Data Science Reveals Patients at Risk for Adverse Outcomes Due to COVID-19 Care Disruptions

This article is based on a 2020Healthcare Analytics Summit(HAS20Virtual) presentation by Imran Qureshi, Chief Information Officer and Chief Data Science Officer, b.well Connected Health, titled, “Navigating the Post-COVID World Through Data Science.”

As of January 2020-21, the pandemic has resulted in 76 million COVID-19 cases and over 1.6 million deaths, many of thosehealthcare personnel. In addition to these devastating outcomes and rising mortality rates, healthcare organizations face additional challenges from the pandemic’s major disruption in routine care. COVID-19 has impacted every area of healthcare: ambulatory practice visits have declined60 percentwhile telehealth visits have increased; diagnostic testing hasdecreased以及在医院寻求治疗的患者;and hospitals nationwide have lost$60.1 billiona month, largely due to an unexpected stop in elective procedures.

These significant care disruptions have far-reaching effects on patients’ health, leaving organizations guessing about how to reach patients and deliver care when routine checkups stopped. Without these regular touchpoints, health systems struggle to identify and treat patients at a higher risk for adverse outcomes. While some people have stopped going to the doctor during the pandemic, patients with serious conditions, such as comorbidities and chronic diseases, can’t afford to miss routine visits. Conditions like diabetes and heart disease negatively impact a person’simmune system, putting them at higher risk for contracting COVID-19, other serious illnesses, and worse long-term health. The danger in delayed care can also lead to irreversible conditions, which providers and patients could have prevented with earlier involvement, and in some cases, an early death.

With so many unknown variables—vaccine development, adherence to face mask mandates, and social distancing guidelines—health systems can rely on healthcaredata scienceto identify their most vulnerable patients and predict the effects of care disruptions on those patients. Predictive modeling, based on accurate data, provides visibility into at-risk patient groups,possible patient outcomes, and the best- and worst-case scenarios, helping health systems plan accordingly and prevent declining health.

Healthcare Data Science Helps Health Systems Identify High-Risk Patients

在新的流动医疗环境中，卫生系统可以通过数据科学驱动的方法(例如，机器学习和预测模型)来解决医疗服务中断的问题，以识别受突发医疗服务提供变化影响最大的患者。由于预测模型提供了因护理中断而面临最严重后果风险的患者的信息，卫生系统可以采用早期干预措施，并将资源用于需要更复杂护理的患者。

To identify patients at the highest risk for adverse outcomes from care disruptions, health systems can first identify care disruption in the past. Although not on the same scale as COVID-19, historical care disruption information (e.g., reduced primary care provider (PCP) visits or increased uninsured patients) is a starting point. Once health systems have gathered enough data sets to identify patients who have experienced care disruptions, they are ready to create predictive models.

Care Disruption in Predictive Models Forecasts Future Outcomes

为了在预测模型中考虑护理中断，医疗数据科学从业人员可以在模型中包括特定的护理中断，例如列出PCP就诊的中断(图1)。在预测模型中包括中断，允许算法预测护理中断对未来结果的影响(例如，总成本)。

While the predictive models are only estimates, they are still powerful tools in helping health systems identify their most vulnerablepopulations. Focusing on the information a health system can access, such as the effects of the care disruption (e.g., stopped PCP visits), instead of focusing on the information a health system does not have (e.g., the impact of the cause of COVID-19 on outcomes), will help providers identify the patients at highest risk of adverse outcomes. Data science experts can also implement analysis techniques—such asoversampling,class balancing,zero inflation, and others—to account for having fewer positive samples in the past data compared to today.

Quantifying Care Disruption Is Critical for Accuracy in Healthcare Data Science

After identifying and selecting a care disruption to include as a feature in the model, the next step is to convert the care disruption into a numeral to insert into the predictive models. To do so, data science teams must quantify each care disruption. It is up to each health system how to quantify each one. For example, one hospital could calculate the proportion of telehealth visits compared to normal visits or estimate the number of visits per month for emergency room, PCP, and specialist visits compared to last year’s visits.

Once a data scientist quantifies the care disruption, she must derive a number that reflects how much it has actually happened. The simplest way to think of this is to create a moving average. For example, a health system can create a three-month moving average to eliminate small variations, such as whether a patient went to the doctor on June 29 or July 2, but still accurately identify any changes in routine care.

As data scientists adjust this moving three-month average, they can identify when the change in the moving average was more significant than the threshold, resulting in care disruption (Figure 2). For example, in the graph below, something happened between March and April that decreased a patient’s moving average of four PCP visits per month to one visit per month. Healthcare data science teams can use this number to measure, or quantify, the change in this patient’s care, and then insert that number into the predictive model.

Care Disruption Combined with Case Mix Information Leads to Better Results

Measuring disruptions in care is the best place to start identifying high-risk patients, but it is not sufficient on its own. Another important tool inidentifying patientsat increased risk for adverse outcomes from COVID-19-induced changes to healthcare is case mix. Case mix includes health information such as chronic diseases and changes in cost or care over the past few years. Adding care disruptions and case mix as features in the predictive models allows for a more accurate prediction because the model can learn why two patients who look the same have different costs.

For example, if a health system has two patients, who both have the same reduction in PCP visits, data scientists should include each patient’s case mix in addition to the care disruption (i.e., reduced PCP visits). In this example, the case mix reveals that the first patient has diabetes and congestive heart failure, both chronic conditions that put him at risk for worse outcomes. The case mix would also show that the second patient is healthy, and the hospital does not need to apply interventions or include additional monitoring. With the case mix information, a health system can avoid wasting its limited resources, staffing, and supplies monitoring patients who do not need additional care.

Predictive Model Results Reveal Which Care Disruption to Prioritize

由于大流行对护理造成了如此多的干扰，数据科学家需要了解每个患者应对哪些护理干扰(例如，PCP就诊次数下降、保险损失或急诊就诊次数增加)。数据科学团队可以使用特征贡献(feature contribution)，这是医疗数据科学中的一个概念，它接受一个预测，并将该预测分布到每个特征中，从而让团队了解每个特征对预测的贡献程度。在下面的模型中(图3)，护理团队需要优先解决PCP就诊中的中断问题，因为它是影响结果的最大因素(在这种情况下，是总成本)。

Care Disruptions Impact Physician Performance, Affect Populations

In addition to the impact of care disruption on patients, health systems can also use machine learning to understand how changes in care affect provider performance including safety, cost, and quality. Predictive models reveal how provider performance variations impact patients at a higher risk for adverse outcomes.

For example, Dr. Jones’s patients cost an average of $100,000, while Dr. Smith’s patients cost an average of $80,000 for the same procedure. At first, the data shows that Dr. Jones costs the health system more money, but additional data, such as the episode date, reveal that Dr. Jones’s patients received care before the pandemic and Dr. Smith’s patients after. The pandemic caused significant care disruptions, such as a halt in primary care visits and non-essential care (e.g., physical therapy), explaining the cost differential between the doctors instead of assuming that the difference is related to provider performance.

Because non-essential services stopped due to COVID-19, patients might have cost the health system more money because they received a full breadth of services before they were unavailable. With this information, leadership can use cost to learn which patients have not received complete services and if they are at higher risk for worse outcomes because of it.

Healthcare Data Science Can Prevent Adverse Outcomes for At-Risk Patients

有了新型冠状病毒，卫生系统不能再依赖于数据科学的基本假设——未来看起来像过去。然而，由于医疗保健一直存在中断，甚至在COVID-19之前，卫生系统可以在预测模型中使用这些信息来预测未来的结果。

正在发生的大流行期间护理中断直接增加了一些患者出现不良后果的风险。卫生系统必须识别这些高危患者，以便在病情恶化之前实施适当的护理干预措施。Predictive modeling withvaried data setsallows leaders to identify the patients at the highest risk of adverse outcomes from care disruptions and plan for different what-if scenarios. With data science, providers and their care teams can proactively intervene and avoid waiting until these high-risk patients come to the hospital.

Additional Reading

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

1. Shifting to Virtual Care in the COVID-19 Era: Analytics for Financial Success and an Optimized Patient Experience
2. Six Proven Methods to Combat COVID-19 with Real-World Analytics
3. How to Run Analytics for More Actionable, Timely Insights: A Healthcare Data Quality Framework
4. Population Health Success: Three Ways to Leverage Data
5. Four Strategies Drive High-Value Healthcare Analytics for COVID-19 Recovery

---

PowerPoint Slides

你想使用或分享这些概念吗?Download the presentation highlighting the key main points.

Click Here to Download the Slides

Data Science Reveals Patients at Risk for Adverse Outcomes Due to COVID-19 Care Disruptionsfrom世界杯葡萄牙vs加纳即时走地