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OUTLINE

 1.  Abstract
 2.  
 3.  KEYWORDS
 4.  List of abbreviations
 5.  Methods
 6.  Results
 7.  Discussion
 8.  Conclusions
 9.  Suppliers
 10. References

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FIGURES (1)

 1. 




TABLES (4)

 1. Table 1
 2. Table 2
 3. Table 3
 4. Table 4




ARCHIVES OF REHABILITATION RESEARCH AND CLINICAL TRANSLATION

Available online 14 September 2024, 100370
In Press, Corrected ProofWhat’s this?

ORIGINAL RESEARCH
ASSOCIATION OF RATE OF FUNCTIONAL RECOVERY WITH THERAPY TIME AND CONTENT AMONG
ADULTS WITH ACQUIRED BRAIN INJURIES IN INPATIENT REHABILITATION

Author links open overlay panelAlison M. Cogan PhD, OTR/L a b, Pamela Roberts
PhD, OTR/L, SCFES, FAOTA, CPHQ, FNAP, FACRM b c d e f, Trudy Mallinson PhD,
OTR/L, FAOTA, FACRM g
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https://doi.org/10.1016/j.arrct.2024.100370Get rights and content
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ABSTRACT


OBJECTIVE

To examine associations among the time and content of rehabilitation treatment
with self-care and mobility functional gain rate for adults with acquired brain
injury.


DESIGN

Retrospective cohort study using electronic health record and administrative
billing data.


SETTING

Inpatient rehabilitation unit at a large, academic medical center.


PARTICIPANTS

Adults with primary diagnosis of stroke, traumatic brain injury, or nontraumatic
brain injury admitted to the inpatient rehabilitation unit between 2012 and 2017
(N=799).


INTERVENTIONS

Not applicable.


MAIN OUTCOME MEASURES

Gain rate in self-care and mobility function, using the Functional Independence
Measure. Hierarchical regression models were used to identify the contributions
of baseline characteristics, units, and content of occupational therapy,
physical therapy, and speech-language pathology treatment to functional gain
rates.


RESULTS

Median length of rehabilitation stay was 10 days (interquartile range, 8-13d).
Patients received an mean of 10.62 units of therapy (SD, 2.05) daily. For
self-care care gain rate, the best-fitting model accounted for 32% of the
variance. Occupational therapy activities of daily living units were positively
associated with gain rate. For mobility gain rate, the best-fitting model
accounted for 37% of the variance. Higher amounts of physical therapy bed
mobility training were inversely associated with mobility gain rate.


CONCLUSIONS

More activities of daily living in occupational therapy is associated with
faster improvement on self-care function for adults with acquired brain injury,
whereas more bed mobility in physical therapy was associated with slower
improvement. A potential challenge with value-based payments is the alignment
between clinically appropriate therapy activities and the metrics by which
patient improvement are evaluated. There is a risk that therapists and
facilities will prioritize activities that drive improvement on metrics and
deemphasize other patient-centered goals.



KEYWORDS

Brain injuries
Electronic health records
Recovery of function
Rehabilitation
Stroke


LIST OF ABBREVIATIONS

ABI
acquired brain injury
ADL
activities of daily living
CMS
Center for Medicare and Medicaid Services
CPT
current procedural terminology
EHR
electronic health record
FIM
Functional Independence Measure
IADL
instrumental activities of daily living
IRF
inpatient rehabilitation facility
LOS
length of stay
OT
occupational therapy
PT
physical therapy
SLP
speech-language pathology
The Centers for Medicare and Medicaid Services (CMS) in the United States is
moving from volume to value-based purchasing of postacute care services. The
purported goal of value-based payment models is to reward health care providers
with incentive payments for quality of care, to support improved population
health, and reduce costs.1 In theory, value-based payments will ensure that
patients receive the optimal amounts of rehabilitation services to produce
positive functional outcomes. There is a crucial need for evidence to support
decisions about the type and volume of therapy services each patient receives so
that each facility can make informed rehabilitation care plan decisions that are
likely to lead to functional improvements.2
In practice, value-based models transfer the risk for therapy costs and patient
functional outcomes to providers.3 The shift from volume to value-based
purchasing is exemplified by the implementation of the Patient Driven Payment
Model in skilled nursing facilities in the United States in October 2019.4 Since
its implementation, studies have shown that skilled nursing facilities have
reduced the amount of therapy services provided to their clients and decreased
the number of therapy staff employed.5, 6, 7, 8 It is unclear how these changes
have impacted patient functional outcomes. CMS plans to implement a value-based
purchasing approach for postacute care in inpatient rehabilitation facilities
(IRFs) in the future.9
Currently, IRFs are required to provide 3 hours of rehabilitation services
(occupational therapy [OT], physical therapy [PT], speech-language pathology
[SLP]) for a minimum of 15 hours per week regardless of primary diagnosis.10
Within each therapy service, there are a variety of codes, known as current
procedural terminology (CPT) codes, that are used for billing purposes to
reflect the types of treatment used. Current CMS policies for IRFs mandate that
patients require at least OT and PT services, but do not specify how that
therapy time should be allocated.11
Prior research suggests volume of services alone is insufficient for
understanding patients’ functional recovery.12, 13, 14, 15 Functional recovery
for some health conditions, such as orthopedic surgery, shows little
relationship with therapy time and may be better predicted by baseline function
and length of stay (LOS).16,17 The IRF service model, in which all patients
receive the same volume of therapy, will not be sustainable in a value-based
payment system; facilities will need to minimize low-value therapy activities.18
There is currently limited evidence on which rehabilitation therapists and
managers can base decisions about the time, frequency, and content of therapy to
support optimal functional recovery.2 In this policy environment, it is critical
to be able to identify the type and amount of rehabilitation treatments that are
associated with the greatest and fastest functional gain for patients receiving
care in IRFs, while concurrently managing staffing needs and costs.
The purpose of this study is to examine the associations between the time and
content of rehabilitation treatment with the rates of self-care and mobility
functional recovery among adults with acquired brain injury (ABI) in a single
IRF. CPT codes for OT, PT, and SLP services were used to represent therapy
content. We hypothesized that OT volume and therapy content as noted in CPT
codes would be positively associated with self-care gain rate, and that PT time
and therapy content as noted in CPT codes would be positively associated with
mobility gain rate.


METHODS

This is a retrospective cohort study using electronic health record (EHR) and
billing data from 2012 to 2017 from an inpatient rehabilitation unit at a large,
urban academic medical center. This study was approved by the institutional
review boards of Cedars Sinai and the University of Southern California.
Informed consent was not applicable because data were collected as part of usual
care.


PARTICIPANTS

Patient records were included for first admissions to the inpatient
rehabilitation unit for a primary diagnosis of stroke, traumatic brain injury,
or nontraumatic brain injury between 2011 and 2017. Participants were excluded
from analysis if they did not have any billed therapy services (ie, admitted to
unit but transferred or discharged before commencing treatment) or expired prior
to discharge.19 There was no minimum LOS for inclusion. All patients were
evaluated with the Functional Independence Measure (FIM) at admission and
discharge by the interdisciplinary team consisting of rehabilitation nursing,
PT, OT, and SLP as part of usual care. There were no missing FIM data at either
admission or discharge.


DATA SOURCE

Data sources included archived EHRs and billing records. Demographic
characteristics, diagnosis, case mix group, comorbidities, LOS, FIM at admission
and discharge, and discharge location were extracted from the EHR and therapy
volume (units) and content (CPT codes) from billing records. Data sets were
merged based on matching patient identifier, date of birth, and date of
admission before being deidentified for analysis. The data cleaning procedures
were previously described which had been undertaken to ensure there were no
duplicate records and that all variables satisfied conformance, completeness,
and plausibility.20,21


OUTCOME MEASURES

The FIM is well established as a valid and reliable measure for adults with
ABIs.22, 23, 24, 25 Self-care and mobility gain rates: gain rates (functional
change per day) were calculated by totaling the FIM items separately for
self-care and mobility at admission and discharge; transforming scores to a
Rasch-based equal interval scale (0-100)26; calculating the difference from
admission to discharge; and dividing the difference by LOS days to reflect unit
change per day.13,16,17 The advantage of using the Rasch-transformed values is
that they are based on a continuous, equal interval scale, in contrast with the
ordinal raw scores.27 Self-care items included eating, grooming, bathing,
dressing upper body, dressing lower body, and toileting. Mobility items included
tub transfer or shower transfer, bed-chair transfer, toilet transfer, walking or
wheelchair, and climbing stairs.26


INDEPENDENT VARIABLES

The primary independent variable of interest was volume of therapy by content
type, as represented by CPT codes.28 Volume of therapy services was calculated
in billing units, which are approximately 15-minute increments. For example,
53-67 minutes of a particular service is billed as 4 units of that service.
Total billed therapy units per person were calculated for each CPT code. Mean
daily time per CPT code was calculated by dividing the respective totals by LOS
days. Evaluation time was excluded. CPT codes by discipline are summarized in
figure 1.
 1. Download: Download high-res image (635KB)
 2. Download: Download full-size image

Fig 1. Current procedural terminology codes by discipline with example treatment
activities. ADL, activities of daily living; IADL, instrumental activities of
daily living.


DATA ANALYSIS

All analyses were conducted using Stata 18.a Descriptive statistics were used to
characterize patient demographics by sex and distribution of therapy activities.
Separate hierarchical regression models were used to evaluate the association of
therapy time and content with gain rate separately for self-care and mobility,
respectively. The order of entering covariates into each model was based on our
hypotheses about which therapy content would be most associated with the
outcome, after controlling for age and functional status at admission.
Therefore, OT-related therapy content was added first in the self-care model,
and PT-related therapy content was added first in the mobility model. Iterations
accounted for additional therapies. Akaike information criterion and Bayes
information criterion were calculated for each model iteration to support
identification of the best-fitting model.29


RESULTS

Out of 799 people in the initial cohort, 763 had billed therapy time and
complete, valid records. Most of the samples were White older adults with
Medicare fee-for-service insurance who were treated for a primary diagnosis of
ABI-stroke. Participant characteristics are detailed in table 1. Table 2
summarizes self-care and mobility measures at admission and discharge.

Table 1. Participant characteristics

VariableOverall (N=799)Male (n=412; 52%)Female (n=387; 48%)Mean age at admission
± SD (y)69.68 ± 16.1368.9 ± 16.370.5 ± 15.9Race/ethnicity, n (%) White604
(75.6%)336 (81.6%)268 (69.3%) Black113 (14.1%)42 (10.2%)71 (18.4%) Asian63
(7.9%)26 (6.3%)37 (9.6%) Hispanic19 (2.4%)10 (2.4%)9 (2.3%)Marital status, n
(%) Married427 (53.4%)277 (67.2%)150 (38.8%) Widowed114 (14.3%)24 (5.8%)90
(23.3%) Separated6 (1.0%)1 (.2%)5 (1.3%) Divorced72 (9.1%)21 (5.1%)51
(13.2%) Never married180 (22.5%)89 (21.6%)91 (23.5%)Primary diagnosis, n
(%) Stroke485 (60.7%)232 (56.3%)253 (65.4%) Traumatic brain injury88 (11.0%)60
(14.6%)28 (7.2%) Nontraumatic brain injury226 (28.3%)120 (29.1%)106 (27.4%)Lived
at home (before admission), n (%)795 (99.5%)411 (99.8%)384 (99.2%)Preadmission
living with, n (%) Alone166 (21.1%)61 (15.0%)105 (27.6%) Family/relative571
(72.6%)327 (80.5%)244 (64.0%) Friends14 (1.8%)6 (1.5%)8 (2.1%) Attendant21
(2.7%)4 (1.0%)17 (4.5%) Other15 (1.9%)8 (2.0%)7 (1.8%)Discharge living
situation, n (%) Home356 (44.6%)192 (46.7%)164 (43.4%) Skilled nursing
facility51 (7.1%)24 (8.3%)27 (7.0%) Home with home health329 (41.2%)157
(38.2%)172 (44.4%) Other/not listed62 (7.8%)38 (9.2%)24 (6.2%)Discharge living
with, n (%) Alone20 (2.5%)8 (1.9%)12 (3.1%) Family/relative459 (57.4%)247
(60.0%)212 (54.8%) Friends12 (1.5%)5 (1.2%)7 (1.8%) Attendant42 (5.3%)13
(3.1%)29 (7.5%) Other19 (2.4%)12 (2.9%)7 (1.8%) Unknown247 (30.9%)127 (30.8%)120
(31.0%)Insurance type, n (%) Medicare FFS513 (64.2%)251 (60.1%)262
(67.7%) Medicare Advantage25 (3.1%)11 (2.7%)14 (3.6%) Private261 (32.7%)150
(36.4%)111 (28.7%)Length of stay (d), median (IQR)10 (8-13)11 (9-14)10 (7-11)

Abbreviations: FFS, fee for service; IQR, interquartile range.

Table 2. Self-care and mobility measures at admission and discharge.

VariableOverall (n=763)Male (n=387)Female (n=376)Self-care
measure Admission49.62 ± 8.7750.23 ± 8.4149.00 ± 9.09 Discharge60.65 ± 8.8960.69
± 8.8160.61 ± 8.99Mobility measure Admission44.56 ± 8.5644.95 ± 8.8444.16 ±
8.25 Discharge57.35 ± 7.4657.63 ± 7.3457.06 ± 7.58

Data are reported as mean ± SD.


THERAPY TIME AND CONTENT

Participants received a mean of 10.62 (SD, 2.05) units (equivalent to
approximately 159min) of therapy per LOS day. Therapy content was distributed
across OT activities of daily living (ADL)/instrumental activities of daily
living (IADL) training (mean, 2.79units/LOS d; SD, 1.00); OT therapeutic
activities (mean, 0.77units/LOS d; SD, 0.56); OT therapeutic exercise (mean,
0.44units/LOS d; SD, 0.46); PT bed mobility training (mean, 0.60units/LOS d; SD,
0.44); PT gait training (mean, 1.47units/LOS d; SD, 0.62); PT therapeutic
exercise (mean, 0.83units/LOS d; SD, 0.54); PT neuromuscular reeducation (mean,
0.88units/LOS d; SD, 0.70); SLP cognitive/communication training (mean,
0.86units/LOS d; SD, 0.82); SLP dysphagia treatment (mean, 0.40units/LOS d; SD,
0.55); and SLP voice treatment (mean, 0.56units/LOS d; SD, 0.92).


SELF-CARE GAIN PER DAY

Hierarchical models for self-care gain per day are presented in table 3. The
best-fitting model, which explained 32% of the variance in the outcome, included
age and self-care function at admission, LOS days, OT ADL/IADL training, PT bed
mobility training, and SLP dysphagia. Only OT ADL/IADL training was positively
associated with self-care gain per day (eg, more OT ADL/IADL time was associated
with faster per day improvement on self-care); all other variables were
inversely associated with the outcome. Adding OT therapeutic activity units per
day did not change the explained variance. OT therapeutic exercise was not
significantly associated with self-care gain rate.

Table 3. Hierarchical model for self-care gain per day outcome

ModelVariable Added to ModelAdjusted R2AICBICCoefficient (SE)Age at AdmissionSex
(F)LOSOT ADL/IADLOT Therapeutic ActivityOT Therapeutic ExercisePT Bed MobilityPT
Gait TrainingPT Therapeutic ExercisePT NM Re-edSLP CogSLP DysSLP VoiceEmpty
CellEmpty CellEmpty CellEmpty CellEmpty CellSC Measure ADMEmpty CellEmpty
CellEmpty CellEmpty CellEmpty CellEmpty CellEmpty CellEmpty CellEmpty CellEmpty
CellEmpty CellEmpty CellEmpty Cell1SC Measure at admission + age at admission +
sex0.0624852503-0.03 (0.01)*-0.01 (0.00)†−0.04 (0.09)2LOS0.2722942317−0.07
(0.01)*−0.01 (0.00)*−0.03 (0.08)−0.13 (0.01)*3OT ADL/IADL0.2722962323−0.07
(0.01)*−0.01 (0.00)*−0.03 (0.08)−0.13 (0.01)*−0.00 (0.04)4OT therapeutic
activity0.2722962328−0.08 (0.01)*−0.01 (0.00)*−0.01 (0.08)−0.13 (0.01)*0.01
(0.04)0.11 (0.07)5OT therapeutic exercise0.2722972334−0.08 (0.01)*−0.01
(0.00)*−0.01 (0.08)−0.13 (0.01)*0.01 (0.04)0.11 (0.07)0.03 (0.09)6PT bed
mobility0.3122472289−0.08 (0.01)*−0.01 (0.00)*−0.03 (0.08)−0.11 (0.01)*0.10
(0.04)‡0.11 (0.08)0.10 (0.09)−0.73 (0.10)*7PT gait training0.3122482294−0.09
(0.01)*−0.01 (0.00)*−0.03 (0.08)−0.12 (0.01)*0.09 (0.04)‡0.09 (0.07)0.09
(0.09)−0.73 (0.10)*0.07 (0.06)8PT therapeutic exercise0.3222462297−0.08
(0.01)*−0.01 (0.00)*−0.03 (0.07)−0.12 (0.01)*0.10 (0.04)‡0.09 (0.07)0.12
(0.09)−0.71 (0.10)*0.09 (0.07)−0.14 (0.07)9PT neuromuscular
reeducation0.3222482304−0.08 (0.01)*−0.01 (0.00)*−0.03 (0.07)−0.12 (0.01)*0.10
(0.04)‡0.10 (0.07)0.12 (0.09)−0.71 (0.10)*0.09 (0.07)−0.14 (0.08)−0.01
(0.06)10SLP cognitive-communication0.3222492309−0.09 (0.01)*−0.01 (0.00)†−0.03
(0.08)−0.12 (0.01)*0.09 (0.04)‡0.09 (0.07)0.13 (0.09)−0.71 (0.10)*0.09
(0.07)−0.14 (0.08)−0.01 (0.06)0.05 (0.05)11SLP dysphagia0.3322392304−0.09
(0.01)*−0.01 (0.00)†−0.05 (0.08)−0.12 (0.01)*0.10 (0.04)‡0.09 (0.07)0.10
(0.09)−0.70 (0.10)*0.06 (0.07)−0.15 (0.08)0.01 (0.06)0.03 (0.05)−0.25
(0.07)‡12SLP voice0.3322402309−0.09 (0.01)*−0.01 (0.00)†−0.06 (0.08)−0.12
(0.01)*0.10 (0.04)‡0.09 (0.07)0.09 (0.09)−0.71 (0.10)*0.06 (0.07)−0.16
(0.08)0.01 (0.06)0.01 (0.05)−0.25 (0.07)*−0.04 (0.04)13Best-fitting
model0.3222342266−0.08 (0.01)*−0.01 (0.00)†−0.11 (0.01)*0.08 (0.04)‡−0.71
(0.10)*−0.25 (0.07)*

R2 is the coefficient of determination.
Abbreviations: Ac, activity; ADL, activities of daily living; ADM, admission;
AIC, Akaike information criterion; BIC, Bayes information criterion; Cog,
cognition; Dys, dysphagia; Ex: exercise; F, female; IADL, instrumental
activities of daily living; LOS, length of stay; NM, neuromuscular; OT,
occupational therapy; PT, physical therapy; Re-ed, reeducation; SC, self-care;
SLP, speech-language pathology; Ther, therapeutic.
⁎
P≤.001.
†
P≤.01.
‡
P≤.05.


MOBILITY GAIN PER DAY

Hierarchical models for mobility per day are presented in table 4. The
best-fitting model, which explained 37% of the variance in the outcome, included
age, sex, self-care function at admission, LOS days, PT bed mobility training,
PT therapeutic exercise, and SLP dysphagia. All variables were inversely related
to the outcome (eg, higher PT bed mobility training was associated with slower
per day recovery of mobility function).

Table 4. Hierarchical model for mobility gain per day outcome

Empty CellEmpty CellEmpty CellEmpty CellEmpty CellCoefficient, (SE)Empty
CellEmpty CellEmpty CellEmpty CellEmpty CellEmpty CellEmpty CellEmpty CellEmpty
CellEmpty CellEmpty CellEmpty CellEmpty CellModelVariable Added to ModelAdjusted
R2AICBICMobility measure at admissionAge at AdmissionSexLOSPT Bed MobilityPT
GaitPT Ther ExPT NM Re-edOT ADL/IADLOT Ther AcOT Ther ExSLP CogSLP DysSLP
Voice1Mobility measure at admission + age at admission + sex0.1334933511−0.10
(0.01)*−0.02 (0.01)*−0.35 (0.17)†2LOS0.3232983322−0.20 (0.01)*−0.02 (0.00)*−0.29
(0.15)−0.26 (0.02)*3PT bed mobility0.3632613289−0.22 (0.01)*−0.02 (0.00)‡−0.29
(0.15)−0.26 (0.02)*−1.24 (0.20)*4PT gait training0.3632633295−0.23 (0.01)*−0.02
(0.00)‡−0.29 (0.15)−0.25 (0.02)*−1.25 (0.20)*0.07 (0.13)5PT therapeutic
exercise0.3632613298−0.22 (0.01)*−0.01 (0.00)‡−0.29 (0.15)†−0.26 (0.02)*−1.20
(0.20)*0.11 (0.13)−0.29 (0.14)6PT neuromuscular reeducation0.3632613302−0.22
(0.01)*−0.02 (0.00)‡−0.32 (0.15)†−0.26 (0.02)*−1.20 (0.20)*0.12 (0.13)−0.36
(0.15)†−0.16 (0.12)7OT ADL/IADL0.3632633309−0.22 (0.01)*−0.02 (0.00)‡−0.31
(0.15)†−0.26 (0.02)*−1.20 (0.20)*0.13 (0.13)−0.36 (0.15)†−0.16 (0.12)−0.03
(0.08)8OT therapeutic activity0.3632643309−0.22 (0.01)*−0.02 (0.00)‡−0.30
(0.15)†−0.26 (0.02)*−1.20 (0.20)*0.12 (0.13)−0.37 (0.15)†−0.18 (0.12)−0.02
(0.08)0.06 (0.14)9OT therapeutic exercise0.3632663321−0.23 (0.01)*−0.02
(0.00)‡−0.30 (0.15)†−0.26 (0.02)*−1.22 (0.20)*0.10 (0.13)−0.39 (0.15)†−0.19
(0.12)−0.01 (0.08)0.08 (0.14)0.16 (0.17)10SLP
cognitive-communication0.3632673327−0.22 (0.01)*−0.02 (0.00)‡−0.30 (0.15)†−0.26
(0.02)*−1.22 (0.20)*0.10 (0.13)−0.39 (0.15)†−0.19 (0.12)0.00 (0.08)0.09
(0.14)0.15 (0.17)−0.06 (0.09)11SLP dysphagia0.3732593324−0.23 (0.01)*−0.02
(0.00)‡−0.35 (0.15)†−0.26 (0.02)*−1.21 (0.20)*0.07 (0.13)−0.41 (0.15)†−0.16
(0.12)0.01 (0.08)0.09 (0.14)0.11 (0.17)−0.11 (0.09)−0.44 (0.14)‡12SLP
voice0.3732613330−0.23 (0.01)*−0.02 (0.00)‡−0.36 (0.15)†−0.26 (0.02)*−1.21
(0.21)*0.07 (0.13)−0.41 (0.15)†−0.16 (0.12)0.01 (0.08)0.09 (0.14)0.10
(0.17)−0.13 (0.10)−0.45 (0.14)‡−0.04 (0.09)13Best-fitting model0.3732503288−0.22
(0.01)*−0.01 (0.00)‡−0.33 (0.15)†−0.25 (0.02)*−1.18 (0.20)*−0.31 (0.14)†−0.44
(0.14)‡

R2 is the coefficient of determination.
Abbreviations: Ac; activity; ADL, activities of daily living; AIC, Akaike
information criterion; BIC, Bayes information criterion; Cog, cognition; Dys,
dysphagia; Ex, exercise; IADL, instrumental activities of daily living; LOS,
length of stay; NM, neuromuscular; OT, occupational therapy; PT, physical
therapy; Re-ed, reeducation; SLP, speech-language pathology; Ther, therapeutic.
⁎
P≤.001.
†
P≤.05.
‡
P≤.01.


DISCUSSION

This study used data from EHRs and administrative billing records from an
inpatient rehabilitation unit to examine the association between the time and
content of therapy and self-care and mobility functional outcomes for adults
with ABI. Findings show that, after accounting for baseline age and function,
more OT ADL/IADL training is associated with faster improvement in self-care
function. This finding is likely attributable to good alignment between the
activities included in OT ADL/IADL CPT code and self-care items on the FIM.
Conversely, more time on PT bed mobility training is associated with slower
improvement per day in both self-care and mobility function. This latter finding
may reflect incongruence between time spent on treatments that are clinically
warranted and the outcome metrics by which progress is evaluated. Specifically,
time spent on bed mobility may be necessary to support functional improvement
but may not produce gains on more challenging mobility items, such as walking
and climbing stairs. The results reflect potential challenges of implementing a
value-based payment model, as self-care and mobility metrics have implications
for how therapy time and content in IRFs are distributed.
OT therapeutic exercise was not significantly associated with either self-care
or mobility gain rate. Possible reasons include that (1) exercise was not of
sufficient intensity to invoke change; (2) LOS was not sufficiently long to
observe changes resulting from exercise; (3) exercise did not translate to gain
in functional activities. This result is similar to prior studies that did not
find a significant relationship between the proportion of therapy time dedicated
to upper extremity exercise and independence with functional activities using
the upper extremities.30 Similarly, PT neuromuscular reeducation was not
associated with either self-care or mobility gain rate. It is possible that
these types of therapies had positive effects on other unmeasured outcomes, such
as muscle strength and endurance, but did not produce measurable change in
self-care or mobility function. Thus, a potential risk of value-based payments
is that rehabilitation treatments will be focused on outcomes by which
reimbursement is determined, possibly to the detriment of other patient-centered
goals. Others have called for value-based payment models to integrate patients’
perspectives as a means to understanding the degree to which rehabilitation care
supports a safe return to community living.31
Value-based payment models utilize measures of quality and cost to determine
payment for providers. This approach is intended to hold providers accountable
for improving outcomes while also providing the right care at the right time in
the right amount for the lowest cost.32 Therapists and facilities need to
consider the contributions of rehabilitation interventions to patient outcomes
more broadly than the functional measures on which they typically focus on (ie,
self-care, mobility). The best-fitting models explained 32% of the variation in
self-care and 37% of the variation in mobility, suggesting that factors other
than therapy services contribute to patient functional outcomes.


STUDY LIMITATIONS

This observational study used data from a single-center academic medical center.
Billing records (CPT codes) were used to characterize therapy content. Within
each code, a variety of intervention approaches are possible. Some of the
activities may not have been captured from a billing code if it did not meet the
minimum threshold for a billing unit (<8 minutes). Data about treating
therapists were not available, nor could we discern if the patients saw the same
team of therapists consistently during their admission. There are other valid
CPT codes for inpatient rehabilitation that did not appear in the data set.
Although there are definitions for when each code should be used, it is possible
that there was some variability in application and overlap between disciplines.
Other factors for which we did not have data, such as medical complications and
comorbidities, may influence rehabilitation outcomes and rate of improvement.
Nonetheless, this study offers useful insight into the distribution of therapy
content as represented by CPT codes, and points to a potential misalignment
between the quality metrics used to evaluate functional improvement and the
types of rehabilitation treatments that are clinically appropriate for patients
with ABIs, particularly in the mobility domain.


FUTURE RESEARCH

Future studies should examine site variation in use of CPT codes and
associations with functional outcomes across multiple IRFs. Given the evidence
to support the role of the therapeutic relationship in patient outcomes, the
number of different rehabilitation providers a patient sees during a LOS and its
association with functional outcomes should be studied. Qualitative methods can
be used to investigate variation in the kinds of activities that occur within
each CPT code and their alignment with functional items mandated by CMS in IRFs.
Value is driven by 2 main components: costs and patient outcomes. In adapting to
value-based payment models, IRFs will need to address both. The most obvious way
to cut costs is to reduce the volume of therapy services; this result has been
observed in skilled nursing facilities after implementation of the Patient
Driven Payment Model.5, 6, 7 There is an opportunity for IRFs to find value by
using their data to drive improved patient outcomes, thereby balancing the value
equation and reducing low-value care.18 The adoption of the learning health
system approach across the United States offers the promise of turning practice
data into actionable knowledge to improve outcomes.33 Rehabilitation departments
can contribute to the implementation of learning health systems by identifying
value-generating practices.34 Relying on cost-cutting to drive value could
result in more aggressive patient screening to limit IRF admission for people
who are likely to be discharged to somewhere other than the community (eg,
skilled nursing facility or long-term acute care hospital), potentially
exacerbating health disparities.


CONCLUSIONS

The transition to a value-based payment model in IRFs will shift risks of poor
patient functional outcomes to rehabilitation therapists. Facilities will need
to make decisions about costs and service delivery to produce optimal results
and limit low-value care. This study showed that time spent in OT ADL/IADL
training was associated with faster self-care gains for adults with ABI;
however, in the value-based model, rehabilitation therapists will need to
consider their contributions to patient outcomes more broadly, as well as other
factors that impact functional improvement. In this changing policy context,
rehabilitation teams have an opportunity to utilize their data to support
informed decisions about what kinds of rehabilitation therapy drive improved
patient outcomes, thus generating better value for patients and meeting facility
goals.


SUPPLIERS

a. Stata, version 18; StataCorp.
Recommended articles



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CITED BY (0)


The authors have no conflicts of interest to report.
This work was supported by the American Occupational Therapy Foundation
(AOTFHSR20Cogan). The funder had no role in the study design, data collection,
analysis, interpretation, writing the report, or decision to submit for
publication. Pamela Roberts was also supported by the National Institutes of
Health National Center for Advancing Translational Science (grant no.
UL1TR001881).
© 2024 The Authors. Published by Elsevier Inc. on behalf of American Congress of
Rehabilitation Medicine.


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