Keywords goals-of-care discussions - inpatient mortality model - mortality risk stratification
- clinical decision support - electronic health records
Background and Significance
Background and Significance
Early referral to palliative and hospice care has been found to improve the quality
of end-of-life care and decrease readmission rates, length of hospital stay, and health
care costs for seriously ill patients.[1 ]
[2 ] Despite these known benefits, the timing and frequency of palliative care and hospice
care consultations vary widely due to their reliance upon clinicians to promptly identify
end-of-life care needs.[3 ] The ability to recognize these needs and conduct goals-of-care discussions (GOCD)
is even more challenging for transferred patients at tertiary hospitals due to the
lack of continuity of care, advanced patient condition at initial presentation, and
the need to discuss sensitive matters in acute crises with little time to establish
rapport with patients and families as depicted in [Fig. 1 ].[4 ]
[5 ] Clinicians have expressed interest in supplementing their clinical judgment with
robust clinical prediction models to increase their prognostic confidence,[4 ]
[6 ]
[7 ] and strategies have been proposed to aid palliative and hospice care consultations
for seriously ill transferred patients with limited life expectancy.[8 ]
[9 ]
[10 ] Mortality risk stratification, a systematic technique for categorizing patients'
risk of death based on health status and other factors, accompanied by innovative
tools to facilitate clinicians' use of assessed risk and guide end-of-life care, is
essential for these patients.[11 ]
Fig. 1 Patient trajectory from an outside facility to Indiana University Health Academic
Center leading to discharge or death. *Key decision points are where goals-of-care
discussions (GOCD) can occur. The preferred outcome is an early GOCD in the trajectory
to provide patient-preferred goal-congruent care. The green box shows preferred outcomes
with early GOCD and discharge to the preferred location. The yellow boxes show outcomes
that can be improved. The red boxes indicate a poor outcome.
The rapid growth of data science combined with the wide use of electronic health records
(EHRs) allows the timely identification of patients for various purposes using predictive
analytics.[12 ]
[13 ] Numerous machine learning models have been developed to predict hospitalized patients'
risk of mortality and other adverse health outcomes.[14 ]
[15 ] However, these models are disease-specific,[16 ] confined to intensive care unit (ICU) patients, exclusively predict postdischarge
mortality,[17 ] or lack prospective evaluation and external validation.[18 ]
[19 ]
[20 ] As a result, there is limited evidence that these mortality prediction models could
benefit clinician decision-making or improve clinical outcomes in a heterogeneous
group of hospitalized patients. A prospective evaluation of the performance of these
models using real-world operational data is needed to evaluate their impact on patient
care.[16 ]
[21 ]
[22 ]
[23 ]
We recently developed a model that predicts 30-day inpatient mortality among transferred
patients based on a retrospective cohort study that examined both administrative and
clinical data from 10,389 patients within 24-hour transfer to our medical center.
Twenty candidate variables associated with mortality were identified from the EHR.
These variables underwent multiple logistic regression and area under the curve-receiver
operating characteristic (AUC-ROC) analysis in a derivation sample (n = 5,194) to determine an optimal risk threshold score and develop the model. The
final model was validated in a separate sample of patients (n = 5,195), and it demonstrated strong discrimination (C-statistic = 0.90) and good
fit. The positive predictive value for 30-day in-hospital death was 68%, with an AUC-ROC
of 0.90. A risk threshold score of −2.19 exhibited maximum sensitivity (79.87%) and
specificity (85.24%) in the derivation and validation sample (sensitivity: 75.00%,
specificity: 85.71%). A complete description of the model's development and evaluation
are published elsewhere.[24 ]
In this study, we hypothesized that an intervention involving real-time communication
of our model's all-cause 30-day inpatient mortality risk with the primary hospitalists
could promote early GOCD in seriously ill transferred patients.[24 ]
Objectives
Our primary objective was to examine the effects of sharing model-generated mortality
risk with hospitalists by assessing (1) if they agreed with the mortality risk, (2)
if they planned to conduct GOCD or consult palliative care within 72 hours of transfer,
(3) if the communication alert affected GOCD timing and other clinical outcomes. We
also aimed to measure the association between both the model-generated and hospitalists'
stratified risk assessment with patient mortality.
Methods
This was a nonrandomized quasi-experimental study incorporating historical controls.
The Indiana University Institutional Review Board categorized this study as a quality
improvement project under expedited review resulting in waiving the requirement for
informed consent. Prior to the study period, we provided an educational session to
all hospitalists at the study site about the upcoming pilot and implementation of
the mortality risk model. We discussed how the model was developed and shared all
the variables that were included in calculating the mortality risk score.[24 ] All hospitalists provided verbal consent prior to the initiation of this pilot study.
Clinical Setting
This pilot study was conducted at one of two hospitals in a large, Midwest academic
medical center. The academic health center admits about 38,000 patients annually,
with about 50% of patients transferred from outside hospitals. The hospitalist service
at the study hospital consists of six teams (Red, Blue, Green, Purple, Orange, and
Yellow), each with two hospitalists working on a 7-day-on, 7-day-off schedule. As
a result, one hospitalist from each team is always present at the facility. Hospitalists
on the Red team did not participate as they were primary researchers in this study
and Orange team hospitalists could not participate as they were engaged in another
study. Concurrent with this study, due to coronavirus disease 2019 (COVID-19) pandemic,
the study hospital was designated as the default hospital for patients admitted through
the academic medical center's emergency department (ED). This resulted in increasing
bed scarcity, more internal admissions, and ultimately fewer external patient transfers.
The hospitalist workforce was reinforced with the incremental deployment of locum
teams to manage the increased patient census.
Participants
The process of participant recruitment, data collection, and analysis are depicted
in [Fig. 2 ]. Patients were eligible for inclusion if they were general medical patients, were
18 years of age or older, transferred from outside facilities, admitted to the hospitalist
service, had decision-making capacity or an assigned health care representative/surrogate,
and were identified by our model to be at risk for 30-day inpatient mortality.[24 ] Recruitment was limited to patients admitted between Saturday 1:00 a.m. and Friday
8:00 a.m. during the intervention period to align with our measurement of whether
palliative care consultations occurred within 72 hours of admission as there are no
palliative care consultations at the hospital over the weekend. Patients were excluded
if they were admitted to locum hospitalist teams, if the primary hospitalist teams
were primary researcher for this study or if they were involved in concurrent research
studies (i.e., Red and Orange teams), if the patients had been transferred directly
to our ICU on admission, if the patients were admitted from our ED, if the patients
died within 24 hours of transfer, and if they had a documented GOCD or palliative
care consultation at an outside hospital before transfer without any changes to the
plan of care. We excluded patients who were admitted from our ED as the model was
developed specifically for transferred patients and did not include patients admitted
directly from our ED. We excluded patients who were admitted directly to our ICU,
as our ICU is a closed unit and hospitalists do not have the opportunity to evaluate
patients in ICU.
Fig. 2 Process of recruitment, data collection, and analysis.
Historical controls were selected to evaluate the intervention's effects within a
real-world, nonexperimental context. The historical control group was retrospectively
identified among patients transferred to the hospitalist service from July 1, 2021,
to July 30, 2021 and met the inclusion and exclusion criteria.
The Intervention
The intervention group was recruited from August 1, 2021, to January 30, 2022. Recruitment
continued until at least 40 patients or 10 patients from each of the four participating
hospitalist teams were reached. Investigators screened eligible patients by reviewing
their record and calculating 30-day inpatient mortality risk according to the model[24 ] within 24 hours of hospitalization. The primary hospitalist was notified on the
second day of hospitalization if a patient met the threshold for inpatient mortality
risk. Notifications were sent using the HIPAA (Health Insurance Portability and Accountability
Act)-compliant mobile communication system, Diagnotes. [Fig. 3 ] presents the initial communication and series of questions asked to hospitalists
in Diagnotes. After evaluating the patient, the primary hospitalist answered the series
of questions in Diagnotes.
Fig. 3 Inpatient mortality risk and GOCD prompt communication to hospitalists in Diagnotes.
GOCD, goals-of-care discussion.
Data Collection and Management
Data for calculating 30-day mortality risk based on our model were collected by investigators
from the EHR. These data were collected, and mortality risk was calculated prospectively
within 24 hours of hospitalization for the intervention group and retrospectively
for the control group.
The following data were collected from hospitalists via Diagnotes soon after communicating
the model's mortality risk: (1) whether they agreed that the patient was at risk for
30-day inpatient mortality risk and stratify that risk as mild, moderate, or high
risk; (2) what was their process for determining mortality risk (i.e., did they use
a risk score or clinical judgement); and (3) if they planned to offer GOCD or consult
palliative care team within 48 hours of receiving the model-predicted high mortality
risk communication. Patient outcomes were collected from the EHR via chart review.
Incidence of deaths that occurred during the hospitalization or within 30 days of
discharge were collected via chart review at 180 days after enrollment in the study
to capture any unrecorded deaths at the time of discharge.
Two physician investigators independently reviewed the identified patient records.
Interrater reliability was established by comparing data collection between two reviewers
and meeting regularly until complete agreement was achieved. All study data were entered
into a Microsoft Excel file and saved on an encrypted computer.
Variables
Demographic variables such as age, sex, and race were examined for a comparative analysis
between the control and intervention groups. Additionally, variables measuring social
determinants of health, including marital status, employment status, and insurance
type, were considered. Body mass index (BMI), a known factor in all-cause mortality,
was also a subject of comparison.[25 ] Baseline characteristics regarding the nature of each patient's hospital admission
and stay were also collected. These include where each patient was transferred from,
their admission status (inpatient or observation), level of admission (medical–surgical
general care or progressive care), and code status on admission.
Primary outcome measures included whether (1) the hospitalists agreed their patients
were at risk for 30-day inpatient mortality risk (yes/no), (2) whether the hospitalist
planned to conduct GOCD or consult palliative care team within 48 hours of communication
(yes/no,) and (3) the number of GOCD conducted within 72 hours of admission.
Secondary clinical outcomes included number of patients with advance directives at
discharge, code status at discharge, day of ICU escalation if it occurred, frequency
and day of hospice enrollment, frequency and day of readmission within 30 days of
discharge, length of stay (i.e., hospitalization during the pilot study), average
number of 30-day postdischarge encounters (i.e., readmission or outpatient visit),
and frequency of deaths that occurred during the hospitalization or within 30 days
of discharge. The impact of communicating model-predicted mortality risk on the timing
of initiating a GOCD was recorded in the subgroup of patients in whom hospitalists
planned to conduct GOCD or request palliative care consult within 72 hours of admission.
Within this subgroup, we recorded the number of patients who actually had GOCD and
who conducted the GOCD (i.e., hospitalist or/and palliative care team), by examining
documentation of GOCD in the EHR GOCD template or hospitalist progress notes.
Statistical Analysis
The analysis for this pilot study was primarily descriptive. Comparisons between the
intervention and control groups were performed using chi-square tests for categorical
variables and a two-tailed t -test for continuous variables. To determine the model's and the hospitalists' abilities
to predict mortality, model-generated mortality risk score and hospitalists' stratification
of mortality risk were compared between patients who were alive and those who died
during their hospitalization or within 30 days of discharge. All statistical analyses
were performed using SAS software version 9.4 (SAS Institute, Cary, North Carolina,
United States), and findings were considered statistically significant at p ≤ 0.05.
Results
A total of 111 patients were screened for inclusion in the study, 84 of whom were
eligible and included (42 in each group). [Table 1 ] describes the frequency of baseline patient characteristics among the control and
intervention groups and compares these characteristics between groups. Baseline patient
characteristics between the control and intervention groups were similar in terms
of age, sex, race, marital status, insurance type, admission status, level of admission,
and code status on admission. However, patients in the intervention group were more
likely to be employed (p = 0.04) and have a lower body mass index (p = 0.001). There were also significant differences among where patients were transferred
from (p = 0.01). The model-generated mortality risk score was not significantly different
between the control and intervention groups (p = 0.93).
Table 1
Comparison of baseline patient characteristics between control and intervention groups[a ]
Characteristic
Control (n = 42)
Intervention (n = 42)
p -Value
Age (y), mean (SD)
62.71 (14.61)
68.45 (16.28)
0.09
Sex, female, n (%)
20 (47.62)
20 (47.62)
1.00
Race, n (%)
1.00
American Indian/Alaskan Native
1(2.38)
1 (2.38)
Black/African American
3 (7.14)
2 (4.76)
White
38 (90.48)
39 (92.86)
Marital status, married, n (%)
22 (52.38)
17 (40.48)
0.45
Employment status, employed, n (%)
17 (40.48)
6 (14.29)
0.04
Insurance type, n (%)
0.09
Medicare/Medicaid
23 (54.76)
34 (80.90)
Other
19 (45.23)
8 (19.05)
Body mass index, mean (SD)
30.71 (6.85)
25.87 (6.51)
0.001
Transferred from, n (%)
0.01
Outside hospital, emergency department
13 (30.95)
22 (52.38)
0.13
Outside hospital, inpatient
22 (52.38)
8 (19.05)
0.01
Long-term acute care (LTAC)
0 (0.00)
1 (2.38)
NA
Outpatient clinic
5 (11.90)
8 (19.05)
0.41
Home
2 (4.76)
1 (2.38)
0.57
Other (subacute rehabilitation center, prison)
0 (0.00)
2 (4.76)
NA
Admission status, n (%)
1.00
Inpatient
41 (97.62)
40 (95.24)
Observation
1 (2.38)
2 (4.76)
Level of admission, n (%)
0.76
Medical–surgical general care
36 (85.71)
35 (83.33)
Progressive care
6 (14.29)
7 (16.67)
Code status on admission, n (%)
1.00
Full code
38 (90.48)
39 (92.86)
DNR/DNI/comprehensive care
4 (9.52)
3 (7.14)
DNR/DNI/comfort care
0 (0)
0 (0)
Model-generated mortality risk score, mean (SD)
−0.57 (1.06)
−0.55 (1.06)
0.93
Abbreviation: SD, standard deviation.
a Chi-square testing was used to analyze the categorical data; t-testing was used for
continuous data.
[Table 2 ] compares patient outcomes between the control and intervention groups. Hospitalists
agreed with the risk of 30-day inpatient mortality as predicted by the model in all
patients (100%). Hospitalists indicated the plan to conduct GOCD or consult the palliative
care team on day 2 of hospitalization more often in the intervention group than in
the control group (21.43 vs. 9.52%, p < 0.001). Hospitalists rated 19% of patients in the intervention group as high risk
(n = 8), 40% as moderate risk (n = 17), and 40% as low risk (n = 17) for 30-day inpatient mortality based solely on their clinical judgement (i.e.,
without any clinical decision support tool). Although not statistically significant,
our results demonstrate possible patient and family preferred choices in the intervention
group including more transitions to DNR/DNI/Comprehensive and comfort care, more enrollments
into inpatient hospice, earlier hospice enrollment, more delayed ICU escalations,
and fewer inpatient deaths.
Table 2
Comparison of patient outcomes between control and intervention groups
Characteristic
Control (n = 42)
Intervention (n = 42)
p -Value
Agreed with mortality risk, n (%)
–
42 (100)
NA
Hospitalists' mortality risk stratification, n (%)
–
42 (100)
NA
High risk
–
8 (19.05)
NA
Moderate risk
–
17 (40.48)
NA
Mild risk
–
17 (40.48)
NA
Hospitalist indicates plan to conduct GOCD or request palliative care consult, n (%)
4 (9.52)
9 (21.43)
<0.001
Day of GOCD, mean (SD)
3.25 (2.06)
3.45 (3.42)
0.91
Day of the palliative care consult, mean (SD)
2.33 (2.31)
5.86 (6.57)
0.40
Code status at discharge, n (%)[a ]
0.24
Full code
39 (92.8)
33 (78.5)
DNR/DNI/comprehensive care
1 (2.38)
6 (14.29)
DNR/DNI/comfort care
2 (4.76)
3 (7.14)
Escalated to intensive care unit (ICU), n (%)
2(4.76)
6(14.29)
0.26
Day of ICU escalation, mean (SD)
5.50 (6.36)
8.50 (9.50)
0.70
Enrolled in hospice, n (%)
0.63
Home with hospice
2 (4.76)
3 (7.14)
Inpatient hospice
1 (2.38)
2 (4.76)
Day of hospice enrollment, mean (SD)
15.00 (5.66)
9.25 (2.87)
0.15
Advance directives at discharge, n (%)
11 (26.19)
11 (26.19)
1.00
Hospital length of stay (d), mean (SD)
8.17 (8.20)
9.76 (7.21)
0.35
30-d readmission, n (%)
6 (14.29)
5 (11.90)
0.33
Day of readmission, mean (SD)
11.83 (5.85)
18.40 (9.29)
0.18
30-d postdischarge encounters, mean (SD)
0.66 (0.78)
1.03 (1.04)
0.08
Deceased, n (%)
12 (28.57)
9 (21.43)
0.45
Abbreviations: NA, not applicable; SD, standard deviation.
a “Full code” includes performing all available and appropriate resuscitative measures
in the event of cardiorespiratory arrest. “DNR/DNI/Comprehensive care” includes standard
approach to care but forbids resuscitation (DNR) and intubation (DNI). “DNR/DNI/Comfort
care” focuses on providing pain relief and comfort rather than attempting to cure
a terminal or serious condition.
[Table 3 ] compares GOCD characteristics in the subgroup of patients in whom hospitalists stated
that they planned to conduct GOCD or consult palliative care within 72 hours of admission.
A higher rate of GOCD were actually completed within 72 hours among patients in the
intervention group than in the control group (75 vs. 50%). The intervention group
had a slightly higher proportion of GOCDs conducted by palliative care (78 vs. 50%)
and slightly lower proportion of GOCD conducted by hospitalists (33 vs. 50%). When
agreed upon and offered, more GOCD were completed within 72 hours in the intervention
group.
Table 3
Comparison of goals-of-care discussion characteristics between subset of patients
in the control and intervention groups whose hospitalists stated that they planned
to conduct a goals-of-care discussion
Characteristic
Control (n = 4)
Intervention (n = 9)
GOCD completed in 72 h, n (%)
2 (50.00)
7 (78.00)
GOCD by a hospitalist in 72 h, n (%)
2 (50.00)
3 (33.33)
GOCD by the palliative care team in 72 h, n (%)
2 (50.00)
7 (78.00)
Abbreviation: GOCD, goals-of-care discussion.
[Table 4 ] compares risk score between patients based on their actual mortality. Our results
indicate that a greater absolute value of our model-generated mortality risk score
was significantly associated with mortality in total sample (p = 0.01), similar to the hospitalists' judgment of mortality risk in the intervention
group (p = 0.02).
Table 4
Comparison of mortality risk prediction between alive and dead patients
Variable
Alive
Dead
p -Value[a ]
Model-generated mortality risk score
(n = 63)
(n = 21)
0.01
Mean ± SD
−0.4 ± 1.0
−1.1 ± 0.9
Min–max
−2.0 to 2.2
−2.1 to 0.7
Hospitalists' risk stratification, n (%)
(n = 33)
(n = 9)
0.02
High risk
3 (4.80)
4 (19.00)
Moderate risk
13 (20.60)
4 (19.00)
Mild risk
17 (27.00)
1 (4.80)
Abbreviation: SD, standard deviation.
a Chi-square testing was used to analyze the categorical data; t-testing was used for
continuous data.
Discussion
This pilot study showed that hospitalists agreed with our 30-day inpatient mortality
risk in all patients in the intervention group and that communicating 30-day inpatient
mortality risk to hospitalists successfully prompted them to assess these patients
for the need for GOCD within 72 hours of admission. Despite the small number of patients
in this pilot study, we found that the greater absolute values of the 30-day inpatient
mortality risk using our previously developed mortality model[24 ] were significantly associated with patient death, suggesting this model has validity
for future use. Although many of the clinical outcomes in this pilot study did not
demonstrate statistically significant effects of the intervention, our results may
be clinically meaningful regarding mortality risk communication and earlier transitions
to hospice and changes in code status. These changes suggest that without acknowledging
these issues, there could be a risk of delivering care that might not align with the
patient's and family wishes.
Similar to the model used in a pilot study conducted by Courtright et al,[10 ] our model has several strengths for real-world applicability, including systematic
identification of patients at risk for 30-day inpatient mortality and delivery of
actionable information to clinical teams. Unlike the pilot conducted by Courtright
et al,[10 ] our study incorporated physicians' clinical decision autonomy as a first step to
risk stratify patients into mild-, moderate-, or high-risk categories and offered
hospitalists to opt-in and participate in the initial GOCD with the patient. Use of
hospitalists' clinical judgment regarding initiation of GOCD is a strength of our
study, as Courtright et al, found that 42.5% of automatically triggered palliative
care consultations were declined due to lack of palliative care needs.[10 ] Although palliative care teams often assist patients and families with GOCD, the
palliative care workforce is extremely limited,[26 ]
[27 ] and strategies may be needed to promote palliative care by providers who are not
palliative care specialists. .
Another strength of this pilot study is its use of a rigorously developed, internally
validated model to identify 30-day inpatient mortality risk, making it more generalizable
than previous models.[24 ] A recent randomized clinical trial by Manz et al had results similar to our pilot
study in increasing GOCD[16 ] but was limited to an outpatient setting and 180-day mortality prediction in oncology
patients. Similarly, pilot study by Haley et al[27 ] examined a narrow patient population, including the factors of cancer, two or more
admissions, residence in a nursing home, ICU admission with multiorgan failure, and
two or more noncancer hospice guidelines (CARING criteria) to predict 1-year all-cause
mortality in hospitalized patients. These criteria were based on group consensus and
literature review.[27 ] In contrast, our focus was mainly on deaths within 30 days of hospitalization,[24 ] and our model was developed using rigorous analysis of local data.[24 ]
We found that the predictive capabilities of our model's 30-day inpatient mortality
risk threshold was comparable to hospitalists' mortality risk prediction. The former
employs data-driven algorithms, utilizing diverse clinical variables and historical
data to forecast inpatient mortality statistically and may provide an unbiased assessment
of numerous factors for a comprehensive outcome. In contrast, the latter relies on
medical professionals' expertise, shaped by experience, intuition, and case context.
This subjective approach offers nuanced insights yet might be influenced by cognitive
biases, limited data access, or overreliance on specific indicators. These differences
highlight the contrast between data-driven and expert predictions, which is crucial
for precise interpretation and acknowledging their strengths and limitations.[28 ] Aligned with a recognized pattern identified in prognostication studies among physicians,
hospitalists in our study might be predisposed to undervalue the seriousness of their
patients' conditions.[29 ]
Our study has several limitations. Generalizability to other patient populations and
settings may be limited by small sample size and lack of randomization. Lack of randomization
likely contributed to significant differences in baseline characteristics between
the intervention and control groups, including employment status, BMI, and where the
patient was transferred from. Like Courtright et al,[10 ] we agreed that alternative study designs, such as randomizing patients at the clinician
or unit level, were not feasible for this relatively small pilot study. Measuring
goal-congruent care was challenging due to limited advance directives and a lack of
standardized GOCD documentation in the EHR. The COVID-19 pandemic, marked by increased
patient volume in our ED, limited outside transfers due to bed capacity constraints,
and reliance on locum teams, impacted recruitment and prolonged the study duration.
Using official death records could have provided more accurate mortality data compared
with the EHR data utilized in our study. Our study solely relied on presenting risk
data without specifically testing strategies such as participant reactivity and nudge
theory. Future research should explore diverse approaches to enhance the effectiveness
of interventions targeting clinician behavior.[30 ] The Hawthorne effect of the GOCD prompt and inherent biases in clinical decision-making
tools based on prediction models may have influenced results and decision-making.
Moreover, all clinical decision-making tools based on prediction models face the potential
of perpetuating human biases present in the foundational data and have the capacity
to capture specific practice patterns and the case-mix index at one time point.[23 ] As with any prediction model, ours may need reevaluation and recalibration to ensure
it is clinically meaningful.[31 ]
Despite these limitations, our study was the first step in assessing the impact of
prospectively communicating a model-generated mortality risk to hospitalists and evaluating
the effects of triggered mortality risk communication on GOCD and patient outcomes.
As the next step, we will conduct semi-structured interviews of hospitalists to incorporate
their perspectives and preferences to enhance the intervention's acceptability. We
also plan to train hospitalists to develop core skills to improve the quality and
documentation of GOCD. Hospitalists may consider initiating meaningful GOCD early
in the inpatient trajectory ([Fig. 1 ]) to optimize end-of-life care and avoid higher health care utilization with burdensome
care transitions.[32 ]
[33 ]
[34 ]
Conclusion
This pilot study demonstrated promising evidence to support the systematic deployment
of our mortality prediction model[23 ] in seriously ill transferred patients via early communication of the mortality risk
with the hospitalists. This intervention may be useful to identify patients at the
greatest need for GOCD early in the hospital stay, thus facilitating patient and family
preferred end-of-life care. Larger randomized control trials are needed to determine
its acceptability and effects on patient outcomes.
Clinical Relevance Statement
Clinical Relevance Statement
This article describes prospective implementation of a novel clinical decision support
system, which alerts hospitalists to risk for 30-day inpatient mortality. In addition,
we evaluated the model's performance in clinical practice by assessing the agreement
of hospitalists with its recommendations and its impact on GOCD. EHR-based mortality
models can provide meaningful input into clinical workflow and decision-making.
Multiple Choice Questions
Multiple Choice Questions
The electronic health intervention used in this study to identify patients at 30-day
inpatient deaths was based on all of the principles below except:
Principles of informational nudge.
Risk stratification of mortality using EHR data.
Augmentation of early clinical decision-making for timely GOCDs by providing patient-specific
data.
Forbidding a few options that a clinician would otherwise provide if not involved
in this study.
Correct Answer: The correct answer is option d. This intervention used the principles of informational
nudge that provides information to alter behavior in a predictable way without forbidding
any available options. This model risk stratified the 30-day risk for inpatient deaths
using EHR-based patient-specific data to augment clinical decision support for timely
GOCDs.
Most clinicians use their clinical judgment to risk stratify patients for inpatient
mortality as:
The inpatient mortality models are either disease-specific or limited to intensive
care patients and are not generalizable to medical floor patients.
Few inpatient mortality models that were proven retrospectively have not been prospectively
evaluated in the clinical practice.
Risk stratification of mortality prediction is a complex process, and there is not
much evidence to support it.
If robust mortality prediction models are available, clinicians may be willing to
supplement their clinical decisions with the model's input.
Correct Answer: The correct answer is option c. Mortality risk stratification is a complex process
and involves several variables, including past medical history, acute presenting condition,
hemodynamic stability, vital signs, laboratory results, diagnostic imaging, and response
to treatment options. There are insufficient data to conclude that machine learning
models are inferior to human mortality risk prediction. Inpatient mortality models
that are prospectively evaluated in large, randomized trials and generalizable to
all patients admitted and not limited by location or diagnoses are needed as clinicians
are willing to adopt such models in their clinical practice.
