3 considerations healthcare providers must make when using consumer data for patient care

by Experian Health 5 min read August 27, 2019

Most healthcare consumers spend only a tiny fraction of their lives in the clinical world of medical appointments and procedures. Where and how they spend the rest of their time has a far bigger impact on their health and well-being. So why are some providers still relying primarily on clinical data to devise their care plans?

Clinical data is crucial when it comes to a patient’s diagnosis and treatment options, but it tells you nothing about their ability to stick to a care plan when they get home. How do their living situation and lifestyle habits play into the physician’s treatment recommendations?

Consumer data is the missing piece of the healthcare jigsaw. When providers have insights into their patients’ social and economic circumstances, they’re better placed to spot the factors that might hinder access to care, and offer a more holistic, tailored and effective support plan.

The predictive power of consumer data

Let’s imagine a single mom of two small kids, working two jobs. Her daily life is a race to get everything done on time, give her children what they need and still make ends meet within her weekly budget. When a reminder for her annual wellness appointment flashes up on her phone, she adds it to her mental to-do list. But by the time the appointment comes around, the stress of taking time off work and scraping together the cash for gas or bus fare means she puts it off. She doesn’t go.

Six months later, she ends up in the emergency room with symptoms of a serious illness. Had her provider known about the barriers in advance, they could have supported her to get to her appointment and discover her illness sooner.

As Dr. David Berg, co-founder of Redirect Health says, “the most important part of getting good results is not the knowledge of the doctors, not the treatment, not the drug. It’s the logistics, the social support, the ability to arrange babysitting.”

Consumer data, such as car ownership, employment status, income level and family information can give you these insights early enough to take action. You’ll know whether your patients can get to their appointments easily, whether they can afford childcare, and a whole host of other factors that might affect their ability to stick to a care plan. And once you know those things, you can offer tailored support to give them the best chance of success.

How to gather non-clinical insights

According to PwC, around 78% of providers lack the data to identify patients’ social needs. Many have basic demographic information on their patient populations, but are missing the more sophisticated insights that could help them better support patients.

It doesn’t have to be complicated, but there are a few considerations healthcare providers should vet as they gather and use consumer data to help drive care plan compliance:

  1. Evaluate the pros and cons of patient surveys

The obvious way to find out more about your patients’ needs is to ask them directly. A survey at the point of registration can help you understand what barriers may prevent them from attending appointments, taking prescriptions or following other medical advice.

However, surveys can be time-consuming and expensive to administer, and recording answers by hand can lead to errors. How a patient interprets the questions and how your team interprets the answers may affect the usefulness of the survey data. And a patient’s circumstances may change between completing the survey and trying to follow the care plan.

This approach also only includes patients who manage to attend an appointment in the first place. Those without access to care such as the mom in the example above, would be omitted from the survey, so you would miss out on discovering how to help them.

  1. Tap data vendors to deepen your consumer insights

A third-party data vendor can give you access to data on your patient population’s income, occupations, length of residence and other social and economic circumstances. When this data is packaged up for your care managers, it can be used to inform proactive, preventative conversations with your patients, to solve any non-clinical gaps in care. It’s more cost-effective than patient surveys and removes the risk of personal bias and interpretation.

Ensuring the reliability and integrity of your data vendor can be a challenge. Data brokers often use consumer data collected in retail and other industries, which may not be completely relevant to your activities or collected in a way that meets the requirements for use in healthcare settings. It’s crucial to be able to verify the source of the data and confirm that individuals were told how their data would be used and given the choice to opt out. Always ask your vendor if they are an “original source compiler.”

Working with a data vendor in the health space, such as Experian Health, can help avoid these pitfalls, as they will have expertise in the appropriate use of consumer data in healthcare.

  1. Understand permissible use of consumer data to stay compliant

To use consumer data successfully, you must have confidence in both its accuracy and your ability to safeguard patient privacy. For example, are your data collection processes compliant with the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act 2018 (CCPA)?

Working with a data management partner who collects data directly from consumers means you can verify that all privacy requirements and opt-outs are in place. They’ll also help you scrutinize hundreds of public and proprietary data sources, so you use only the most relevant, up-to-date data to inform your decision-making.

By evaluating and understanding these three areas, you’ll be able to leverage consumer data to tailor your patient engagement and support and make it easier for your patients to comply with their care plan. The more you are able to see and treat each patient as a whole, individual person, the better their health outcomes are likely to be. Consumer data lets you do that.

Related Posts

Andy’s New WP Workflow Test Article Using Quick Edit

Key takeaways: Revenue cycle teams can use automation to reduce repetitive work and apply AI where data-driven prediction, matching or prioritization can improve a workflow. Experian Health’s 2025 State of Claims survey found that 41% of providers now face denial rates of 10% or higher, while 68% say submitting clean claims is more challenging than a year ago. Patient Access Curator™ (PAC) uses AI to support front-end data validation and insurance discovery, while AI Advantage™ helps teams predict denial risk and prioritize denial follow-up. Artificial intelligence (AI) and automation can support administrative work in healthcare. In the revenue cycle, teams depend on accurate information, timely decisions and efficient follow-up to keep claims moving. In revenue cycle management, AI and automation can help organizations reduce manual checks, find data gaps, predict denial risk and prioritize work queues. These tools are most useful when they support staff judgment, payer expertise and compliance oversight. They can handle repetitive, data-heavy tasks so staff can focus on exceptions and decisions that need human review. In 2023, McKinsey & Company reported that research suggests effectively deploying automation and analytics could eliminate $200 billion to $360 billion of spending in U.S. healthcare. For revenue cycle leaders, the practical question is where to apply those capabilities first. The case for applying AI and automation in healthcare Revenue cycle teams juggle many daily tasks. Staff collect and verify patient information, confirm eligibility, identify the right payer, submit clean claims, monitor status, work denials and manage collections. Small data gaps at the beginning of the process can create downstream rework and delays. Rework also consumes staff time, adding to these operational pressures. As costs rise and revenue cycles tighten, there is increasing pressure to do more with less. Experian Health’s 2025 State of Claims survey found that 54% of providers say claim errors are increasing and 90% of claim denials are reworked with at least some human review before resubmission. Providers are also managing broader financial and administrative pressures. The American Hospital Association has reported that prior authorization requirements, claim audits, denials and other payer policies add administrative burden and cost for hospitals and health systems. These requirements also consume staff time to appeal denials and manage payer processes. AI and automation are different but complementary. Automation follows defined rules to complete repeatable work. AI models can identify patterns in data, predict risk and help teams decide where to focus attention. When used together, they can support more consistent revenue cycle workflows. How AI and automation can support revenue cycle workflows Revenue cycle management automation and AI are most useful when tied to a specific workflow and a measurable operational problem. The goal is to help teams act earlier, reduce avoidable rework and focus staff time where judgment is needed most. For example, automation can complete rule-based eligibility checks. AI can help identify claims with a higher likelihood of denial. In insurance discovery workflows, AI can also help identify coverage that wasn’t captured at registration. When these tools fit into existing workflows, they can support more consistent decisions and reduce manual work. Three practical applications include: 1. Improving front-end data quality with Patient Access Curator Patient and coverage information collected early in the revenue cycle can affect downstream claim outcomes. Incomplete or outdated demographic details, eligibility responses, coordination of benefits or Medicare Beneficiary Identifier information can create problems that lead to claim delays or denials later in the cycle. Experian Health’s Patient Access Curator helps prevent claim denials by validating demographics, eligibility, insurance discovery, coordination of benefits and Medicare Beneficiary Identifier data in seconds. PAC’s AI and machine learning capabilities help improve match accuracy, coverage sequencing and data confidence by writing the validated data back into the host system and sequencing payers before the claim is created. This automates work that would otherwise require manual coverage checks. 2. Using insurance discovery to find coverage not captured at registration When active coverage isn’t identified during registration, claims may be delayed or submitted with incomplete insurance information. Insurance discovery looks for coverage that may not have been captured during registration. Patient Access Curator includes insurance discovery as part of its front-end validation workflow. It can help identify and correct missing or incorrect insurance information so claims can be submitted with more complete coverage data. 3. Using AI to prevent and prioritize denials Even with strong front-end processes, some claims still require additional attention. AI can help claims teams decide which claims to review before submission and which denials to work first after payer response. Experian Health’s AI Advantage supports two denial management use cases:1. AI Advantage – Predictive Denials uses a client’s historical claims data and Experian’s knowledge of payer rules to identify claims with a high likelihood of denial before submission so teams can take corrective action.2. AI Advantage – Denial Triage uses AI to segment denials and identify those with the highest potential for reimbursement. This approach can help teams prioritize with more confidence. Rather than treating every claim or denial the same way, teams can use predictive models to focus on the work that needs the most attention. Potential benefits of AI and automation in the revenue cycle A high-performing revenue cycle depends on timely, accurate and consistent work. AI and automation can help providers modernize that work without losing the expertise of the people who manage complex payer and patient situations every day. When applied to the right workflows, these tools can help organizations: Reduce manual data searches that take staff away from higher-value work Improve front-end data quality before claims are created Identify missing or incorrect coverage information earlier Spot claims that may be at higher risk of denial Prioritize denied claims by potential reimbursement Reduce rework caused by inaccurate or incomplete information Give staff more consistent information for follow-up decisions A focused AI strategy starts with the workflow problem, uses data that is relevant to that problem and keeps staff in control of judgment-based decisions. A more proactive approach to revenue cycle management Revenue cycle teams can move from reactive work toward a more proactive approach: catch errors earlier, validate coverage before claims are created and prioritize the claims and denials that need the most attention. Experian Health offers revenue cycle solutions that use AI and automation in targeted ways to support front-end data quality, reduce rework and manage denials. Patient Access Curator supports registration and coverage validation, while AI Advantage supports denial prediction and triage. Learn more about Experian Health’s Patient Access Curator and AI Advantage.

October 2, 2026 by Andy.Monte@experian.com
Experian Health ranked #1 in Best in KLAS for 2025

Experian Health is very pleased to announce that we've ranked #1 in the 2025 Best in KLAS: Software & Services report, for our Contract Manager and Contract Analysis product, for the third consecutive year. Contract Manager, when paired with Contract Analysis, empowers healthcare providers by ensuring payers comply with contract terms, identifying and recovering underpayments, and arming them with real claims data to negotiate contracts. This enables providers to negotiate more favorable terms and maintain financial stability.  Clarissa Riggins, Chief Product Officer at Experian Health, says, “In the ever-evolving healthcare landscape, our Contract Manager solution has once again been recognized as the #1 Revenue Cycle Management tool by KLAS for the third consecutive year. This prestigious ranking underscores the significant value our solution delivers to our clients by identifying underpayments and facilitating revenue recovery. We are honored to continue supporting our clients with innovative solutions that drive financial success and operational efficiency.”  Learn more about how Contract Manager and Contract Analysis can help your healthcare organization validate reimbursement accuracy, recover underpayments and boost revenue.   Learn more Contact us

February 5, 2025 by kelly.nguyen
How to increase patient engagement

Learn how providers can increase patient engagement, why it matters and key strategies that deliver improved end-to-end patient experiences.

January 30, 2025 by Experian Health

Spotlight test

Spotlight Description

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Sticky Subscribe Title

Sticky Subscribe Description
Sticky Subscribe

Testing Spotlight Paragraph block

Testing the spotlight block header

Archive Testing

Categories