The AI-Powered Healthcare Transformation

WHITEPAPER

Healthcare has been in the throes of a transformation. From the beginning, it has been procedure driven in a fee-for-service model. In the US, change came in with the HIPAA act to protect privacy. Adoption of value-based care tied payments to quality of care. More recently, the ARRA with its incentives boosted EHR adoption. Automation finally arrived in American healthcare. Basic EHR adoption went up from 9.4% in 2008 to 83.8%1 in 2015. (JaWanna Henry, Yuriy Pylypchuk, Talisha Searcy, & Vaishali Patel, 2016). Efficiency and safety of healthcare went up.

But challenges soon arose. By increasing clerical data entry (Physicians spend 34% to 55% of their workday in creating notes and reviewing medical records in the EHR2 ), they took away from valuable face time with patients, causing clinician burnout and pushing down quality of care. Most EHRs were found to serve frontline users poorly3. To add, they also increased financial burden. Plus, interoperability issues created silos. Another consequence was a dramatic increase in amount of data being managed, expected to grow to 25,000 petabytes by 20204. Adding to data growth are the home devices sending data directly to EHR. Some providers built their own custom EHRs but these were difficult, time-consuming, and expensive initiatives, not practical for the largest providers. Also arose the need to increase cybersecurity standards as ransomware attacks became more prevalent.

Enter AI to make current EHR systems more flexible and intelligent. From extracting data from clinical notes to predictive models to clinical decision support, AI is helping to overcome the hurdles implicit in EHR through data discovery and abstraction and bring in personalized care. Besides standalone AI systems, the bigger EHR players are incorporating AI into their solutions.

Most data in EHRs is unstructured data like physician notes. This free text format is not directly suitable for data analytics. Then there are the data entry procedures that cause physician angst. AI is stepping in to uncover insight from both structured and unstructured data for care delivery and the revenue cycle.

Clearly, the EHR-based automation has had significant drawbacks. A different approach to automation has become necessary, AI-powered Intelligent Automation. This has several components:

RPA to automate repetitive tasks that are entered via the UI.

NLP to extract insight from unstructured data and free text.

Speech recognition to support the creation of notes in real time.

AI to integrate clinical information and provide real-time recommendations.

AI to optimize the coding and billing process for faster revenue cycle management.

AI and analytics are enabling smarter financial workflows to prioritize claims, predict outcomes, and accelerate cash flows. AI/ML models are being used in precision medicine. The move to value-based care has increased the number of variables in payor reimbursement many-fold. Machine learning and natural language processing can lower administrative complexity of the health care system. Specifically, within the revenue cycle, NLP can improve coding, step up clinical documentation, and free up clinicians to focus on care rather than administrative tasks.

Today, AI and Analytics are transforming the end-to-end value network, as illustrated below, across Healthcare and Life Sciences. A truly personalized patient centric paradigm is emerging in the healthcare landscape, driven by data, automation, and AI/Analytics.

1https://dashboard.healthit.gov/evaluations/data-briefs/non-federal-acute-care-hospital-ehr-adoption-2008-2015.php2https://www.mayoclinicproceedings.org/article/S0025-6196(18)30142-3/pdf3https://jamanetwork.com/journals/jama/article-abstract/2666717?resultClick=14https://www.beckershospitalreview.com/healthcare-information-technology/taming-the-beast-how-to-best-manage-ehr-data-growth.html

The AI-Powered Healthcare Transformation

1Cognitive Platform

PAYOR PROVIDER

EHR

Life Sciences

Cyber Security And DevOps Best Practices In Compliance To HIPAA And HITRUST

Supply Network

PATIENT

Hospital

Medical Practice

s

Insurer

Pharmacy

The Ai-Powered Healthcare Transforma�on

Some resulting examples of value realization across the patient journey are listed below.

Patient

Modulating treatment with data from patient monitoring devices

Selecting the right providers and personalizing care

Provider

Applying precision medicine, optimizing and accelerating outcomes, improving patient experience

Payor

Identifying fraudulent claims

Predicting/identifying patients that are likely to have payment challenges, driving prescriptive options for these patients, and recommending alternative sources for payments

Developing cost-effective treatments for patients

Pharmaceutical

Tailoring treatments to individual patient genomes, proteomes, and metabolic attributes

Exploratory drug research discovery, clinical trials management, manufacturing, and sales & marketing directly to consumers

With the increased pace of digital adoption, there is paramount concern regarding the below:

Cybersecurity risks – from attack vectors and ransomware that target internal assets of providers and payers.

Data security risks – ensuring privacy of patient data, especially with data sharing between providers and payors.

The increase in volume, variety, and velocity of data is putting the spotlight on patient privacy. If patient data is not fully secured, identity theft, fraudulent claims, and incorrect treatment recommendations may soon follow.

To mitigate these risks, training models using differential privacy and federated learning constructs from private data helps protect them from being compromised and ensures that the confines of adoption are controlled. In addition, running the models on secure infrastructure further enables patient privacy, while delivering precision medicine.

A patient centric value network is at hand. The gains to players across the chain represent many orders of magnitude improvements in the system, risk is not just a checkbox to be ticked off but needs to be methodically applied at each stage of the digital journey. The key is to balance risk with digital adoption to seize the transformation that lies ahead.

To sum up, the end goal of digital transformation in healthcare is improving patient experience through models like value-based care while optimizing financial outcomes. The adoption of the model varies across payors to providers. Intelligent automation is emerging as a key enabler to promote value-based care. Using AI to automate basic tasks in operations and administration and integrating AI/Analytics into the workflow results in better patient experience, higher quality of service, and lower costs. Conversational interfaces are providing both chatbots and voice enabled interfaces to make the user interface even more seamless. Best practices around cloud infrastructure and DevOps are helping healthcare players stay ahead of security risks. As a result, clinical, operational, and financial KPIs are getting closely aligned with the healthcare organization’s strategic imperatives.

The AI-Powered Healthcare Transformation

Business Value Realization Framework

REVENUE

ClinicalPerformance

OperationalPerformance

FinancialPerformance

OPERATIONS ASSETS ORGANIZATION

Improved clinical quality of careImproved patient safety and reduced medical errorsImproved wellness and disease managementImproved patient acquisition, satisfaction, and retentionImproved physican/provider network management

Reduced operational costsIncreased operational effectiveness and efficiencyImproved inventory management and reduced leakageImproved provider pay for performance accountabilityIncreased operational speed and agility

Improved revenueImproved ROIImproved utilizationOptimized supply chain and HR costsImproved risk management and regulatory compliance/reduced finesReduced fraud and abuse leakage

Strategic Imperatives

2

The Ai-Powered Healthcare Transforma�onThe AI-Powered Healthcare Transformation

AI/ML Best Practices for Healthcare and Life Sciences

As you embark on your disruption, calibrate your progress on the roadmap to your strategic imperatives by capturing the target business value through KPI’s and aligning the KPIs to your technology decisions to clarify your investments.

As a best practice, adopt a Business Value Realization Framework, illustrated below, contextualizing the KPIs and the AI/ML Analytics Journey Map, providing visibility to your architecture decisions to analyze the impact of your financial investments, resulting in improved patient experience with cost optimizations and revenue enablement.

3

Content ServicesCognitive Platform

Weather data CDC data Epidemics

Data Ingestion

Data Preparation

Data Selection

Flu heat map

Increase value based care

GovernanceComplianceRisk ManagementReal Options Analysis

ContainerizationServePerformance ManagementNon-functional RequirementsTCO InfrastructureOptimizationAIOps

Train

Test

Validate

Versioning

Exploration

Cleansing

Inference

Transformation

Time Series forecasting

Feature Engineering

Feature Selection

Model Selection

Algorithm Selection

Hyperparameter Tuning

Versioning

Ensemble

Stacking

ROC / AUC

Precision Recall

Scoring

Explainability

Interpretability

Differential Privacy

Federated Learning

Calibration

Operational

Model Creation

Problem Comprehension

Customer Journey MapBusiness Model CanvasCritical Success FactorsPersonasKPI’s

1

23

4

5

2a

2b

2c

Data Curation

Pipeline

DataOps

MLOps

Problem Comprehension Data Curation Model Creation Operational Calibration

Marlabs Inc.(Global Headquarters)One Corporate Place South, 3rd FloorPiscataway, NJ - 08854-6116

Tel: +1 (732) 694 1000 Fax: +1 (732) 465 0100 Email: contact@marlabs.com

The AI-Powered Healthcare Transformation

Authors

Sanjay is a global technology leader with demonstrated experience in leading innovation centre of excellence, analytics, automation, cloud, cyber security, data, salesforce practices, engineering teams, and driving transformational initiatives in the digital economy, enabling revenue growth and achieving operational excellence while aligning to target operating models.

Sanjay has a rich experience in healthcare, life sciences, pharma, and telecom. He has expertise in specializing global financial services including asset management, commercial banking, corporate trust, mortgage, ratings agency, retail banking, treasury, and wealth management. chatbot(s), customer experience improvements, offers and campaign management, intergeneration wealth transfer, payments gateway, personalization, robo advisor(s), and robotic process automation has been his other areas of interest.

Sanjay also plays a role in providing thought leadership to C-level, senior management teams, start-up community, and partner ecosystems in developing enterprise strategies for data analytics and platform modernization, consisting of: API management and microservices, data analytics, data lake migration, cloud migration, and enterprise content management.

Raj has extensive experience in IT services across Digital Marketing, Alliances, Sales, Project Management, and BPM Consulting. He excels when it comes to distilling the value proposition of complex tech offerings: cutting through the "bits and bytes" details, developing creative messaging, and executing an integrated go-to-market plan. Raj’s experience spans Blockchain, AI/Analytics, Networks, Cloud, Cybersecurity, e-learning, BPM, and ERP technology areas. At Marlabs, Raj is responsible for marketing management, lead generation/inside sales, and sales support.

Sanjay B. BhaktaVice President Global Head Enterprise Solutions at Marlabs Inc.

Rajendra MenonHead of NA Marketing, Marlabs