shares
Natural Language Computer Assisted Coding of Medical Procedures Forecasts
  Medical Best Practice Linking

1. COMPUTER ASSISTED CODING MARKET DESCRIPTION AND MARKET DYNAMICS

1.1 Coding Challenge
  1.1.1 Computer Assisted Coding (CAC)
  1.1.2 Advances In Natural Language Processing And Informatics
  1.1.3 Using Electronic Health Record (EHR) Documentation To Generate Codes
1.2 Computer-Assisted Coding
  1.2.1 Physician Practice Industry Forces Affecting Development of CAC
  1.2.2 Application of CAC Technology
1.3 Development of a CAC Tool For Hospital Inpatient Use
  1.3.1 CAC Impact on the Coding Workflow
  1.3.2 Computers Replace Human Coders
  1.3.3 CAC Applied Without Human Intervention Depends On Critical Differences Between CAC Systems
1.4 Healthcare Industry Largest In United States
  1.4.1 Building a Safer Health System
  1.4.2 Facilitating the Use of Technology in the Healthcare Industry
  1.4.3 Prescription Drug Modernization
1.5 Medical Necessity and Medical Necessity Errors
1.6 Physician Office Electronic Coding
  1.6.1 CAC Automates and Accelerates Auditing
1.7 Natural Language Solutions
  1.7.1 State Of Language Technology Evaluation
1.8 Computerized Workflow System
  1.8.1 Confidence Assessment Module
  1.8.2 Researching Electronic Coding Products:

2. COMPUTER ASSISTED CODING MARKET SHARES AND FORECASTS

  2.1.1 Medical Best Practice Linking
  2.1.2 CAC for Coders
  2.1.3 Computer Assisted Coding Best Practice
  2.1.4 Coding Solutions
  2.1.5 Physician Computer Assisted Coding Services
2.2 Natural Language Computer Assisted Coding Market Shares
  2.2.1 3M
  2.2.2 3M
  2.2.3 3M Merging Quality With Reimbursement
  2.2.4 Optum
  2.2.5 Optum Automated Code Identification
  2.2.6 Optum
  2.2.7 nThrive / Precyse
  2.2.8 Dolbey
  2.2.9 McKesson
  2.2.10 Cerner
  2.2.11 TruCode
2.3 Natural Language Computer Assisted Coding of Medical Procedures Forecasts
  2.3.1 CAC Market Software and Services Segmentation
  2.3.2 CAC Hospitals and Facilities and Physicians Market Segment
  2.3.3 Computer Assisted Coding Physician Market
  2.3.4 Worldwide Computer Assisted Coding, Hospitals and Facilities and Physicians
  2.3.5 CAC Software Market Hospital and Physician Segments
  2.3.6 CAC Services Market Segment
  2.3.7 US Computer Assisted Coding Physician Software License / Maintenance and Cloud SaaS Services
  2.3.8 Physicians Computer Assisted Coding
  2.3.9 US Computer Assisted Coding Software Units
  2.3.10 US Computer Assisted Coding Software / Cloud Services for Independent Radiology Clinics Market Forecasts
  2.3.1 US Independent Radiology Imaging Centers
  2.3.2 Erroneous Selection of Principal Diagnoses Impacting Reimbursement
  2.3.3 Growth of the U.S. Healthcare Industry
2.4 Worldwide, Number of Patients and Procedures
2.5 Making The Shift To The Modern ICD-10 Requirements
2.6 Computer Assisted Coding Prices
2.7 Computer Assisted Coding Regional Analysis
  2.7.1 3M 360 Encompass System

3. COMPUTER ASSISTED CODING PRODUCT DESCRIPTION

3.1 3M
  3.1.1 3M 360 Encompass System
  3.1.2 3M 3M CodeAssist System
  3.1.3 3M APR DRG Solutions Aspects
  3.1.4 3M Merging Quality With Reimbursement
  3.1.5 3M APR DRG Software
  3.1.6 3 M Classification System For Patients
  3.1.7 3M APR DRG Software Features:
  3.1.8 3M Coding Technology
  3.1.9 3M Computer-Assisted Coding Solutions
  3.1.10 3M Medical Coding Tools Streamline Processes
  3.1.11 CodeAssist Automating the Medical Coding Process
  3.1.12 3M CodeComplete Outsource Solution for Medical Coding
  3.1.13 3M DataScout Clinical Data Extraction and Identification
  3.1.14 3M and American Academy of Professional Coders (AAPC)
  3.1.15 3M Data Mining Technology
  3.1.16 3M Systems for Overcoming Documentation Shortfalls
  3.1.17 3M Solutions for a Changing Healthcare Landscape
  3.1.18 3M Web-Based Coding Software Return on Investment
  3.1.19 3M Coding Software Functions
  3.1.20 3M Computer-Assisted Coding Solutions Targeted to Specialty Areas
  3.1.21 3M CodeAssist Functions
  3.1.22 3M CodeComplete Business Process Management
3.2 Dolbey
  3.2.1 Dolbey Coding Productivity Management
  3.2.2 Dolby Fusion Suite Modules
3.3 Optum Coding Service
  3.3.1 Optum Coding
  3.3.2 Optum CPT Codes
  3.3.3 Optum Medicare Fee Schedule
3.4 McKesson
  3.4.1 Mckesson Watching the Cash
  3.4.2 McKesson Securing the Subsidy
  3.4.3 McKesson Quality Control And Process Improvement
3.5 Cerner Computer Assisted Coding
3.6 Platocode Computer-Assisted Coding
  3.6.1 Platocode ICD 10
  3.6.2 Platocode Solution For Ambulatory Surgery
  3.6.3 Platocode API
  3.6.4 Communication Between 3rd-Party Applications And A Platocode Server
3.7 Nuance Computer Assisted Coding
  3.7.1 Nuance Clinical Documentation Review
  3.7.2 Nuance Clinical Documentation Compliance
  3.7.3 Nuance Clintegrity Computer Assited Coding (CAC)
  3.7.4 Nuance Clintegrity Computer Assisted Coding (CAC) Key Features
  3.7.5 Nuance Clintegrity Facility Coding Solutions for Healthcare
  3.7.6 Nuance Clintegrity Facility Coding
  3.7.7 Nuance Clintegrity Computer Assisted Coding (CAC) Features
  3.7.8 Nuance Clintegrity Physician Coding
  3.7.9 Nuance Clinician Reimbursement Calculation
  3.7.10 Nuance Clintegrity Compliance & ICD-10 Transition
  3.7.11 Nuance Clintegrity Facility Coding
  3.7.12 Nuance Clintegrity Abstracting
  3.7.13 Nuance Clintegrity ICD-10 Education Services
  3.7.14 Nuance Automated Coding
  3.7.15 Nuance Natural Language Processing
  3.7.16 Nuance Natural Language Understanding
  3.7.17 Nuance Mapping and Modeling Disparate Controlled Medical Vocabularies (CMVs);
3.8 Artificial Medical Intelligence Emscribe CAC
  3.8.1 Artificial Medical Intelligence EMscribe Dynamic Search
  3.8.2 Artificial Medical Intelligence EMscribe Encoder
  3.8.3 AMI EMscribe Dynamic Medical Term And Coding Search Tool
  3.8.4 Artificial Medical Intelligence Autonomous Coding
  3.8.5 Artificial Medical Intelligence (AMI) EMscribe Dx
3.9 CodeCorrect
  3.9.1 CodeCorrect Capture Revenue and Maintain Compliance
  3.9.2 CodeCorrect knowledge
  3.9.3 CodeCorrect Medical Necessity Verification and APC Performance Tools
  3.9.4 QuadraMed
3.10 M*Modal Coding
  3.10.1 M*Modal Workflow
  3.10.2 M*Modal Management Tools
  3.10.3 M*Modal Single Platform
3.11 nThrive / MedAssets-Precyse and Equation
  3.11.1 Precyse Medical Coding and Computer Assisted Coding

4. COMPUTER ASSISTED CODING RESEARCH AND TECHNOLOGY

4.1 Computer-Assisted Coding Technology
4.2 Hybrid Technology
  4.2.1 Computer Assisted Coding Engine
4.3 Optum Computer Assisted Coding Technology
4.4 Preventable Medical Conditions
4.5 Natural Language Processing (NLP) Medical Coding
  4.5.1 Rules Based Approaches
  4.5.2 Reports Based On Statistics
  4.5.3 Normalize the Data
4.6 Reports Must Be In Some Kind Of Electronic Format
  4.6.1 NLP Software Statistical Analysis
  4.6.2 Workflow
  4.6.3 Feedback for Machine Learning
  4.6.4 Coding
  4.6.5 Accuracy And Specificity Of Retrieval
  4.6.6 Natural Language Programming (NLP) Vocabulary Processor
  4.6.7 Robust Underlying Terminological Model And A Component Architecture
4.7 TeSSI (Terminology Supported Semantic Indexing)
  4.7.1 L&C’s LinkBase Medical Ontology
  4.7.2 Semantic Indexing With The TeSSI Indexing Engine
  4.7.3 Semantic Indexing Solution Automates The Indexing Process
  4.7.4 Information Extraction with TeSSI Extraction Engine
  4.7.5 Semantic Search with TeSSI Search Engine

5. COMPUTER ASSISTED CODING COMPANY PROFILES

5.1 CAC Key Market Players
5.2 3M
  5.2.1 3M Business
  5.2.2 3M Health Care Segment
  5.2.3 3M Electronics and Energy Business
  5.2.4 3M Health Information Systems
5.3 Artificial Medical Intelligence
5.4 Cerner
  5.4.1 Cerner Business
  5.4.2 Cerner Acquired Siemens Health Services
  5.4.3 Cerner 2016 Fourth Quarter and Full-Year Highlights:
5.5 Craneware
5.6 Dolbey
5.7 EPIC
5.8 Group One / CodeCorrect
5.9 M*Modal
5.10 nThrive
  5.10.1 nThrive / Precyse
5.11 Nuance
  5.11.1 Nuance Healthcare
  5.11.2 Nuance Business Description
  5.11.3 Nuance Key Metrics
  5.11.4 Nuance Healthcare Trends
5.12 Quest Diagnostics
5.13 TruCode
5.14 UnitedHealth Group / Optum
  5.14.1 UnitedHealth Group / Optum
  5.14.2 UnitedHealth Group Optum Health Information Technology Acquires Clinical Data Analytics Vendor Humedica
  5.14.3 Optum Acquires Physician Practice Management And Revenue Management Software Firm, MedSynergies and Support Arm of ProHealth Physicians Group
  5.14.4 Optum MedSynergies Synergies
  5.14.5 Optum Life Sciences
  5.14.6 United Healthcare Revenue
5.15 Selected CAC Companies
Wintergreen Research,
WinterGreen Research Methodology
List of Figures
Figure 1. Computer Assisted Coding of Medical Information Market Driving Forces
Figure 2. Computer Assisted Coding of Medical Information Market Driving Factors
Figure 3. CAC Workstation Coder Benefits
Figure 4. CAC Management Tools
Figure 5. Electronic Coding Solution Market Driving Forces
Figure 6. Electronic Coding Product Issues
Figure 7. Computer Assisted Coding Software and Services Market Shares, Dollars, 2016
Figure 8. Computer Assisted Coding Market Forecast, Dollars, Worldwide, 2017-2024
Figure 9. Barriers to CAC
Figure 10. Electronic Coding Integrated Database Issues
Figure 11. Medical Necessity Online
Figure 12. Physician Office Computer Assisted Coding Key Benefits:
Figure 13. Natural Language Solutions System For Coding
Figure 14. Computerized Workflow System Systems Features
Figure 15. 3M Confidence Assessment Module System
Figure 16. Computer Assisted Coding of Medical Information Market Driving Forces
Figure 17. Computer Assisted Coding of Medical Information Market Driving Factors
Figure 18. CAC Workstation Coder Benefits
Figure 19. CAC Management Tools
Figure 20. Electronic Coding Solution Market Driving Forces
Figure 21. Electronic Coding Product Issues
Figure 22. Computer Assisted Coding Software and Services Market Shares, Dollars, 2016
Figure 23. Worldwide Computer Assisted Coding License Shipments and Cloud Services Market Shares, Dollars, 2016
Figure 24. 3M CAC Research Areas
Figure 25. 3M Core of NLP Computer Assisted Coding
Figure 26. Optum CAC Key Benefits:
Figure 27. Optum LifeCode, CAC Functions
Figure 28. Optum LifeCode, CAC Reconciliation Functions
Figure 29. Optum CAC Reconciliation Module Functions:
Figure 30. Platocode Computer Assisted Coding Solution Benefits
Figure 31. Computer Assisted Coding Market Forecast, Dollars, Worldwide, 2017-2024
Figure 32. Computer Assisted Coding CAC Market Forecasts, Dollars, 2017 to 2023
Figure 33. CAC Market Segments Software and Services Key Topics
Figure 34. Worldwide Computer Assisted Coding Hospitals and Facilities and Physicians Market Shares, Dollars, 2016
Figure 35. Worldwide Computer Assisted Coding: Large Teaching Hospitals, Mid-Size and Small Hospitals, and Clinical Facilities, Market Shares, Units, 2016
Figure 36. Worldwide Computer Assisted Coding, Hospitals and Facilities and Physicians, Market Shares, Units, 2016
Figure 37. Worldwide Computer Assisted Coding, License Shipments, Services, and Cloud Services
Figure 38. Worldwide and US Computer Assisted Coding Number of Large Physician Practices, CAC Installed Base, Market Forecasts, Number, 2017-2023
Figure 39. Worldwide and US Computer Assisted Coding Software and Cloud Services CAC Market Forecasts, Dollars, 2017-2023
Figure 40. Worldwide and US Computer Assisted Coding for Physicians CAC Market Forecasts, Dollars, 2017-2023
Figure 41. US Computer Assisted Coding Physician Software License / Maintenance and Cloud SaaS Services, CAC Market Forecasts, Dollars, 2017-2023
Figure 42. US Computer Assisted Coding Software License / Maintenance and Cloud SaaS Services CAC For Large Independent Physician Practices Market Forecasts, Dollars, 2017-2023
Figure 43. US Computer Assisted Coding Software Units Retired CAC For Large Independent Physician Practices Market Forecasts, 2017-2023
Figure 44. US Computer Assisted Coding Software Units Retired CAC For Large Independent Physician Practices Market Forecasts, 2017-2023
Figure 45. US Computer Assisted Coding Software and Cloud SaaS Services CAC For Independent Radiology Clinics Market Forecasts, Dollars, 2017-2023
Figure 46. US Computer Assisted Coding CAC Software License / Maintenance and Cloud SaaS Percent Penetration For Independent Radiology Clinics Market Forecasts, Dollars, 2017-2023
Figure 47. US Outpatient Procedures Forecasts, Number of Procedures, 2016-2023
Figure 48. US Computer Assisted Coding Outpatient Procedure Market Penetration Forecasts, % Penetration, 2017-2023
Figure 49. 3M APR DRG Software Functions: 123 2.5 Making The Shift To The Modern ICD-10 Requirements
Figure 50. Computer Assisted Coding Regional Market Segments Dollars, Worldwide, 2016
Figure 51. Computer Assisted Coding Regional Market Segments Dollars, Worldwide, 2016
Figure 52. 3M 360 Encompass 1,500 Hospitals And Healthcare User Organizations
Figure 53. 3M 360 Encompass 1,500 Hospitals And Healthcare User Organizations
Figure 54. 3M 3M CodeAssist System Features
Figure 55. 3M Codefinder Functions
Figure 56. 3M Codefinder Features
Figure 57. 3M Codefinder Intelligent Functions
Figure 58. 3M APR DRG Solutions Aspects
Figure 59. 3M Applications For Severity- And Risk-Adjusted Data
Figure 60. 3M Classification System
Figure 61. 3M APR DRG Software Features
Figure 62. 3M Medical Coding Tools
Figure 63. 3M DataScout Functions
Figure 64. 3M Clinical Information Extraction Functions
Figure 65. 3M Systems For Overcoming Documentation Shortfalls Focus
Figure 66. 3M Systems Topics
Figure 67. 3M Systems Metrics Included For Computerized Coding
Figure 68. 3M Web-Based Coding Software Return On Investment Metrics
Figure 69. 3M Web-Based Coding Software Key Benefits
Figure 70. 3M Coding Software Functions
Figure 71. 3M Coding Software Features
Figure 72. 3M Internet-Based Computer-Assisted Medical Coding Target Markets
Figure 73. 3M CodeAssist Functions
Figure 74. 3M CodeComplete Functions
Figure 75. Dolbey Fusion CAC Benefits
Figure 76. Dolbey Computer-Assisted Coding Solution Features
Figure 77. Dolby Computer Assisted Coding CAC Fusion Suite Modules
Figure 78. Optum Computer-Assisted Coding (CAC) Intelligent CAC Functions:
Figure 79. Optum Coding Service
Figure 80. Optum Medicare Fee Schedule
Figure 81. McKesson Practice Management Priorities
Figure 82. Cerner Discern nCode Functions
Figure 83. Cerner Discern nCode Key Features
Figure 84. Platocode Process
Figure 85. PlatoCode Code Set Filters
Figure 86. Nuance Personnel Impacted by Transition to ICD-10
Figure 87. ICD-10 Critical Business Concerns:
Figure 88. Nuance Clintegrity Facility Coding Healthcare Solutions Platform Functions
Figure 89. Nuance Clintegrity Facility Coding Healthcare Solution Functions
Figure 90. Nuance Clintegrity Computer Assisted Coding (CAC) Professional Fee Coding Functions
Figure 91. Nuance Clintegrity Clinician Coding Features
Figure 92. Nuance Clintegrity Facility Coding Features
Figure 93. Nuance Clintegrity Platform Modules
Figure 94. Nuance Clintegrity Platform, Modules Customizable Data Collection Fields Features218
Figure 95. Nuance Advantages Of Automating Coding With Clintegrity
Figure 96. Nuance Coding Algorithms
Figure 97. Nuance Mapping And Modeling Disparate Controlled Medical Vocabulary Functions223
Figure 98. Artificial Medical Intelligence EMscribe CAC
Figure 99. Artificial Medical Intelligence EMscribe Dynamic Search
Figure 100. Artificial Medical Intelligence (Ami) Emscribe Dx Benefits
Figure 101. Precyse Coding Services
Figure 102. PrecyseCode CAC Coding Functions
Figure 103. Computer Assisted Technology Functions
Figure 104. How ICD-9 and ICD-10 Are Different
Figure 105. ICD-9 and ICD-10 Different Formats
Figure 106. Semantic Indexing for Medical Ontology
Figure 107. Semantic Process Flow Index Component Medical Ontology
Figure 108. Information Extraction With Tessi Extraction Engine Functions
Figure 109. 3M 360 Encompass 1,500 Hospitals And Healthcare User Organizations
Figure 110. Current EPIC Computer Assisted Coding CAC Integrations


More Publications