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TYPE
Original Research
PAGE NO.
07-14
10.37547/tajmspr/Volume07Issue07-02
OPEN ACCESS
SUBMITED
07 June 2025
ACCEPTED
24 June 2025
PUBLISHED
25
July 2025
VOLUME
Vol.07 Issue 07 2025
CITATION
Tatevik Melkumyan. (2025). Use of QAPI Methodology for Risk
Management in Home Palliative Care. The American Journal of Medical
Sciences
and
Pharmaceutical
Research,
7(07),
7
–
14.
https://doi.org/10.37547/tajmspr/Volume07Issue07-02
COPYRIGHT
© 2025 Original content from this work may be used under the terms
of the creative commons attributes 4.0 License.
Use of QAPI Methodology
for Risk Management in
Home Palliative Care
Tatevik Melkumyan
MD Endocrinologist, Radiologist, Armenia,
Owner NT QA INC, Qa consultant for home health and
hospices, USA
Los Angeles, California, USA
Abstract:
This article presents a theoretical and
analytical review of the applicability of the QAPI (Quality
Assurance
and
Performance
Improvement)
methodology for risk management in home-based
palliative care. The study is based on an interdisciplinary
approach that integrates systems theory, the
Donabedian model, quality of care assessment tools,
and digital monitoring algorithms. Particular attention is
given to aligning structure, process, and outcome
indicators with empirical data on patient needs and
organizational barriers specific to outpatient settings.
Sources covering patient-centered care, resource
constraints,
multicultural
contexts,
and
care
digitalization are analyzed. Based on regression models
and content analysis of the literature, key risks are
identified, including emotional burnout, informational
deficits, inadequate symptom control, and insufficient
spiritual support. A conceptual model for QAPI
integration is proposed, which incorporates both
technical and humanitarian aspects of quality. The
developed framework includes indicators adapted to
the context of home care, digital visualization tools, and
principles for sustainable implementation under
resource constraints. This article will be of interest to
researchers in palliative medicine, quality management
professionals, outpatient care coordinators, and all
those involved in developing and implementing patient-
centered systems for evaluating and improving home-
based care.
Keywords:
QAPI, palliative care, home care, risk
management, quality of care, Donabedian model,
quality indicators, emotional needs, digital monitoring,
outpatient medicine.
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Introduction
The contemporary healthcare system is undergoing
profound transformation driven by the rising prevalence
of chronic disease, population ageing, and shifting
priorities in service delivery. Against this backdrop,
home-based palliative
‐care models are gaining traction
as clinically effective, economically sound solutions that
meet the expectations of patients and their families [1].
Care delivered at home reduces hospital admissions,
honours end-of-life preferences, and strengthens family
engagement. Scaling such models, however, involves
multifaceted risks
—
from poor coordination across
healthcare structures to social, cultural, and
informational barriers that are particularly acute in rural
and multicultural settings [5].
In efforts to mitigate these risks and safeguard quality,
researchers and practitioners have turned their
attention to the QAPI methodology. Originally
developed as part of a federal initiative to enhance long-
term-care facilities, QAPI combines quality control with
continuous process improvement, encompassing the
identification of critical points, root-cause analysis, and
corrective measures. Although widely adopted in
institutional settings, its potential in the home-palliative
context remains under-explored. Effective adaptation
must account for intermittent clinical oversight, cross-
functional collaboration, and a substantial share of
informal care.
A comprehensive analysis of factors influencing patient
needs and risk in home-based palliative care highlights
managerial intervention zones. A cross-sectional study
from China demonstrated that physical symptoms,
emotional status, financial strain, and functional
autonomy reliably predict the intensity of palliative
needs [7]. These findings provide a foundation for a
QAPI model oriented toward proactive risk management
and individualised care.
Both scholarly and applied literature show growing
interest in developing quality indicators suitable for
ambulatory palliative care. A systematic review of 312
unique quality indicators conducted by Kan et al. [6]
revealed a pronounced imbalance between technical
dimensions (structure and process) and humanistic
dimensions (cultural and spiritual care), underscoring
the need for a more holistic approach to quality
management in the home setting.
The aim of this study is to analyse the applicability of the
QAPI methodology for risk management in home-based
palliative care. The investigation synthesises current
approaches to quality and safety in ambulatory palliative
services, identif
ies common risks, and evaluates QAPI’s
potential as an instrument for systematic adaptation
and sustained improvement.
Materials and Methods
The methodological basis of this theoretical
investigation lies at the intersection of a systems
approach to qualit
y management, Donabedian’s
framework, and the QAPI concept, with focus on the
specific features of home-based palliative care and
associated risk management. The research adopts a
theoretical-analytical design intended to interpret
existing models and regulations in order to construct a
conceptual
framework
for
QAPI-driven
risk
management in home palliative practice.
The analytical strategy is structured according to
international methodological standards PRISMA and
AMSTAR, facilitating systematic coverage and critical
appraisal of literature published in peer-reviewed
outlets. Application of PRISMA principles formalised the
stages of source selection and categorisation relevant to
QAPI in the context of home palliative care, whereas
AMSTAR was employed to assess the validity and
methodological rigour of the included studies.
A primary tool for systematising indicators and risks in
this study is Donabedian’s model, which organises
quality metrics into three key categories: structure,
process, and outcome. This methodology enables the
arrangement of diverse quality indicators into a
coherent and comparable framework applicable to
evaluating home-based palliative services. In the work of
Kan et al. [6], generalised approaches to indicator
classification are presented, with emphasis on their
distribution across clinical and non-clinical domains of
care.
Complementing the content typology, Shalom et al. [9]
developed a formalised quality-assessment system
based on fuzzy-logic algorithms and principles of
automated clinical-data analysis. Such a model allows
dynamic monitoring of compliance with established care
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standards and rapid identification of deviations. In the
present analysis, this system is interpreted as a potential
risk-monitoring module within the QAPI approach,
particularly suited to settings with limited resources and
high dependency on human factors.
Contextual validation of the conceptual model is
achieved by incorporating data from Haneuse et al. [5],
which describes key organisational and infrastructural
obstacles to implementing palliative care in remote and
rural regions. Focus is placed on factors such as
workforce shortages, geographic isolation, and local
cultural characteristics that must be considered when
developing sustainable quality-improvement strategies.
Similarly, Alizadeh et al. [1] proposed a comprehensive
home-based palliative-care model for oncology patients,
highlighting
the
necessity
of
multidisciplinary
collaboration, adaptation to local realities, and active
family involvement. Additionally, the parameters for
evaluating the implementation of improvements
—
reach, perceived impact, sustainability, and fidelity of
intervention
—
are derived from the work of Toles et al.
[10] and integrated as core indicators of QAPI-practice
viability in home-care environments.
Thus, the theoretical-analytical model developed in this
investigation is based on the alignment of structured
indicators, empirical findings, and context-specific data.
Systematic extraction and cross-analysis of sources have
established a foundation for the concept of QAPI-driven
risk management in home-based palliative care,
encompassing
both
clinical
and
organisational
determinants.
Results
At the first stage of analysis, a search strategy was
formulated around the concepts of care quality, patient
experience, and end-of-life palliative care. The
methodological foundation drew on the framework
established by Quigley and McCleskey [8], in which key
search directions encompassed terms reflecting quality
improvement, patient experience, and end-of-life care.
The combination of MeSH headings and free-text terms
enabled capture of a broad array of studies from 2021
–
2025 relevant to QAPI in home-based palliative care. The
strategy is summarized in Table 1.
Table 1
–
Search strategy (Source: [8])
Concept
Medical Subject Headings (MeSH)
Search Terms
Improving quality
Quality improvement
Quality improvement; performance
improvement; process
improvement; plan-do-study-act;
six sigma; learning collaborative;
best practices;
Patient and/or caregiver experience
Patient-centered care; patient
satisfaction
Patient experience; patient
centered care; patient satisfaction;
bereaved family; bereaved
caregiver
End-of-life care
Hospices; hospice care
Hospices; hospice care; nursing
home; assisted living facilities;
palliative care; end of life care; end
of life experience survey
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Application of this strategy yielded key theoretical
sources that reflect the particularities of QI approaches
in hospice and palliative settings, including quality-
assessment instruments, interdisciplinary models, rural-
region barriers, and digital care methodologies [2]. Of
particular note was the study by Liu et al. [7], which
proposed a quantitative model linking quality of life to
palliative-care needs. Conducted in China among 440
patients with progressive cancer receiving home care,
the authors utilized a modified PNPC-sv scale to assess
seven core domains alongside the EORTC QLQ-C30
quality-of-life questionnaire. Multiple regression
analysis revealed that physical status, functional
limitations, emotional state, and financial difficulties
were the principal predictors of heightened palliative-
care needs. Key results are presented in Table 2.
Table 2
–
Multiple regression analysis of factors influencing home-based palliative care needs (Source:
[7])
Factors
Unstandardize
d coefficients
(B)
Standard error
(SE)
Standardized
coefficients
(β)
P
95% CI
Constant
46.623
7.806
–
<0.01
31.278 to 61.968
KPS
–
0.302
0.048
–
0.367
<0.01
–
0.395 to
–
0.208
Physical
functioning
–
0.079
0.039
–
0.151
0.044
–
0.156 to
–
0.002
Role
functioning
–
0.116
0.029
–
0.250
<0.01
–
0.173 to
–
0.059
Emotional
functioning
–
0.113
0.031
–
0.160
<0.01
–
0.174 to
–
0.051
Nausea/vomiti
ng
0.059
0.025
0.103
0.016
0.011 to 0.107
Pain
0.049
0.025
0.089
0.048
0.000 to 0.098
Sleep
disturbances
0.054
0.023
0.095
0.020
0.009 to 0.099
Financial
difficulties
0.092
0.021
0.179
<0.01
0.050 to 0.133
The results demonstrate that declines in physical and
role functioning, amplification of symptoms, and
increasing economic pressures significantly heighten the
intensity of palliative-care needs. Financial difficulties
are especially notable, serving as predictors of high
patient vulnerability.
Analysis of successful and unsuccessful interventions in
palliative care requires alignment with the core logic of
the QAPI methodology, which comprises four
interrelated domains:
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●
detection of the problem;
●
root-cause analysis;
●
implementation;
●
subsequent review of interventions.
These stages form a closed loop of continuous
improvement, ensuring responsive risk management
and systemic resilience of changes. Based on a
theoretical-analytical literature review, an attempt was
made to categorise palliative-care interventions within
this model.
At the detection stage, routine clinical data, patient and
caregiver complaints, and recurring adverse events
typically act as triggers. In their scoping review, Toles et
al. [10] systematised the most frequent risk areas in
long-term-care facilities
—
pressure ulcers, falls, and
inadequate pain control. However, the authors highlight
that only one third of studies provided a clear
description of the root-cause analysis logic, impeding
the translation of interventions to other contexts. This
gap is particularly pronounced in facilities with limited
analytical resources, where problems may be recorded
but underlying mechanisms remain unrecognised.
When comparing implementation strategies, flexible,
context-sensitive approaches consistently outperform
rigid, centralised measures. Carpenter et al. [3] show
that intervention effectiveness is directly tied to the
presence
of
local
“quality
champions”
and
interdisciplinary engagement. For example, integrating
family members into daily rounds at hospice facilities
markedly improved person-centredness of service and
reduced complaint rates. In contrast, Quigley and
McCleskey [8] note that interventions lacking local
adaptation (such as telemedicine surveys without on-
site support) exhibited very low effectiveness and were
not perceived as meaningful by staff.
The least developed component of QAPI cycles remains
the review of interventions. Toles et al. [10] report that
fewer than 20 % of publications included post-
intervention monitoring data beyond six months. The
absence of a mechanism for revalidation renders even
successful initiatives vulnerable to attrition amid
organisational
turbulence.
Underestimation
of
subjective
indicators
—patients’
and
families’
perceptions of quality
—
is particularly critical. Carpenter
et al. [3] emphasise that without systematic collection of
these data, real transformations in care culture cannot
be assessed.
Discussion
The analysis of predictors of needs among patients
receiving home-based palliative care, as presented by
Liu et al. [7], highlights key influencing factors and
reveals structural and methodological gaps in existing
quality-assessment systems. The data demonstrate
strong prognostic value for measures such as functional
status (KPS), role and physical functioning, emotional
well-being, and financial hardship. However, this model
simultaneously exposes systemic limitations of the
traditional QI repertoire, particularly regarding
subjective and socio-psychological dimensions of care.
The most pronounced association is observed between
declining KPS and rising palliative-care needs, which
aligns with classical biomedical paradigms. Yet, when
factors related to emotional functioning, sleep quality,
anxiety, and financial distress are examined, their
underrepresentation in current QI systems becomes
evident. Specifically, Kan et al. [6] report that of over 300
identified quality indicators in home palliative care,
fewer than 3 % address cultural and spiritual
dimensions, while indicators capturing emotional
burden or financial stress are virtually absent from
validated scales. This disconnect between empirically
significant predictors and indicator registries points to
structural imbalances in assessment frameworks.
Moreover, as noted by Shalom et al. [9], existing
formalised QI-evaluation algorithms
—
including fuzzy-
logic
–
based models
—
are chiefly oriented toward
compliance with clinical protocols and technical metrics.
While this focus ensures reproducibility and
automation, such models fail to capture the dynamics of
caregiver burnout, caregiver anxiety, or patient
information deficits. Consequently, an illusion of quality
may arise in the absence of sensitive indicators for
subjective and social risks.
The findings of the regression analysis [7], together with
the content analysis of the literature, indicate that
systematic gaps exist both at the structural level
(insufficient indicators covering emotional, spiritual, and
financial aspects) and at the process level (lack of
adapted procedures for gathering and interpreting
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subjective experiences). Long-term care institutions and
ambulatory services are particularly vulnerable, as
mechanisms for integrating subjective risks into QAPI
cycles are either lacking or fragmentary.
The development of an integrated QAPI model for
home-based palliative care requires the combination of
standardized indicators, contextual constraints, and
digital assessment tools. The methodological foundation
was
the
Donabedian
classification
—“structure,”
“process,” and “outcome”—
as presented by Kan et al.
[6], which proposed a unified framework for assessing
quality in the home setting. This structure enables the
operationalization of both internal care processes and
clinical outcomes within a variable context.
A key step in shaping the QAPI model is the alignment of
patient needs described by Liu et al. [7] with resource
constraints detailed by Haneuse et al. [5]. For example,
the 57 % rate of complaints about insufficient
information on pain management [7] in typical rural
conditions correlates with a lack of educational
interventions and staff prepared to deliver them. These
intersections form the basis of Table 3, which presents
care-related
risks,
their
key
predictors,
and
corresponding QAPI indicators, taking implementation
barriers into account.
Table 3
–
Comparison of Key Risks, Predictive Factors, and QAPI Indicators (Compiled by the author
based on sources: [5], [6], [8])
Type of Risk
Patient Needs /
Predictive Factors
QAPI Indicators
Implementation
Barriers
Emotional burnout
Decline in emotional
functioning
Availability of regular
psychological support
(process)
Lack of in-house
psychologists
Financial constraints
High out-of-pocket
expenses, low
reimbursement rates
Indicator of financial
accessibility to services
(outcome)
Incomplete coverage,
limited public funding
Informational deficit
Poor knowledge about
pain, prognosis, and
care routines
Number of educational
sessions delivered
(process)
Low staff engagement,
time constraints
Staffing instability
Limited contact with
care providers, home
visit inaccessibility
Patient-to-staff ratio
(structure)
High turnover,
geographic remoteness
Spiritual and cultural
neglect
Absence of meaning-
making and ethical
dialogue
Access to spiritual
counselor, cultural
adaptation (process)
Lack of training and
dedicated roles
Symptom control
Frequent reports of
pain, nausea, insomnia
Frequency of symptom
assessment tool use
(process)
Tool inaccessibility,
staff overload
As Table 3 shows, the principal risks faced by home-
based patients correspond with their subjective needs
and failures in the operationalization of structural and
process elements of the system. The absence of routine
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pain assessment and the underestimation of emotional
status point to chronic gaps between regulatory
standards and practice, especially in resource-limited
settings. Moreover, practical implementation of QAPI
indicators requires accessible tools (e.g., visualization
platforms described by Elshehaly et al. [4]) and a
supportive
organizational
context,
including
management backing and sustainable funding.
Conclusion
This study has conceptualized the potential of the QAPI
methodology as a tool for systematic risk management
in home-based palliative care. It demonstrates that a
methodology originally developed for long-term care
facilities can be effectively adapted to outpatient
settings, taking into account their organizational,
cultural, and resource constraints. The theoretical-
analytical
approach
—grounded
in
Donabedian’s
structure
–
process
–
outcome model, digital quality-
assessment algorithms, and analysis of empirical
determinants
—
has enabled the structuring of the
problem domain and identification of operationalizable
indicators across those three domains.
The findings indicate that integrating QAPI into home
palliative
practice
requires
the
simultaneous
consideration of both clinical parameters and subjective
characteristics of care, including patients’ emotional
states, levels of information, and financial vulnerability.
The model’s success hinges on its sensitivity to
context
—
staff availability, local infrastructure, the
readiness of care teams for multidisciplinary
collaboration, and the ability to account for the
intangible aspects of the patient experience.
Risk-system analysis revealed that informational and
emotional deficits remain dominant, alongside an
imbalance
between
technical
and
humanistic
components of quality. This underscores the need to
expand existing models by incorporating adapted
procedures for collecting subjective experience data and
by enhancing the validity of indicators related to
cultural, spiritual, and financial aspects of care.The
proposed conceptual QAPI model for home settings
demonstrates the feasibility of combining standardized
processes with the flexibility needed to respond to
individual needs. Its resilience is supported by the
digitalization of monitoring procedures, engagement of
family and informal caregivers, and a modular design
that allows adaptation to local resources and care
contexts.
Thus, the QAPI methodology, in its extended and
adapted form, serves both as a managerial quality-
control instrument and as a foundation for transforming
palliative-care culture toward greater personalization,
proactivity, and contextual resilience. Future research
should focus on empirical validation of the proposed
model in field settings, integration of digital platforms
into QAPI cycles, and development of indicators that
capture the dynamics of subjective and cultural
experience in ambulatory palliative care.
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