Predicting health-related social needs in Medicaid and Medicare populations using machine learning

Abstract

Providers currently rely on universal screening to identify health-related social needs (HRSNs). Predicting HRSNs using EHR and community-level data could be more efficient and less resource intensive. Using machine learning models, we evaluated the predictive performance of HRSN status from EHR and community-level social determinants of health (SDOH) data for Medicare and Medicaid beneficiaries participating in the Accountable Health Communities Model. We hypothesized that Medicaid insurance coverage would predict HRSN status. All models significantly outperformed the baseline Medicaid hypothesis. AUCs ranged from 0.59 to 0.68. The top performance (AUC = 0.68 CI 0.66–0.70) was achieved by the “any HRSNs” outcome, which is the most useful for screening prioritization. Community-level SDOH features had lower predictive performance than EHR features. Machine learning models can be used to prioritize patients for screening. However, screening only patients identified by our current model(s) would miss many patients. Future studies are warranted to optimize prediction of HRSNs.

Full text

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Predicting health‑related social
needs in Medicaid and Medicare
populations using machine learning
Jennifer Holcomb1,2, Luis C. Oliveira3,4, Linda Higheld5,6, Kevin O. Hwang7,
Luca Giancardo8 & Elmer Victor Bernstam3,6*
Providers currently rely on universal screening to identify health‑related social needs (HRSNs).
Predicting HRSNs using EHR and community‑level data could be more ecient and less resource
intensive. Using machine learning models, we evaluated the predictive performance of HRSN status
from EHR and community‑level social determinants of health (SDOH) data for Medicare and Medicaid
beneciaries participating in the Accountable Health Communities Model. We hypothesized that
Medicaid insurance coverage would predict HRSN status. All models signicantly outperformed
the baseline Medicaid hypothesis. AUCs ranged from 0.59 to 0.68. The top performance (AUC = 0.68
CI 0.66–0.70) was achieved by the “any HRSNs” outcome, which is the most useful for screening
prioritization. Community‑level SDOH features had lower predictive performance than EHR features.
Machine learning models can be used to prioritize patients for screening. However, screening only
patients identied by our current model(s) would miss many patients. Future studies are warranted to
optimize prediction of HRSNs.
e association of social determinants of health (SDOHs) and social needs with health outcomes has been rec-
ognized internationally and in the United States. While oen used interchangeably, these are distinct concepts.
SDOHs are broader upstream social conditions in which people are born, live, and work while social needs are
more immediate and downstream individual or family needs impacted by the conditions1,2. Social needs such
as food insecurity havebeen associated with depression3, diabetes distress3, and chronic health conditions4–7.
Similarly, children who experience energy insecurity (i.e., inability to obtain energy to heat or cool one’s home) in
their household are at an increased odds of food insecurity, hospitalization, and poor health8. Unmet social needs
have also been associated with missed medical appointments, more frequent emergency department (ED) use
and hospital readmission9,10. ere is increasing evidence of the impact of social interventions to increase access
to preventive healthcare11, improve management of chronic conditions11, and reduce hospital admissions12,13,
reducing healthcare costs.14–16.
To achieve more equitable health outcomes at lower costs17, healthcare systems should prioritize individual
patients for social interventions18. Screening patients, particularly those who are low-income and those at highest
risk for adverse health outcomes, is an important step in addressing social needs19. Current approaches to screen-
ing for social needs in U.S. healthcare settings rely on universal screening of patients. Various universal screening
approaches have been tested through the Protocol for Responding to and Assessing Patient Assets, Risks, and
Experiences (PRAPARE)20, Your Current Life Situation screening tool developed by the Kaiser Permanente Care
Management Institute21, and the Accountable Health Communities (AHC) Model screening tool developed by
OPEN
1Department of Management, Policy, and Community Health, The University of Texas Health Science Center
at Houston (UTHealth) School of Public Health, 1200 Pressler St, Houston, TX 77030, USA. 2Sinai Urban Health
Institute, 1500 South Faireld Avenue, Chicago, IL 60608, USA. 3The University of Texas Health Science Center
at Houston (UTHealth) School of Biomedical Informatics, 7000 Fannin, Houston, TX 77030, USA. 4Houston
Methodist Academic Institute, 6670 Bertner Ave, Houston, TX 77030, USA. 5Departments of Management, Policy,
and Community Health and Epidemiology, Human Genetics and Environmental Sciences, The University of Texas
Health Science Center at Houston (UTHealth) School of Public Health, 1200 Pressler St, Houston, TX 77030,
USA. 6Department of Internal Medicine, The University of Texas Health Science Center at Houston (UTHealth)
John P and Katherine G McGovern Medical School, 6410 Fannin, Houston, TX 77030, USA. 7Center for Healthcare
Quality and Safety at UTHealth/Memorial Hermann, The University of Texas Health Science Center at Houston
(UTHealth) John P and Katherine G McGovern Medical School, 6410 Fannin, Houston, TX 77030, USA. 8Center
for Precision Health, The University of Texas Health Science Center at Houston (UTHealth) School of Biomedical
Informatics, 7000 Fannin, Houston, TX 77030, USA. *email: elmer.v.bernstam@uth.tmc.edu
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the Centers for Medicare & Medicaid Services (CMS) Innovation Center (CMMI)22,23. e PRAPARE social
needs assessment has been used frequently across healthcare settings and aligns with national data systems (e.g.,
Uniform Data System used by the Health Resources and Services Administration with Federally-Qualied Com-
munity Health Centers (FQHCs)24. A pilot approach to universal health-related social need (HRSN) screening
through CMMI’s AHC Model22,23 is currently being implemented by 28 organizations across the U.S. However,
the U.S. currently lacks standards and guidelines related to the collection of social needs screening data, par-
ticularly in healthcare settings25–28. Surveying patients requires healthcare sta to build trust with patients and
for healthcare systems to increase healthcare spending to ensure dedicated healthcare sta, electronic health
record (EHR) infrastructure, other resources (e.g., funding, sta training, screening materials) and time29–31.
Additionally, studies have shown that healthcare sta, including primary care physicians, do not feel condent
screening for and responding to social needs, leading to low screening rates30,32. Integration of HRSN data into
the EHR in an actionable format continues to present a challenge for healthcare providers and limits screening
as a pathway to addressing social needs33.
An alternative approach to universal screening is to utilize patient risk scores or risk prediction models
to identify and prioritize patients who are most likely to have HRSNs. Risk scores are already widely used in
healthcare settings to predict a range of outcomes from specic disease conditions (e.g., cardiovascular disease)
to hospital readmissions, healthcare cost, and ED utilization34–38. Recently, there has been increasing interest in
using SDOH and social needs data to improve risk prediction models. Risk prediction eorts linking community-
level geocoded data with EHRs and other patient-level data sources (e.g., claims/administrative data) are nascent
and to date have primarily focused on predicting healthcare utilization, such as hospital readmission and ED
visits34,39–41. Studies have been limited by lack of data on individuallevel social needs and in most cases limiting
to a single healthcare provider or system34,41,42. To date, few studies attempted to predict individual patient social
needs43. ese studies attempted to predict social service referrals rather than whether the patient reported a
social need.
An opportunity exists to better understand the potential for integrating risk prediction to proactively identify
patients in need of further social need assessment or social intervention outside of the healthcare model. Predict-
ing HRSN status is a novel application of predictive models and highly relevant and actionable as screening is the
rst step in the social intervention pathway. Risk prediction could also help address the structural and logistical
barriers to universal HRSN screening implementation that have been identied in recent research, including the
low level of uptake by providers, lack of time, EHR integration, availability of trained or skilled sta to conduct
screening (and intervention), patient preference, and increased costs, which are oen not reimbursed22–26. To our
knowledge, there are not currently studies available comparing universal versus targeted screening approaches
for HRSN. However, research in other health domains such as HIV indicates that targeted screening can be
benecial when implementation barriers such as those noted above are present44. e objective of this study
was to predict HRSNs of patients in the CMMI AHC Model from patient-level EHR data and publicly available
community-level SDOH data. We evaluated the predictive performance versus a baseline method using Medicaid
status to assume existence of HRSNs. Our hypothesis was that using a combined dataset would outperform any
single data source alone. We further hypothesized that patients insured by Medicaid would be likely to have a
HRSN and that the combined dataset would more accurately predict social needs status (e.g., positive or nega-
tive) than the Medicaid assumption.
Methods
Study design. Patient-level HRSN screening data were collected from September 2018 through Decem-
ber 2020 in the Greater Houston area, Texas in a cross-sectional study design. e AHC Model implementa-
tion in the Greater Houston area is a part of a national randomized controlled trial funded by CMMI to test a
systematic approach to HRSN screening, community resource referral, and community resource navigation of
CMS beneciaries22,23,45. Any community-dwelling CMS beneciaries accessing care across 13 clinical delivery
sites including Emergency Departments (ED), Labor and Delivery Departments and ambulatory clinics in three
large health systems were eligible to be screened. e three health systems included a nonprot, private hospital
system (Health System A), a network of outpatient clinics at an academic health university (Health System B),
and a safety net hospital (Health System C). Patient EHR data and community-level SDOH data were combined
to predict the HRSN status of those patients in the AHC Model. Eligible patients for analysis were those with a
completed screening survey in the AHC Model, EHR data from two years prior to HRSN screening date, and
an address for geocoding to facilitate linkage of community-level SDOH data. is study has been approved by
the Committee for the Protection of Human Subjects (CPHS, the UTHSC‐H Institutional Review Board) under
protocol HSC‐SBMI‐13‐0549. All methods were performed in accordance with the relevant guidelines and regu-
lations. Informed consent for this study was waived by the CPHS as part of protocol HSC-SBMI-13-0549.
Data features. Individual patient-level EHR data included demographics, diagnosis codes (ICD-10), pro-
cedure codes (CPT codes for ambulatory and hospital), and insurance type (Medicare, Medicaid or dually cov-
ered). For ICD-10 codes, only part of the code describing the disease category was used in order to create
clinically relevant "clusters". For community-level SDOH features, we reviewed the existing literature to identify
potential SDOH factors associated with known health outcomes, healthcare utilization, and HRSNs13,19,40,46–50.
Community-level SDOH data at the Texas state Census Tract level were derived from the 5-year (2015 to 2019)
estimates from U.S. Census Bureau’s American Community Survey (ACS) and the Centers for Disease Control
and Prevention’s Social Vulnerability Index (SVI) (2018). e 12 SDOH features include median household
income, poverty level, educational attainment, unemployment, health insurance coverage including uninsured
and public insurance, car ownership, home ownership, Supplemental Nutrition Assistance Program (SNAP)
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benets, overcrowding, and disability from the ACS and from the SVI, minority status and language. A descrip-
tion of the EHR and Census data can be found in the Supplementary Material.
As part of the HRSN survey patients were asked about four indicators of social needs, and for this study, we
developed models predicting the need for each of these indicators based on a patient’s EHR and associated ACS
and SVI Census data23. We used the following indicators based on 4 of the 5 core social needs screened for in
the AHC Model23: (1) Core need: housing situation—Identies whether respondent has HRSN related to hous-
ing stability and/or housing quality. (2) Core need: food insecurity—Identies whether respondent has HRSN
related to purchasing food. (3) Core need: transportation—Identies whether respondent has HRSN related to
accessing reliable transportation. (4) Core need: utilities—Identies whether respondent has HRSN related to
diculty paying utility bills. (5) Any core need—is indicator is true if at least one of the four core needs is
true. (6) All core needs—is indicator is true if all of the four core needs are true. In addition to these metrics,
we used the Medicaid ID on the survey to indicate whether the respondent was a Medicaid beneciary. is
metric was used as a baseline for testing the predictive model, under the assumption that respondents who are
Medicaid beneciaries would have HRSNs.
Data linkage. Figure1 illustrates how the data sources were combined to create the combined dataset.
We used a table specially created in the Master Patient Index database (MPI) to map patient IDs from the
HRSN survey to the corresponding patient ID in the EHRs51. e Match Analysis Methodology in the MPI
uses key information from the HRSN surveys like survey patient ID, rst name, last name, middle name, date
of birth, sex, address (city, state, zip) Medicare Beneciary Identier (Medicare), Medicare eective date, and
Medicaid eective date to link the records to the EHR data. We used the address provided in the survey and the
EHR to geocode each patient’s address and then determined the corresponding Census tract for the address. At
each stage of matching, exclusion criteria were applied. HRSN surveys without corresponding EHR data for the
patient were excluded (n = 2418). Any records whose geocode did not match between the HRSN survey data
and the EHR data as were excluded (n = 2814). ese records had a greater than 1-km dierence between the
address provided in the HRSN survey and in the EHR. e corresponding Census Tract was then used to match
the SDOH information from Census data. Any records matched with Census Tracts located outside of the state
of Texas were excluded because they could not be matched with SDOH information (n = 40). A Consort ow
diagram52 was used to depict sample size at each step in Fig.2.
Data analysis. First, we randomly allocated the samples into three datasets: 20% of the samples (n = 1960)
were allocated for the test set (not used during the training process); 80% of the remaining samples (n = 6272)
were allocated for the training set (64% of the entire data set), with the remaining samples (n = 1568) allocated
HRSN Outcomes
Community level
SDoH
Demographics
Procedures
Diagnoses
Survey id
Address
Census tract
Paent id
AHC Survey
Electronic Health
Record
(EHR)
enterprise
Master Person Index
(eMPI)
Census data
Geoloca
on
Figure1. Data sources and linkage for modeling. Flow chart showing the data sources combined to create the
dataset displayed. e data sources are displayed as three cylinders displaying the data linking between sources.
e measures from each source are displayed as a rectangle linking to other cylinders. Patient-level HRSNs were
collected in the AHC screening survey. Using survey demographic data, patients were mapped using a Master
Patient Index database (MPI) to patient ID, demographics, diagnosis, and procedures in the EHR. Patients
addresses provided in the survey and the EHR were geocoded to each patient’s address and corresponding
Census tract. e geocoding is displayed as a diamond connected to the HRSN survey data measures.
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for the validation set (16% of the entire data set. ese datasets were stratied such that each group contained
approximately the same percentage of samples of each target class as the complete set. e test set was reserved
for testing and was neither used for model training, nor for manually or automatically evaluating feature selec-
tion or any of the other model parameters. A Gradient Boosting Decision Tree Machine Learning algorithm
(LightGBM) was used to predict HRSN status using the individual and combined data sets53. ese types of
algorithms oer some degree of interpretability and work particularly well in machine learning problems with a
high dimensionality, large number of features, and large sparsity of data, which is one of the main hurdles when
dealing with EHR data. LightGBM is inherently able to handle missing data which allows us to avoid any type
of articial data imputation. e LightGBM model hyperparameters were tuned using a Bayesian optimiza-
tion, which allowed for an unbiased search of the best performing model without direct trial and error which
could lead to overtting54. Specically, we used the scikit-optimize library55 for a crossed validated Bayesian
search (implemented in the BayesianSearchCV scikit-optimize class) on the training set. e best combination
of hyperparameters was selected by maximizing the accuracy on the validation set. e test set remained com-
pletely independent from the hyperparameter search, thereby avoiding any risk of overtting and data leakage.
For a full description of LightGBM and the Bayesian hyperparameter search we refer the readers to the papers
referenced53–55.
Area Under the Receiving Operating Characteristic Curve (AUC)56 and comparison to a baseline decision
using Medicaid status were used to evaluate model performance on the test set. Analysis was performed using the
Python scikit-learn and lightGBM libraries. P-values were also computed with a non-parametric Mann–Whitney
U test, under the null hypothesis that, for each HRSN, the distribution of the ordinal real value output of the
models is equal when HRSN = False or HRSN = True.
Results
Table1 summarizes the demographic and HRSN characteristics of the patients included in the nal modeling.
Patients were primarily female (52.7%), Black or African American (40.6%), single marital status (59.3%), cov-
ered by Medicaid (85.4%), and screened at Health System A, a nonprot, private hospital system (83.8%). Over
half of patients (57%) screened positive for at least one HRSN. Food insecurity was the highest frequency need,
reported at 39%. Housing, transportation and utility needs were reported with similar frequencies (26–29%).
In Fig.3, we compare and contrast the predictive performance of the ML model trained with the set of features
(EHR, Census, or EHR + Census) and the baseline Medicaid status to determine a HRSN. All models trained
with EHR and Census features signicantly outperformed the baseline Medicaid insurance status to determine
presence of a HRSN as shown by the 95% condence intervals (CI) of the Receiver Operating Characteristic
(ROC) curves (shaded areas) when compared to the baseline Medicaid decision (shown as a red cross). When
all features were used, AUCs ranged from 0.59 to 0.68. e top performance (AUC = 0.68, CI 0.66–0.70) was
achieved by the “any HRSNs” outcome, which is the most useful for patient HRSN screening prioritization. In the
majority of experiments, models trained with community-level SDOH features had lower predictive performance
Health System A
surveys (n=12,961)
Health System C
surveys (n=1,469)
Health System B
surveys (n=642)
Total paents
(n=15,072)
Paents with EHR
data (n=12,654)
Excluded (n=2,418)
No EHR data
Paents with
accurate locaon
(n=9,840)
Excluded (n=2,814)
Locaon not aligned (EHR
and survey)
Paents with census
data (n=9,800)
Excluded (n=40)
Missing census data
Training dataset
(n=6,272)
Test dataset
(n=1,960)
Validaon dataset
(n=1,568)
Figure2. Consort ow diagram. Consort ow diagram depicting the sample size at each step of the data
linkage. e diagram moves downward with each step displayed as a rectangle. Patients were excluded from
the nal datasets if they had no EHR data, if there was not alignment with the EHR and survey addresses, and
if their geocoded location was missing corresponding Census data. ese exclusions are depicted as rectangles
with arrows along the diagram indicating where a patient sample was excluded. From these exclusions, the
bottom and nal three rectangles depict the training, validation, and test datasets included in the data analysis.
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than EHR features alone. e only exception was the “Diculty Paying Utilities” HRSN, where the main drivers
for predictive performance were Census features. In order to aid the reproducibility of our ndings, all model
hyperparameters automatically identied by the Bayesian search are shown in the Supplementary Material.
Discussion
We found that the addition of readily available SDOH data at the community-level did not improve performance
over data typically available in the EHR for predicting patient social needs status. Of the models, “any HRSN”
had the best predictive power at 0.68. Our AUC values were slightly lower than some previous studies43, but it
is important to note that our outcome (whether the patient reported a HRSN) is dierent than other currently
published studies (referral to a social service), limiting our ability to compare. Use of patient Medicaid insur-
ance status signicantly under-predicted social needs status, indicating that use of Medicaid insurance coverage
alone is not predictive and we caution providers against using this factor to determine need or who should be
screened. Previous studies have shown that patients of lower income status have high rates of social needs, poorer
self-rated health, and higher rates of chronic conditions19, particularly those with Medicaid and those dually
covered by Medicare and Medicaid57–59. Given that Medicaid in Texas covers low-income populations and our
Table 1. Demographic Characteristics and Health-Related Social Needs (HRSNs) of CMS Beneciaries in the
Accountable Health Communities (AHC) Model in the Greater Houston Area, September 2018 to December
2020. a Includes "Unknown", "Declined", "Not Answered” responses and records that had no response. b Patients
could be in multiple categories so numbers do not sum to total.
Characteristics
Sample (n = 9800)
Patients, No. (%)
Age, mean (SD), years 35.5 (26.3)
Race
Black or African American 3978 (40.6)
Other 2857 (29.2)
White 1763 (18.0)
Latin American 612 (6.2)
Hispanic or Latino 337 (3.4)
American Indian or Alaska Native 23 (0.2)
Asian or Pacic Islander 15 (0.2)
Unknowna215 (2.2)
Marital status
Single 5809 (59.3)
Married 1411 (14.4)
Widowed 368 (3.8)
Divorced 365 (3.7)
Separated 67 (0.7)
Life Partner 6 (0.1)
Legally Separated 6 (0.1)
Unknown 1768 (18.0)
Sex
Female 5162 (52.7)
Male 3049 (31.1)
Unknown 1589 (16.2)
Insurance typeb
Medicaid 8370 (85.4)
Medicare 2231 (22.8)
Health Related Social Needs (HRSNs)b
Housing instability and/or quality 2876 (29.3)
Food insecurity 3780 (38.6)
Transportation 2722 (27.8)
Diculty paying utility bills 2582 (26.3)
Any core need 5588 (57.0)
All core needs 813 (8.3)
Health System
Health System A 8211 (83.8)
Health System B 1108 (11.3)
Health System C 481 (4.9)
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sample of dually covered beneciaries was too low to allow splitting between test, training and validation datasets
(< 5% of the overall sample), we felt using Medicaid status represented a reasonable baseline hypothesis to apply.
What our ndings indicate is that the relationship of social needs to insurance status may be more sensitive than
previous literature has been able to detect without screening tools. e AHC screening tool recently underwent
psychometric testing and was found to have concurrent validity and be sensitive for detecting social needs across
a wide swath of patients60. When compared to other tools, AHC was more sensitive to detecting certain social
needs including housing instability.
Our study adds to the growing literature on the application of machine learning and integration of commu-
nity SDOH data for use in healthcare settings39. Studies to date have found mixed results when adding SDOH
data, with some reporting minimal to no improvements in model prediction43. Similar to these studies, we
found that the addition of SDOH data led to very little improvements to model performance for HRSN status
with the exception of diculty paying utility bills. As other authors have identied, there may be a number
of reasons why community-level SDOH are not good predictors of HRSNs. First, while our study included a
large population of patients, their geographic locations were clustered into a small number of Census Tracts.
e similarity of demographic and SDOH factors resulting from relatively few Census Tracts may have limited
discriminatory power43. As noted in previous studies40, the SDOH factors could be correlated with the patient-
level demographics and health status in existing EHR data, therefore, adding limited predictive power in the
models from community-level SDOH.
Additionally, we only examined four of the domains of HRSNs, thus it is possible that the SDOH and EHR
data might have predictive power in other social need domains such as nancial health, social isolation, com-
munity safety, and health literacy61–63. While the SDOH data from community sources andHRSN screening data
measure dierent constructs, dierent levels of the associated constructs or dierent time periods for associated
constructs, might impact discriminatory power. For example, diculty paying utility bills conceptually aligns
with SDOH community-level socioeconomic status, particularly income and poverty. e AHC Model survey
question asks about diculty paying bills for the previous 12months. Whereas, questions from the survey for
housing ask about today. Future studies using survey design methods to consider variation in constructs, levels of
measurement, and impact of time period assessed are warranted. Lastly, a larger, more geographically and socially
diverse sample could be valuable to future modeling eorts to determine if SDOH are truly predictive or not for
HRSN status. It is also possible that the high rate of social needs observed in our population limited discrimi-
natory power. is is similar to previously published studies showing high rates of HRSN in ED populations34.
Figure3. Machine learning model predictive value by HRSN status.
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Our study oers a number of strengths to the existing literature on the application of machine learning models
for predicting HRSNs. To our knowledge this is the rst study to directly model HRSN status using publicly
available data, EHR data, and individual level HRSN screening data. We utilized data from three large health
systems representing patients from the largest medical center in the world. e EHR included both ambulatory
and inpatient visit data in addition to patient demographics. We used individual level data on a large number of
patients who were screened for HRSN through a universal oer to screen. We used readily available EHR and
public SDOH data to model HRSN status making our approach easily replicable by other researchers and health
systems. We also compared our ndings with Medicaid insurance status as a baseline assumption and potential
proxy for HRSN status. Previous studies have found strong associations between Medicaid coverage, social
needs, and healthcare utilization and outcomes64. Finally, we used state of the art Gradient Boosting Decision
Tree ML approaches whose hyperparameters where automatically tuned with Bayesian optimization without
using a non-overlapping test set. is allowed for a fully unbiased ne tuning of the algorithm to each HRSN
without direct trial and error which could lead to overtting.
Using community-level SDOH data to predict individual HRSN status collected via screening is prone to
limitations and potential biases. First is the risk of ecologic fallacy, where assumptions made about individuals
using aggregate-level (area) data yield incorrect results65. Despite the value of using such data to predict HRSN
status, our study adds to previously published ndings that the ecological fallacy may be a limitation to the
utility of such eorts.
Second, a potential limitation is the use of individual level HRSN screening data collected via self-report.
Self-reported data are prone to bias. Characteristics may dier from those who agreed to answer the HRSNs
questions versus those who declined, though our high survey completion rate (~ 45%) lessens this likelihood.
Patients might also underreport HRSNs because of social stigma, social desirability bias, or lack of perceived
benet of reporting needs (i.e., access to navigation services66).
Lastly, additional limitations relate to the selected SDOH data used in our study. We selected community-
level SDOH factors based on previously published studies. However, there is a vast diversity of secondary data
available and it is possible that there is an unmeasured and un-modeled SDOH factor, which could improve
predictive performance40.
ere are implications from this study for healthcare providers and institutions. First, targeting those patients
with the highest social and health needs could help improve patient health and healthcare utilization. A previous
study has shown that social interventions targeting high-utilizing patient populations decreased overall health-
care utilization with more signicant eects seen in low–socioeconomic status patients67. In terms of hospital
utilization, a dose–response relationship has been reported between HRSNs and hospital readmission68. is
further highlights the need to understand and intervene on high-utilizing populations with social needs.
Second, there is a need to identify how to best screen patients for social needs while reducing clinic burden
across healthcare setting types. Machine learning methods can help prioritize patients for HRSN screening
while reducing clinic burden39. Predicting HRSNs could reduce the need for additional data collection, EHR
infrastructure, sta time, and training needed to oer the screening69. However, we did not have the ability to
screen out any patient group or target people for future intervention without the risk of missing or excluding
people. It is dicult to dene a threshold for predictive accuracy that would be acceptable. Dierent accuracy
thresholds may be acceptable depending on multiple factors including the specic use case (e.g., prioritizing
screening vs. excluding a subpopulation from screening), institutional resources, and other factors. Based on our
model prediction and AUC, our results indicate that providers need to continue to use universal oer to screen
approaches while more research is conducted on how to best model social needs status and on the clinical and
cost eectiveness of social needs screening across healthcare settings27. Ideally, a future analysis would apply data
from all 28 AHC Model sites in the US coupled with their EHR data to provide a large and geographically diverse
enough sample to test the potential predictive power and application of risk modeling for HRSNs.
ird, the integration of SDOH with EHR data has implications for healthcare institutions with the shi to
value-based care in the U.S.39. e use of community and individual level data could help identify factors associ-
ated with social needs to improve healthcare utilization and health outcomes.
We examined the predictive power of HRSN status using community-level SDOH data with individual patient
EHR data. We found the addition of SDOH data led to very little improvement in model performance, with the
exception of the presence of a utility need. Models trained with EHR and SDOH data performed better than
Medicaid insurance statusalone. However, screening only these patients identied by the better performing
models would miss many patients with HRSNs. Future studies should examine variation of SDOH and EHR data
in a geographically broader patient sample to identify possible model enhancements to predict HRSN status and
prioritize patients for social interventions.
Data availability
e AHC and EHR datasets generated during and/or analyzed during the current study are not publicly available
due to identifying beneciaries and clinical site information, but are available from the corresponding author
on reasonable request.
Received: 24 September 2021; Accepted: 3 March 2022
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Acknowledgements
e authors would like to acknowledge the participating AHC Greater Houston area clinical delivery sites.
is work was supported by CMS,the Cullen Trust for Healthcare, the National Center for Advancing Transla-
tional Sciences (NCATS) under awards UL1TR000371 and U01TR002393; the Cancer Prevention and Research
Institute of Texas (CPRIT), under award RP170668, and the Reynolds and Reynolds Professorship in Clinical
Informatics.
Author contributions
All authors contributed to the design of the work. J.H and L.H wrote the main manuscript. L.G and L.C.O con-
ducted the data analysis and wrote the results section of the manuscript. All authors revised the manuscript and
approved the nal version.
Funding
is publication was supported by the Centers for Medicare and Medicaid Services (CMS) of the U.S. Depart-
ment of Health and Human Services (HHS) as part of a nancial assistance award totaling $529,632 with 10%
percentage funded by CMS/HHS and 90 percentage funded by non-government source(s), the Cullen Trust
for Healthcare. is work was supported in part by the National Center for Advancing Translational Sciences
(NCATS) under awards UL1TR000371 and U01TR002393; the Cancer Prevention and Research Institute of
Texas (CPRIT), under award RP170668, and the Reynolds and Reynolds Professorship in Clinical Informatics.
Competing interests
e authors declare no competing interests.
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Additional information
Supplementary Information e online version contains supplementary material available at https:// doi. org/
10. 1038/ s41598- 022- 08344-4.
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