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Half of Medicare reimbursement goes toward caring for the top 5% of the most expensive patients. However, little is known about these patients prior to reaching the top or how their costs change annually. To address these gaps, we analyzed patient flow and associated health care cost trends over 5 years.
To evaluate the cost of health care utilization in older patients by analyzing changes in their long-term expenditures.
This was a retrospective, longitudinal, multicenter study to evaluate health care costs of 2643 older patients from 2011 to 2015. All patients had at least one episode of home health care during the study period and used a personal emergency response service (PERS) at home for any length of time during the observation period. We segmented all patients into top (5%), middle (6%-50%), and bottom (51%-100%) segments by their annual expenditures and built cost pyramids based thereon. The longitudinal health care expenditure trends of the complete study population and each segment were assessed by linear regression models. Patient flows throughout the segments of the cost acuity pyramids from year to year were modeled by Markov chains.
Total health care costs of the study population nearly doubled from US $17.7M in 2011 to US $33.0M in 2015 with an expected annual cost increase of US $3.6M (
Although many health care organizations target intensive and costly interventions to their most expensive patients, this analysis unveiled potential cost savings opportunities by managing the patients in the lower cost segments that are at risk of moving up the cost acuity pyramid. To achieve this, data analytics integrating longitudinal data from electronic health records and home monitoring devices may help health care organizations optimize resources by enabling clinicians to proactively manage patients in their home or community environments beyond institutional settings and 30- and 60-day telehealth services.
The United States spends more on health care per person than any other country in the world [
A sizable proportion (20%) of all national health care expenditures are due to Medicare spending, a federal health insurance program for US citizens who are 65 years and older, younger people with certain disabilities, and those who suffer from end stage renal disease [
Unsustainable health care costs and the need to improve overall efficiency is the driving force for the introduction of value-based care, wherein clinicians aim to cost effectively monitor, diagnose, and treat patients. Many health care organizations (HCOs) now use value-based care strategies [
Health care expenditures in the United States are unevenly distributed across individuals and different segments of the population [
The primary aim of this study was to evaluate the health care costs of older patients using PERS over a period of 5 years. Specifically, to answer the following questions:
What is the total health care cost of the study population from fiscal year 2011 to fiscal year 2015 (FY11-FY15) and its distribution across specific cost segments?
Are there longitudinal trends in health care cost across the cost segments?
How many patients are moving up/down the cost segments and how do their health care costs vary annually?
This was a retrospective, longitudinal, multicenter study to evaluate health care costs of inpatient and outpatient hospital encounters in patients using PERS for any length of time during the study period of 5 years (FY11-FY15). The study was conducted using US data and was approved by the Partners Human Research Committee, the Institutional Review Board for Partners Healthcare hospitals.
Study participants were identified from Partners Healthcare at Home (PHH), a home health agency that offers general care as well as specialized services to help patients within the Partners Healthcare System (PHS) network of hospitals to manage chronic conditions while at home. Patients are usually referred to the PHH service by their care providers after discharge from the hospital. In addition to in-person home visits, PHH utilizes a variety of health care technologies to manage their patients. One of these technologies is the Lifeline PERS, which PHH care providers routinely recommend to chronically ill patients who are at risk of falls or other health-related emergencies. Detailed descriptions of PHH and PERS were described in a previous paper [
Subjects included in this study received health care at any of the 5 PHS affiliated hospitals and had at least one inpatient and/or outpatient encounter. Study subjects had at least one episode of PHH care with average duration of 2-3 months and were enrolled to PERS through PHH for any length of time during FY11-FY15. Initially, there were 4290 patients identified as PERS users from the Lifeline database, as illustrated in
The primary data source for this study was the enterprise data warehouse (EDW), an electronic medical record data repository for hospitals within the PHS network. It includes data such as patient demographics, medical conditions, clinical encounters, and health care costs. Health care cost data in EDW is obtained from the PHS costing system (ie, billing and internal cost to the hospital); it does not refer to insurer payment or cost to the patient. “Total cost” is the sum of variable and fixed costs for direct and indirect patient care during hospital inpatient and outpatient encounters. Hospital costing data are divided into fiscal years (FYs), as opposed to calendar years, with the fiscal year beginning Oct 1, (eg, FY11 begins on 2010 Oct 1). All mention of “year” herein refers to the fiscal year.
The PERS database included patient demographics, living situation, caregiver network, self-reported medical conditions, and medical alert data. The latter included all information gathered during the interactions of the patients with Lifeline call center associates when the PERS help button was pressed, including the reasons for pressing and the outcomes of the interactions.
The subject segmentation was based on the following steps performed for each fiscal year (FY11-FY15). Firstly, we selected the patients that had any health care costs in a particular FY from all 2643 patients included in the study. Secondly, we calculated the annual cost of each patient as the sum of the total costs of their inpatient and outpatient encounters. Third, we ranked subjects by their annual health care costs from highest to lowest. Finally, we grouped them into the following segments: T segment constitutes the top 5% (0%-5%) most expensive patients; M segment comprises the middle 45% (5%-50%) of all patients; B segment includes the bottom 50% (50%-100%) least expensive patients. We visualized these 3 segments for each fiscal year by an annual cost acuity pyramid, as illustrated in
To address the aforementioned study objectives, our primary outcomes were to quantify patients who moved up, down, or stayed in the same segment of cost acuity pyramids over a 2-year period and to evaluate the costs associated with these flows.
Prior to analyzing the primary outcomes, we conducted exploratory analyses to evaluate a secondary outcome of the total health care cost of the study population and its distribution across the segments of the cost acuity pyramids for each available fiscal year. In addition, we performed inferential analysis to identify longitudinal trends in the total health care costs of the complete study population and each segment of the cost acuity pyramid.
Demographic and health care utilization data for FY11-FY15 were extracted from EDW using Microsoft Structured Query Language Server Management Studio (SSMS) 2014. Data management and deidentification were achieved through SSMS and Microsoft Excel 2007. The statistical analysis described below was performed via R version 3.4.1 [
To evaluate our primary outcomes, we applied a 3-step analysis, which included the following steps: model the patients’ flow between the T, M, and B segments of the cost acuity pyramid over each 2-year period, group these flows to quantify patients moving up, down, or staying at the same segment of the cost acuity pyramid, and estimate the cost flow associated with the patient flow.
Flow chart diagram of the study subjects.
Cost acuity pyramid based on health care cost in 2015.
Markov chain of the patient flow and associated transition matrix.
To model the patients’ flows in step 1 above, we created a Markov chain of the flow from each segment to all others over 2 successive FYs. A Markov chain describes a sequence of possible events, in which the probability of each event depends only on the state attained in the previous event. Markov chains have been used in the economic evaluation of health care [
To evaluate health care expenditure trends, we conducted linear regression analyses. Four linear regression models were built with health care costs of the total study population and T, M, and B segments as the dependent variables with each available fiscal year serving as the independent variable. Each model provided an estimate of the expected annual cost increases/decreases.
The study population was, on average, 79 years old, predominately female (1990/2643, 75.29%), white (2312/2643, 93.41%), living alone (2483/2643, 93.95%), without family caregivers (2629/2643, 99.47%), and at least 86.70% (1310/1511) had a high school education (
Health care costs were unevenly distributed across the segments of the cost acuity pyramid for each fiscal year. For example, there were 2206 patients with any health care utilization in 2015, as illustrated in
The total health care expenditure of the study population nearly doubled from US $17.7M in FY11 to US $33.0M in FY15, although the number of patients per year having any costs remained similar, as illustrated in
The M segment was the most expensive with total costs increasing from US $9.1M in FY11 to US $18.9M in FY15, as illustrated in
The Markov model of the patient flow throughout the segments of the cost acuity pyramid is illustrated in
An alternative visualization using the cost acuity pyramids is shown in the upper part of
The cost flow associated with the patient flow is depicted in the lower part of
We evaluated the potential demographic differences between patients who moved up, stayed, or moved down the cost acuity pyramid, as detailed in
After quantifying the patient and cost flows throughout the segments of the cost acuity pyramids, we evaluated the primary outcome of how many patients moved up, down, or stayed in the same segment the following year, as illustrated in
Characteristics of the study population.
Variables | Study population (N=2643), mean (SD) or n (%)a | |
Age, mean (SD) | 79 (11) | |
<65 | 303 (11.46) | |
65+ | 2340 (88.54) | |
Female | 1990 (75.29) | |
White | 2312 (93.41) | |
Hispanic | 9 (0.36) | |
Black/African American | 128 (5.17) | |
Other | 26 (1.05) | |
0 | 2629 (99.47) | |
1 | 14 (0.53) | |
Yes | 2483 (93.95) | |
No | 160 (6.05) | |
≥College | 551 (36.47) | |
Some college | 102 (6.75) | |
High school | 657 (43.48) | |
<High school | 201 (13.30) | |
Married | 695 (29.28) | |
Divorced | 317 (13.35) | |
Single | 475 (20.00) | |
Widowed | 887 (37.36) | |
0 | 386 (14.60) | |
1 | 529 (20.02) | |
2 | 562 (21.26) | |
3 | 473 (17.90) | |
≥4 | 693 (26.22) |
aPercentages may not add to 100 due to rounding.
bUnknown: n=168.
cUnknown: n=1132.
dUnknown: n=269.
eSelected medical conditions included disordered lipid metabolism, atrial fibrillation, congestive heart failure, chronic obstructive pulmonary disease, malignant cancer, fractures, pneumonia, obesity, and acute myocardial infarction.
Health care cost trends of total population, top (T), middle (M), and bottom (B) segments from 2011 to 2015.
Patient and cost flows of top (T), middle (M), and bottom (B) segments of cost acuity pyramid.
Characteristics of study population (N=2223) who moved up, stayed, or moved down the cost acuity pyramid from fiscal year 2014 to fiscal year 2015.
Variables | Moved upa | Stayeda | Moved downa | ||||||
Total, N (%) | 403 (18) | 1327 (60) | 493 (22) | ||||||
Age, mean (SD) | 10.9 (78.9) | 11 (78.8) | 11 (78.6) | .91 | |||||
.96 | |||||||||
<65 | 48 (11.91) | 162 (12.21) | 58 (11.76) | ||||||
65+ | 355 (88.09) | 1165 (87.79) | 435 (88.24) | ||||||
.73 | |||||||||
Female | 306 (75.93) | 1000 (75.36) | 364 (73.83) | ||||||
.86 | |||||||||
White | 368 (94.12) | 1169 (92.85) | 433 (92.52) | ||||||
Hispanic | 1 (0.26) | 6 (0.48) | 1 (0.21) | ||||||
Black/African American | 17 (4.35) | 71 (5.64) | 30 (6.41) | ||||||
Other | 5 (1.28) | 13 (1.03) | 4 (0.85) | ||||||
.42 | |||||||||
None | 402 (99.75) | 1319 (99.40) | 492 (99.80) | ||||||
.02 | |||||||||
Yes | 375 (93) | 1255 (95) | 449 (91) | ||||||
.86 | |||||||||
≥College | 98 (38.43) | 282 (37.20) | 104 (35.25) | ||||||
Some college | 19 (7.45) | 38 (5.01) | 21 (7.12) | ||||||
High school | 103 (40.39) | 330 (43.54) | 126 (42.71) | ||||||
<High school | 35 (13.73) | 108 (14.25) | 44 (14.92) | ||||||
.94 | |||||||||
Married | 109 (29.78) | 352 (29.33) | 126 (28.38) | ||||||
Divorced | 49 (13.39) | 166 (13.83) | 65 (14.64) | ||||||
Single | 64 (17.49) | 237 (19.75) | 90 (20.27) | ||||||
Widowed | 144 (39.34) | 445 (37.08) | 163 (36.71) | ||||||
<.01 | |||||||||
0 | 16 (3.97) | 149 (11.23) | 31 (6.29) | <.01 | |||||
1 | 55 (13.65) | 249 (18.76) | 91 (18.46) | .06 | |||||
2 | 94 (23.33) | 297 (22.38) | 112 (22.72) | .92 | |||||
3 | 92 (22.83) | 245 (18.46) | 109 (22.11) | .07 | |||||
≥4 | 146 (36.23) | 387 (29.16) | 150 (30.43) | .03 |
aPercentages may not add to 100 due to rounding.
bUnknowns: moved up: n=12; stayed: n=68; moved down: n=25.
cUnknowns: moved up: n=148; stayed: n=569; moved down: n=198.
dUnknowns: moved up: n=37; stayed: n=127; moved down: n=49.
eSelected medical conditions included disordered lipid metabolism, atrial fibrillation, congestive heart failure, chronic obstructive pulmonary disease, malignant cancer, fractures, pneumonia, obesity, and acute myocardial infarction.
Patients moving throughout the cost acuity pyramid and associated cost flow.
This study is the first to quantify patients’ annual movements through the segments of the cost acuity pyramid and associated changes in health care costs. We discovered 3 main findings. First, the total health care cost of the study population doubled from US $17.7M to US $33.0M (FY11-FY15) with an expected annual increase of US $3.6M (
Our first finding is consistent with those of the prior studies characterizing high-cost users as predominantly older patients with functional limitations and multiple chronic conditions [
The second finding that the M segment (not the T segment) of the cost acuity pyramid was the most expensive each year is a new insight that reveals the importance of the M segment for cost management. Currently, most HCOs develop population health management programs targeting the T segment of the cost acuity pyramid [
The third finding illustrates how health care expenditures of the different segments of the cost acuity pyramid changed over the 2-year period. Previous work [
In evaluating potential group differences in patients who moved up, stayed, or moved down the cost acuity pyramids, we observed that patients who stayed in the same segment were more likely to live alone and to have fewer comorbid conditions. Patients who moved up the cost acuity pyramid had the highest proportion of comorbid conditions. Future work will examine additional patient characteristics.
In summary, our findings demonstrate that a holistic cost management approach is needed to attenuate the overall increases in total health care costs, taking into account the dynamic flows between all segments of the cost acuity pyramid, rather than the T segment only. This approach would target interventions to patients at risk of moving up the cost acuity pyramids.
This study had a number of limitations. Firstly, PERS used by this population was self-paid and may limit the study generalizability to patients that could afford the service. Secondly, our analyses did not include the costs of patients’ clinical encounters that may have occurred outside the Partners Health care network. Further, information about patients’ alignment with insurers accepted by PHS at the time of their health care utilization was not available because the dataset was derived from EHR, rather than the insurance claims. Thirdly, other types of health care costs, such as skilled nursing facilities and home health agencies, are not included in our analysis because of data unavailability. Finally, this analysis was conducted using US data from the PHH population; therefore, other population results may vary.
Future work will investigate which patient characteristics have the potential to predict patient flow from year to year, including hospital utilization, encounter-level principal diagnoses and procedures, in addition to the patient demographics evaluated herein. We will also evaluate whether these characteristics are static or dynamic over time. Additionally, we will conduct a prospective study to evaluate the cost savings of disease management programs for older patients using PERS and CareSage as a long-term home monitoring service [
Although many HCOs target intensive and costly interventions to their most expensive patients, this analysis unveiled potential cost savings opportunities by managing the patients in the lower cost segments that are at risk of moving up the cost acuity pyramid. Accordingly, HCOs should prioritize population health management programs able to identify patients at risk of moving up the cost acuity pyramid and provide interventions tailored to a patient’s specific problem, which might be related to frequent ED transports/visits, medication nonadherence, or lack of social support. To achieve this, data analytics integrating longitudinal data from the EHRs and home monitoring devices may help HCOs optimize resources by enabling clinicians to proactively manage patients in their home or community environments, beyond institutional settings, and in 30- or 60-day telehealth services.
Centers for Medicaid and Medicare Services
emergency department
enterprise data warehouse
electronic health record
fiscal year
health care organizations
personal emergency response service
Partners Healthcare at Home
Partners Healthcare System
structured query language server management studio
The authors wish to thank Hans-Aloys Wischmann, PhD, for his invaluable advice and edits in reviewing this manuscript.
All authors read and approved the final manuscript. SA, MS, SG, JodB, and LS designed the research. MS and SG performed the statistical analyses; SA, LS, JK, KJ, AO, NF, and JF provided feedback on analyses and interpretation of results; MS, SG, SA, JF, NF, and JodB wrote the paper; and SA had primary responsibility for the final content.
Philips provided funding for this study and four Philips employees (MS, JB, LS and AO) played a role in the design of the study, data collection, analysis, interpretation and writing of the manuscript.