Tag: Population Health Management

Population Health Management: SDOH Challenges and Solutions

Aug 7, 2023

By ARJUN GOSAIN

In the United States alone, one in ten people live in poverty, 10.2% of households are food insecure, and more than half of people living below the poverty line are transportation insecure. These statistics represent social determinants of health (SDOH) measures that describe a patient’s experience outside hospital walls.

Health.gov defines SDOH as “the conditions in the environments where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks.” This definition argues that a patient’s experiences are just as crucial if not more telling than their biology.

And this makes sense as a person who is housing insecure may not have the same access to nutritional food, transportation, or social support. Additionally, some patients, in their efforts to maintain health, may experience discrimination based on their skin color or religious beliefs.

Some studies have found SDOH can drive up to 80% of health outcomes. This means that the traditional healthcare model—hospitalization, healthcare delivery, and treatment—only affects a mere 20% of a person’s overall health. To tap into this 80%, healthcare professionals need data. However, SDOH data collection poses significant challenges.

SDOH Overview

Before we dive into data collection, let’s review the specific measures of SDOH and why they should take top priority among healthcare professionals.

SDOH concepts include:

Employment insecurity: Measures whether the patient is employed and their current employment or unemployment experience. This includes whether they were harassed on the job or experiencing unequal pay. Employment insecurity can lead to financial stress, mental health problems, and reduced healthcare access.
Psychological circumstances: Measures current events that are affecting the patient’s health. This encompasses a wide range from unwanted pregnancies to exposure to war or violence. Stress, anxiety, and other negative emotions can have a direct effect on a patient’s physical health and contribute to disease development.
Housing insecurity: Notes whether a patient has a consistent place to live or is forced to move regularly. Homelessness or housing insecurity can lead to exposure to the elements, mental health challenges, and increased vulnerability to infection.
Social adversity: Examines a patient’s social experience including any discrimination or persecution the individual may be facing. Increased social adversity can cause an individual to socially isolate and develop feelings of depression.
Transportation: Observes the patient’s access to transportation including available public transport. Missed appointments can be the direct result of transportation inaccessibility which leads to a decrease in the quality of care.
Food insecurity: Indicates whether a patient has adequate food access and safe drinking water access. Receiving adequate nutrition is essential for maintaining optimal physical health. For example, if a child is food insecure, it can lead to serious developmental issues and chronic disease.
Education and literacy: Observes a patient’s ability to read and comprehend hospital paperwork. Note that individuals with higher literacy and education rates typically make more informed health decisions.
Occupational risk: Examines how a patient’s current employment affects their overall health. Determines if their job site places them at risk of toxin exposure, physical harm, undue stress, or other hazardous conditions that can contribute to injuries or illnesses.
Economic insecurity: Measures a patient’s poverty level to determine if copays, rent, and hospital bills are manageable. A patient living with inadequate finances will face a greater barrier to quality care.
Lack of support: Notes whether a patient has reliable support when experiencing difficult circumstances such as the death of a loved one. If a patient has a present support network, they will be able to receive practical, emotional, and physical assistance in times of need.
Upbringing: Takes a patient’s childhood, family, and upbringing into account to assess if a patient is carrying trauma from previous years. Adverse childhood experiences can increase the risk of chronic diseases and mental health issues later in life.
Language: Examines any language or communication concerns, so that a patient can both communicate their issues and understand oral and written treatment. Miscommunications can lead to misdiagnoses and inadequate treatment.

These contributing factors cannot be ignored since, as previously stated, they can directly impact up to 80% of health outcomes. Thus, organizations that choose to neglect SDOH factors are only focused on the 20%.

This is why providers must find ways to address SDOH in a meaningful and productive manner, which is where SDOH data comes in. The collection and analysis of SDOH data can help providers identify at-risk populations to provide informed, effective interventions. Measures like patient needs assessments and population-level health disparity analysis can let providers get to the root cause without the guesswork.

SDOH Data Collection Challenges

SDOH data collection is a sensitive topic. After all, if a patient is experiencing abuse or is unemployed, they most likely would not disclose that information outright. Providers also have limited time to ask additional questions because many feel rushed during routine consultations and may not have the resources needed to collect SDOH data.

Beyond SDOH data scarcity, there is the issue of standardization. How providers collect housing data, for instance, can vary across definitions and measurements, making quantifying data difficult. So, how can providers offer whole-person care with limited data and a lack of definitive measurements? The solution is three-fold.

The Great ACO Debate: 2014 Edition

Jan 19, 2014• 12

By Joseph Damore & Wes Champion

With the beginning of 2014 comes another year of the great accountable care organization (ACO) debate.

Is it a model to deliver high-quality, cost-effective care and improve population health management (PHM)? Or, just a passing fad, similar to the HMOs of decades ago?

Many opinions exist, and they’ll continue to be debated, especially during an election year. One thing most of us can agree on about ACOs is they are a work in progress.

We can say with some certainty that ACOs are taking hold; look no further than their growth, which now exceeds 600 public and private ACOs nationwide with the recent addition of 123 ACOs to the Medicare Shared Savings Program. But they still beg more questions than answers. What types and sizes of hospitals are forming ACOs, and where are they located? What does the pipeline of emerging ACOs look like, and how long will their journey take? And what capabilities, investments and partnerships are essential to ACO participation? What is the longer term performance?

Who better to ask than the decision makers running the organizations that participate in an ACO?

In August of 2013 we surveyed 115 C-suite executives– primarily CEOs (43.5%), chief financial officers (17.4%) and chief operating officers (16.5%) – across 35 states to collect data on their perspectives on ACO and PHM.

Survey results support the increase in ACO popularity. According to respondents, ACO participation has almost quadrupled since spring 2012: More than 18% say their hospitals currently participate in an ACO, up from 4.8% in spring 2012. This growth is projected to accelerate, with about 50% of respondents suggesting their hospitals will participate in an ACO by the end of 2014. Overall, 3 out of 4 senior executives surveyed say their hospitals have ACO participation plans.

Since survey respondents also represent hospitals of different locations, sizes and types, we are able to obtain a broader look at current and future ACO participation and found that:

Non-rural hospitals (82.1%) are most likely to participate in an ACO overall, followed by hospitals in an integrated delivery network (81.1%).
The lowest rates of projected participation are among rural hospitals (70.7%) and standalone hospitals (72.6%).
Large hospitals are moving more quickly, as 30.8% said they’d be part of an ACO by the end of 2013.
And though they’re equally as likely as large hospitals to ultimately participate in an ACO, small hospitals say they require additional time, with 48.6% planning to join in 2014 or 2015.

But some providers have been more deliberate and cautious about when they start their ACO journey. The pace of ACO adoption has been slower than originally anticipated 18 months ago, when more than half of executives predicted their systems would create or join an ACO by the end of 2013. Current survey results show that about 1 out of 4 will meet that projection.

Behavioral Economics and Influenza Immunization

Feb 10, 2013• 1

By JAAN SIDOROV

On occasion, your correspondent fights the northeast’s dreary weekend winter evenings with a dram of spirituous liquor like Macallan 12. Unlocked with a small splash of water and a single ice cube, a generous ounce of that pungent cinnamon leathery elixir turns the cold into cozy.

So naturally, your correspondent relies on spouse to help keep a therapeutic stock available. Both yours truly and spouse run errands and it shouldn’t be too hard for either to be proactive by periodically checking supplies, buying some Macallan when necessary and avoiding the unhappiness of a dispirited and cold author.

Unfortunately, spouse doesn’t always see it that way.

Welcome to the complicated world of behavioral economics. It tells us that it’s difficult for persons to expend effort today to reduce the tomorrow’s risk of an unlikely event. It’s why many persons chose to not take or pay for medications today to reduce the distant likelihood of disability or early death. There’s more on the topic here.

This also explains why persons don’t do a good job getting a flu shot for themselves or their loved ones. Check out this interesting information from athenahealth. According to their pooled electronic health record (EHR) data, 2.5% of children without a flu shot came down with the flu, versus only 0.9% of those who got the shot. While getting a shot reduced the relative risk of coming down with the disease by approximately two thirds, the vast majority of kids who went without immunization (97.5%) did OK. Data from the CDC in adults reflects the same kind of numbers: 80% of persons in the U.S. do not come down with the flu in the course of the year.

How can the population health and care management community leverage behavioral economics to increase immunization rates?

Why Everybody Should Read “Why Nobody Believes the Numbers”

Sep 21, 2012• 1

By Jaan Sidorov, MD

Back in the early 1900s, Albert Einstein had a problem. Sophisticated instruments were unexpectedly showing that the measured speed of light was the same if the source or the observer were moving or stationary. In other words, if one were moving away from a bullet, it should look (to the observer) that the bullet had slowed down. Light’s refusal to conform to the prevailing common sense about how the universe should work ultimately forced Einstein in 1905 to conclude that, in order for the speed of light to be constant, time and mass had to be elastic. This ushered in a new field of relativity mathematics that is still being used to plumb the known universe’s Music of the Spheres.

While the controversies surrounding the effectiveness of “population health management” (PHM) are quite minor compared to Einstein’s Theory of Relativity, the comparison is still instructive. The similar mismatch between what is assumed, what is observed and how to mathematically describe the ultimate truth also underlies Al Lewis’ book, Why Nobody Believes the Numbers. In other words, we assume care management-based patient coaching always yields savings, increasingly sophisticated observations often fail to show it and, as a result, we need new mathematics to reconcile what we assume and what we observe.

Interestingly, author Al Lewis of the Disease Management Purchasing Consortium never doubts the speed of light or that high quality PHM ultimately can save money. While PHM vendors may interpret his long history of skepticism as some sort of shakedown, Al’s passion is clearly evident: Why Nobody Believes the Numbers is ultimately driven by a search for the truth. For that he deserves a lot of credit.