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November 2018
The Future of RCM
in Healthcare
Organizations
White Paper
1
Historically, healthcare providers and hospitals have
designed their revenue cycle systems and processes
around B2B relationships, mostly involving payments
from government and commercial healthcare
insurance companies. However, with the shift in the
marketplace to consumer-centric healthcare and a
focus on high deductible health plans, providers are
struggling to efficiently handle the dramatic increase
in consumer payments, including co-pays, deductibles
and POS collections.
As today’s healthcare system becomes more value-
based, new technologies in healthcare need to be
utilized at scale. Organizations need to leverage
emerging technologies like Blockchain, Artificial
Intelligence and cloud computing to improve
performance and outcomes.
The figure illustrates various RCM processes involved
in healthcare organizations and technologies that can
be utilized effectively at clinical / front end and back
end functions. The technologies listed aren’t limited
and can be used simultaneously at each function
depending on the healthcare organizations’
technology adoption maturity and focus.
RCM IN HEALTHCARE: SHIFTING FOCUS FROM VOLUME TO VALUE
Contract
Management
Practice
Management
Systems
EHR
Billing &
Claims
Systems
Denial
Management
Systems
RCMSystemsEntities
 Payer
 Provider
 Provider
 Patient
 Contract
Management
 Pricing models
 Allowed
amount
 Payer-provider
collaboration
Processes
 Pre-
authorization
 Patient
Scheduling
 Patient
enrollment
 E&B enquiry
Cloud
Robotics Process
Automation (RPA)
Front end functions
 Provider
 Patient
 Hospital Visit
 Diagnosis
 Service charge
capture
 Charge
description
master
 Billing Center
 Clearing House
 Review charge
sheet
 Clean claim
submission
 Claim
adjudication
 Payment
posting
 Resubmission
 Patient
collections
 A/R mgmt.
(follow up on
outstandings)
 Appeals
 Big Data &
Analytics
 Interoperability
 Cloud
 Blockchain
 Artificial
Intelligence
 RPA
 Big Data
Back end functions
Clinical
functions
Technology
 Payer
 A/R Team
As healthcare technology companies build new RCM
technologies, it has become imperative to optimize
the technology environment for healthcare providers
and payers. Here are a few areas wherein technology
companies need to forge new partnerships to reduce
the span of the entire RCM cycle and optimize their
financial performance:
1. Blockchain
Blockchain technology has potential to accelerate
payment accuracy and revenue velocity, which will
benefit payers, providers, and consumers themselves.
Example: In the US alone, Medicare fraud in 2017-18
led to a $30 million loss. Blockchain can provide
powerful solutions to minimize medical billing related
frauds. Blockchain contains an inbuilt identity
mechanism – a cryptographically secure public-private
key pair – used to associate activity on the network
with a specific participant. This would ensure each
penny paid to Medicare is tracked.
By automating the majority of claim adjudication and
payment processing activities, blockchain systems
could help eliminate the traditional clearinghouse and
reconciliation layers.
This is done with the help of “smart contracts”
(pieces of executable computer code representing
business logic). A blockchain based payer network
transfers the provider data into smart contracts. A
payer must verify that a reimbursement request came
from a credentialed physician and the care delivered is
reimbursable.
Smart contracts help determine whether the patient
falls under payer’s contract and automate claims
processing within unimaginable time frame.
2. Complement Existing Workflows
with RPA (Robotics Process
Automation)
RPA involves automated software robots that enable
the creation of a digital workforce to drive greater
efficiency. RPA can help complement current RCM
IMPLICATIONS FOR HEALTHCARE TECHNOLOGY COMPANIES
2
3
processes to increase operational efficiency and
minimize human errors in various processes.
For example: With RPA, health insurance eligibility
check can be automated with the retrieval of coverage
data from insurer sites, catching potential eligibility
issues early in the revenue cycle.
If a practice needs to rework 500 claims per month, it
would require more FTEs who can work a day on new
claims. This could be automated by RPA scripts
resulting in rejection, automated payer calling for
details, determining coding mistakes, refiling the claim
or filing an appeal.
RPA software script can be maintained to update
outstanding collections sheets for the Revenue
Department. This would allow the patients’ account
representative to get an updated status of each
account to work on that day and follow up with the
patients. This would result in a huge acceleration of
revenue generation.
Like an automated employee, RPA can assist in various
processes like patient enrolment and eligibility check /
medical coding errors / Denials recovery follow-ups /
accelerate account settlements etc.
3. Big Data and Analytics to solve
Revenue Leakage
The primary objective of any RCM company is to
augment the billing process in such a way that a
healthcare provider can run a profitable practice.
Registration data / clinical data / financial data and
claims data are high in volume and require extensive
mining of data. As per HFMA, out of the 1.4 billion
claims submitted annually, 200 million are denied (1
out of every 7) which costs around $5-10 Million each
year.
Big data technologies can be used for RCM data
integration, management and processing. Denial rates
can be reduced or even eliminated through RCM big
data analytics.
For example: big data analytics can provide detailed
insights on specific patterns that cause denials, and
distribute action to the respective division group. Also,
the manual turnaround time to process and route one
claim to the correct division takes two business days,
while turnaround time for an entire automated process
(deploying analytic capabilities to pinpointing
potential issues near real time) takes less than 24
hours.
4. Disruptive Patient Access Systems
(PAS)
Effectively managing patient data is one of the
costliest tasks modern RCM practices must perform.
While some healthcare organizations see the initial
costs as prohibitive, organizations that are adopting
disruptive technology find it to be an investment in
greater efficiency.
With HDHPs (High Deductible Health Plans), patients
are now responsible for their bills. This calls for new
collection strategies. Statistics indicate that only 32%
of patients who owe money receive a collection letter
from the practice. Effective Patient Access Systems
should provide features such as patient liability
estimator tools and credit scoring technology to
determine liability in advance. The financial clearing
process can be optimized by automating the process
of calculating a patient’s liability using updated CDM
(Charge Description Master) files and current patient
benefit accumulators. This would help PAS staff in
efficient collections at pre-service and POS collections.
5. Artificial Intelligence
Healthcare data when coupled with efficient
algorithms can provide deep insights and knowledge
around improving various RCM processes.
Machine Learning and Natural Language Processing
(NLP) algorithms can be applied to applications such
as intelligent alerting of denial risk, medical coding
error detection, claims administration, accounts
receivable, denial recovery, etc.
4
For example: Manual prior- authorization processes
generally take 15-20 minutes to complete. Machine
Learning can input huge (although not all) clinical data
in the request without any human assistance. This
makes it possible to build an efficient input
management system that avoids manual work and
learns from previous events. This new kind of input
management system can be further divided into three
steps:
a. Data analysis
b. Data clustering, and
c. Routing
While there is still some manual clinical data input
involved, the time taken to make an authorization
request can be reduced by 50% of more.
Medical coding errors can be reduced significantly by
automating the coding process. Medical codes should
map to a physician’s consultation against a multilevel
medical reference dictionary. NLP processes enable
extraction of accurate code and significantly reduce
cost in coding.
Conclusion
According to Harvard Business Review, the healthcare
industry accounts for 30% of all electronic data
storage in the world. A large part of this is in the form
of unstructured data filled with key medical facts and
information.
RCM companies have invested in setting up the
foundational building blocks of analytics. Emerging
technologies, coupled with advances in computing
power, offer the next step for organizations to derive
insights and improve financial and operational
performance.
5
efficacy and safety of product is paramount. The
journey of the product from manufacturer to
consumer has multiple stages: transportation,
handling, storage, redistribution, retail. Things can go
wrong during these stages, from simple human error
to malicious intent (fraud). In a conventional system, it
can get difficult to identify the problem as participants
in the supply chain may usually maintain their own
records and may share their information usually just
one level up and one level down the chain.
Furthermore, if these records are paper-based, they
are more prone to errors. These factors further delay
investigations to identify the issues with drug supply
chain.
Additionally, with governments and consumers
demanding transparency in the supply chain for such
products, companies have worked towards
maintaining an open or transparent supply chain
system. Depending on one organization to track
irregularities still isn't fool proof.
 https://cdn2.hubspot.net/hubfs/2233523/White%20Papers/RPA%20Accelerates%20Healthcare%20Collections%20&
%20RCM_FINAL.pdf?t=1504875650204
 https://www.webpt.com/blog/post/8-ways-to-optimize-patient-collections
 https://www2.deloitte.com/content/dam/Deloitte/de/Documents/Innovation/Artificial-Intelligence-in-Insurance-
Whitepaper-deloitte-digital.pdf
 https://insidebigdata.com/2018/03/05/himss-2018-perspectives-health-industry-use-ai-machine-learning/
 https://www.kofax.com/-/media/Files/Solution-Overview/EN/so_robotic-process-automation-for-healthcare_en.pdf
REFERENCES
6
Seema Nagwani
Healthcare Business Analyst, CitiusTech
seema.nagwani@citiustech.com
Seema has 6+ years of experience in the healthcare IT industry. She has strong experience across processes related to
provider and payer domains, including Health Information Exchange, Revenue Cycle Management, EDI transactions,
MU, ACO measures and healthcare interoperability standards like HL7. Seema has worked on several healthcare
projects for leading healthcare organizations. She holds a Master’s degree in Business Administration.
ABOUT THE AUTHOR
CitiusTech is a specialist provider of healthcare technology services and
solutions to healthcare technology companies, providers, payers and life
sciences organizations. With over 3,200 professionals worldwide,
CitiusTech enables healthcare organizations to drive clinical value chain
excellence - across integration & interoperability, data management
(EDW, Big Data), performance management (BI / analytics), predictive
analytics & data science and digital engagement (mobile, IoT).
CitiusTech helps customers accelerate innovation in healthcare through
specialized solutions, healthcare technology platforms, proficiencies and
accelerators. With cutting-edge technology expertise, world-class service
quality and a global resource base, CitiusTech consistently delivers best-
in-class solutions and an unmatched cost advantage to healthcare
organizations worldwide.
For queries contact thoughtleaders@citiustech.com
Copyright © CitiusTech 2018. All Rights Reserved.

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The Future of RCM in Healthcare Organizations

  • 1. November 2018 The Future of RCM in Healthcare Organizations White Paper
  • 2. 1 Historically, healthcare providers and hospitals have designed their revenue cycle systems and processes around B2B relationships, mostly involving payments from government and commercial healthcare insurance companies. However, with the shift in the marketplace to consumer-centric healthcare and a focus on high deductible health plans, providers are struggling to efficiently handle the dramatic increase in consumer payments, including co-pays, deductibles and POS collections. As today’s healthcare system becomes more value- based, new technologies in healthcare need to be utilized at scale. Organizations need to leverage emerging technologies like Blockchain, Artificial Intelligence and cloud computing to improve performance and outcomes. The figure illustrates various RCM processes involved in healthcare organizations and technologies that can be utilized effectively at clinical / front end and back end functions. The technologies listed aren’t limited and can be used simultaneously at each function depending on the healthcare organizations’ technology adoption maturity and focus. RCM IN HEALTHCARE: SHIFTING FOCUS FROM VOLUME TO VALUE Contract Management Practice Management Systems EHR Billing & Claims Systems Denial Management Systems RCMSystemsEntities  Payer  Provider  Provider  Patient  Contract Management  Pricing models  Allowed amount  Payer-provider collaboration Processes  Pre- authorization  Patient Scheduling  Patient enrollment  E&B enquiry Cloud Robotics Process Automation (RPA) Front end functions  Provider  Patient  Hospital Visit  Diagnosis  Service charge capture  Charge description master  Billing Center  Clearing House  Review charge sheet  Clean claim submission  Claim adjudication  Payment posting  Resubmission  Patient collections  A/R mgmt. (follow up on outstandings)  Appeals  Big Data & Analytics  Interoperability  Cloud  Blockchain  Artificial Intelligence  RPA  Big Data Back end functions Clinical functions Technology  Payer  A/R Team
  • 3. As healthcare technology companies build new RCM technologies, it has become imperative to optimize the technology environment for healthcare providers and payers. Here are a few areas wherein technology companies need to forge new partnerships to reduce the span of the entire RCM cycle and optimize their financial performance: 1. Blockchain Blockchain technology has potential to accelerate payment accuracy and revenue velocity, which will benefit payers, providers, and consumers themselves. Example: In the US alone, Medicare fraud in 2017-18 led to a $30 million loss. Blockchain can provide powerful solutions to minimize medical billing related frauds. Blockchain contains an inbuilt identity mechanism – a cryptographically secure public-private key pair – used to associate activity on the network with a specific participant. This would ensure each penny paid to Medicare is tracked. By automating the majority of claim adjudication and payment processing activities, blockchain systems could help eliminate the traditional clearinghouse and reconciliation layers. This is done with the help of “smart contracts” (pieces of executable computer code representing business logic). A blockchain based payer network transfers the provider data into smart contracts. A payer must verify that a reimbursement request came from a credentialed physician and the care delivered is reimbursable. Smart contracts help determine whether the patient falls under payer’s contract and automate claims processing within unimaginable time frame. 2. Complement Existing Workflows with RPA (Robotics Process Automation) RPA involves automated software robots that enable the creation of a digital workforce to drive greater efficiency. RPA can help complement current RCM IMPLICATIONS FOR HEALTHCARE TECHNOLOGY COMPANIES 2
  • 4. 3 processes to increase operational efficiency and minimize human errors in various processes. For example: With RPA, health insurance eligibility check can be automated with the retrieval of coverage data from insurer sites, catching potential eligibility issues early in the revenue cycle. If a practice needs to rework 500 claims per month, it would require more FTEs who can work a day on new claims. This could be automated by RPA scripts resulting in rejection, automated payer calling for details, determining coding mistakes, refiling the claim or filing an appeal. RPA software script can be maintained to update outstanding collections sheets for the Revenue Department. This would allow the patients’ account representative to get an updated status of each account to work on that day and follow up with the patients. This would result in a huge acceleration of revenue generation. Like an automated employee, RPA can assist in various processes like patient enrolment and eligibility check / medical coding errors / Denials recovery follow-ups / accelerate account settlements etc. 3. Big Data and Analytics to solve Revenue Leakage The primary objective of any RCM company is to augment the billing process in such a way that a healthcare provider can run a profitable practice. Registration data / clinical data / financial data and claims data are high in volume and require extensive mining of data. As per HFMA, out of the 1.4 billion claims submitted annually, 200 million are denied (1 out of every 7) which costs around $5-10 Million each year. Big data technologies can be used for RCM data integration, management and processing. Denial rates can be reduced or even eliminated through RCM big data analytics.
  • 5. For example: big data analytics can provide detailed insights on specific patterns that cause denials, and distribute action to the respective division group. Also, the manual turnaround time to process and route one claim to the correct division takes two business days, while turnaround time for an entire automated process (deploying analytic capabilities to pinpointing potential issues near real time) takes less than 24 hours. 4. Disruptive Patient Access Systems (PAS) Effectively managing patient data is one of the costliest tasks modern RCM practices must perform. While some healthcare organizations see the initial costs as prohibitive, organizations that are adopting disruptive technology find it to be an investment in greater efficiency. With HDHPs (High Deductible Health Plans), patients are now responsible for their bills. This calls for new collection strategies. Statistics indicate that only 32% of patients who owe money receive a collection letter from the practice. Effective Patient Access Systems should provide features such as patient liability estimator tools and credit scoring technology to determine liability in advance. The financial clearing process can be optimized by automating the process of calculating a patient’s liability using updated CDM (Charge Description Master) files and current patient benefit accumulators. This would help PAS staff in efficient collections at pre-service and POS collections. 5. Artificial Intelligence Healthcare data when coupled with efficient algorithms can provide deep insights and knowledge around improving various RCM processes. Machine Learning and Natural Language Processing (NLP) algorithms can be applied to applications such as intelligent alerting of denial risk, medical coding error detection, claims administration, accounts receivable, denial recovery, etc. 4
  • 6. For example: Manual prior- authorization processes generally take 15-20 minutes to complete. Machine Learning can input huge (although not all) clinical data in the request without any human assistance. This makes it possible to build an efficient input management system that avoids manual work and learns from previous events. This new kind of input management system can be further divided into three steps: a. Data analysis b. Data clustering, and c. Routing While there is still some manual clinical data input involved, the time taken to make an authorization request can be reduced by 50% of more. Medical coding errors can be reduced significantly by automating the coding process. Medical codes should map to a physician’s consultation against a multilevel medical reference dictionary. NLP processes enable extraction of accurate code and significantly reduce cost in coding. Conclusion According to Harvard Business Review, the healthcare industry accounts for 30% of all electronic data storage in the world. A large part of this is in the form of unstructured data filled with key medical facts and information. RCM companies have invested in setting up the foundational building blocks of analytics. Emerging technologies, coupled with advances in computing power, offer the next step for organizations to derive insights and improve financial and operational performance. 5
  • 7. efficacy and safety of product is paramount. The journey of the product from manufacturer to consumer has multiple stages: transportation, handling, storage, redistribution, retail. Things can go wrong during these stages, from simple human error to malicious intent (fraud). In a conventional system, it can get difficult to identify the problem as participants in the supply chain may usually maintain their own records and may share their information usually just one level up and one level down the chain. Furthermore, if these records are paper-based, they are more prone to errors. These factors further delay investigations to identify the issues with drug supply chain. Additionally, with governments and consumers demanding transparency in the supply chain for such products, companies have worked towards maintaining an open or transparent supply chain system. Depending on one organization to track irregularities still isn't fool proof.  https://cdn2.hubspot.net/hubfs/2233523/White%20Papers/RPA%20Accelerates%20Healthcare%20Collections%20& %20RCM_FINAL.pdf?t=1504875650204  https://www.webpt.com/blog/post/8-ways-to-optimize-patient-collections  https://www2.deloitte.com/content/dam/Deloitte/de/Documents/Innovation/Artificial-Intelligence-in-Insurance- Whitepaper-deloitte-digital.pdf  https://insidebigdata.com/2018/03/05/himss-2018-perspectives-health-industry-use-ai-machine-learning/  https://www.kofax.com/-/media/Files/Solution-Overview/EN/so_robotic-process-automation-for-healthcare_en.pdf REFERENCES 6 Seema Nagwani Healthcare Business Analyst, CitiusTech seema.nagwani@citiustech.com Seema has 6+ years of experience in the healthcare IT industry. She has strong experience across processes related to provider and payer domains, including Health Information Exchange, Revenue Cycle Management, EDI transactions, MU, ACO measures and healthcare interoperability standards like HL7. Seema has worked on several healthcare projects for leading healthcare organizations. She holds a Master’s degree in Business Administration. ABOUT THE AUTHOR
  • 8. CitiusTech is a specialist provider of healthcare technology services and solutions to healthcare technology companies, providers, payers and life sciences organizations. With over 3,200 professionals worldwide, CitiusTech enables healthcare organizations to drive clinical value chain excellence - across integration & interoperability, data management (EDW, Big Data), performance management (BI / analytics), predictive analytics & data science and digital engagement (mobile, IoT). CitiusTech helps customers accelerate innovation in healthcare through specialized solutions, healthcare technology platforms, proficiencies and accelerators. With cutting-edge technology expertise, world-class service quality and a global resource base, CitiusTech consistently delivers best- in-class solutions and an unmatched cost advantage to healthcare organizations worldwide. For queries contact thoughtleaders@citiustech.com Copyright © CitiusTech 2018. All Rights Reserved.