Resolving Uncompensated Care: Artificial Intelligence Takes on One of Healthcare’s Biggest Costs

卫生系统可以采取什么行动来显著改善其底线?向病人收取未支付的保健服务余额(无补偿护理);通过这样做，系统可以控制它们的最高成本之一。

This article will explain how artificial intelligence (AI) powers solutions that help organizations reduce their rates of uncompensated care and improve the patient experience. Known as propensity-to-pay tools, these solutions integrate with workflow tools to identify the likelihood that a patient will pay a balance, and then assign appropriate ways for finance teams to reach out to the patient.

Uncompensated Care Can Cost Health Systems Billions Annually

Uncompensated care includes bad debt (balances that can’t be recovered) and charity care (healthcare provided for free or at reduced costs to low-income patients) for self-pay patients. Self-pay patients either don’t have health insurance or have a balance due that their insurance doesn’t cover (due to coinsurance, deductibles, or services their policy doesn’t cover).

对卫生系统而言，无补偿医疗的经济影响是重大的。For example, one regional health system wrote off$350 millionto bad debt in 2016. In 2015, a larger health system reported bad debt of over $3 billion. Healthcare consumers are also feeling the impact of high-deductible plans and large out-of-pocket expenses:medical debtis the number one cause of bankruptcy in the U.S.

Uncompensated Care Is Not Going Away in the Era of High-Deductible Health Plans

随着高免赔额医疗计划越来越普遍，保险计划允许人们在不确定消费者是否能够支付自付费用的情况下注册，卫生系统正经历着无补偿医疗的可观增长。

According to theKaiser Family Foundation and Health Research & Education Trust’s 2015 Summary of Findings, the proportion of U.S. workers with high-deductible insurance plans grew from 13 percent in 2010 to 24 percent 2015. During that same period, the patient’s annual financial responsibility rose from $2,713 to $4,955. This trend in high-deductible plans and uncompensated care will continue, and may even become more relevant under at-risk reimbursement models, as organizations take on more financial risk.

Health Systems Need a More Effective Way to Collect on Balances

In addition to the proliferation of high-deductible health plans, another factor behind the massive unpaid balances in healthcare today is that organizations don’t have effective and efficient ways to collect on these debts. Because high-deductible plans place all payment risk on the patient and the health system, organizations must navigate each individual insurance plan, balance, and allowable charges, and then collect on the balance.

卫生系统通常有数十到数百名团队成员的部门，专门负责收集患者余额，或为患者提供慈善护理。但是，考虑到收集的复杂性(如上所述)，这些部门只能做这么多;与他们必须收集的余额相比，他们的资源相对较少。例如，大型组织可能在任何给定时间都需要收集25万名患者的余额，而只有大约50人负责收集工作。

The collection process often involves calls and letters to anyone with an outstanding balance, so that people who are likely to pay and those unlikely or unable to pay receive the same treatment. This one-size-fits-all approach can create a bad experience for patients who intend to pay in full, as they’re unnecessarily pursued, while futilely expending resources on patients who are unlikely or unable to pay and would be better served by charity care.

There’s a lot of nuance to an unpaid bill that health systems don’t have a way of understanding (e.g., the patient has a good income, but can’t pay a huge sum out of pocket at once, versus a patient with a low income who’s a candidate for charity care). To effectively reduce uncompensated care, organizations need to be thoughtful about who they contact about collection and how they reach out.

Current EMRs and billing systems employ overly simplistic methods that use generic modes of prioritization, such as sorting collections by highest to lowest balance, oldest to newest balance, or alphabetically by patient name. This blind approach leads to lost revenue and inappropriate treatment of patient balances.

Even experts in healthcare billing are vulnerable to bad debt. For example, arevenue cycle一家大型综合医疗系统的合同管理顾问因为账单寄错了地址而没有付款，结果被催收。这名记账专业人员有意愿和手段来支付，但卫生系统没有一个有效的方法来确定其人口的支付倾向。如果这个对账单流程有高度了解的人能够漏网，那么任何人都可能遇到这种情况。

为了降低无补偿护理率和改善患者体验，卫生系统必须有更好的方式来了解患者付费的可能性，并有效地向他们伸出援助之手。

Artificial Intelligence Propensity to Pay: A More Informed Approach

An AI-driven approach to propensity to pay generates a comprehensive view of a patient’s likelihood to pay by combining external (e.g., census, income, and poverty levels) and internaldata(e.g., patient payment performance data that’s unique to that health system and its population). By merging millions of records, using algorithms to derive a patient’s propensity-to-pay score, and seamlessly integrating that knowledge into the organizational workflow, AI gives health systems a pragmatic approach to collecting from patients and driving down uncompensated care.

By understanding propensity to pay, health systems can determine which patients need reminders, which need financial assistance, and whether payment patterns are likely to change over time and after particular events. Organizations can then dedicate resources to the bills most likely to be paid, rather than pursuing balances that are unlikely to be resolved.

Figure 1 shows how the propensity-to-pay program uses algorithms to designate a patient’s propensity to pay and align that patient with an appropriate intervention:

• Prioritize collection efforts (high likelihood to pay).
• Send to charity team (low likelihood and low ability to pay).
• Collect on balance or send to bad debt (low likelihood and high ability to pay).

Five Key Actions in the Propensity-to-Pay Process

AI-driven propensity to pay performs four key actions:

Step #1: Identifies propensity to pay.

An algorithm uses several factors to determine a patient’s propensity to pay: historical payment and demographic information, external socioeconomic data, size of balance, patient age (older individuals are more likely to pay), and many more data points. The propensity-to-pay program then designates corresponding interventions. The integrated propensity-to-pay identification step occurs in the EMR workflow to enable finance departments to better allocate staff and resources and follow intervention recommendations.

Step #2: Designates intervention for patients with lower propensity to pay.

支付倾向较低的患者将获得自动提醒或经济援助咨询。记账人员可以看到哪些账户的支付倾向最低，并将其冲销为坏账(为更有前途的账户保留资源)。例如，一个支付倾向低、结余高、有慈善护理历史的病人可能无法支付。与其让这个病人继续背负坏账，财务团队可以通过将病人转移到慈善机构或政府资助来立即提供帮助。

Step #3: Designates intervention for patients with medium propensity to pay.

中等付费倾向的患者将接受针对他们情况的干预(例如，提醒电话，建议慈善护理等)。该系统进一步将中等付费倾向的患者分为中等低和中等高类别，针对这两类患者，开发人员目前正在测试干预措施和方法(这是人工智能实现持续改善的一个例子)。例如，这些患者可能对自动支付计划和电子邮件提醒的反应最好。

Step #4: Designates intervention for patients with high propensity to pay.

Patients with high propensity to pay will receive no initial intervention. If these patients don’t pay after a set time frame, they receive a phone call or other targeted intervention (e.g., email or text reminders). If patients with high propensity to pay miss a payment, staff can reach out to help the patients avoid bad debt.

Step #5: Seamlessly integrates with the EMR.

支付倾向分析引擎在EMR后台顺利工作，并将未结算账户分类直接交付给计费团队的原生工作流。

Understanding Propensity to Pay Is Critical in Today’s Healthcare Industry

随着高免赔额医疗计划变得越来越普遍，财务风险继续向患者和卫生系统转移，各组织必须有办法遏制日益增长的无补偿医疗。人工智能支持的付费倾向工具是一个有效的解决方案，因为它们结合了内部和外部的患者数据，让财务部门全面了解患者的付费可能性。这些预测模型还直接集成到工作流程中，使计费部门能够立即获得患者群体中的支付倾向和建议的干预措施。

With an intelligent, analytics-driven propensity-to-pay approach, health systems will lose less money on uncompensated care, patients will avoid unnecessary bad debt collection, and those in financial need will get timely help.

Additional Reading

Would you like to learn more about this topic? Here are some articles we suggest:

1. Data-Driven Approach Identifies Nearly $33 Million of Savings Annually
2. How One Hospital Took the Pain Out of Getting Paid
3. Healthcare Revenue Cycle: How to Improve Data Timeliness and Reduce Manual Work