Towards a Formal Process-driven Framework for Streamlining Patient-centric Care Wen Yao 1 , Akhil Kumar 2 , Jerome Rolia 3 , Sujoy Basu 3 , Sharad Singhal 3 1 College of Information Sciences and Technology, Penn State University 2 Smeal College of Business, Penn State University, University Park, PA 16802 {wxy119,akhilkumar}@psu.edu 3 Services Research Lab, Hewlett-Packard Laboratories, Palo Alto, CA 94304, USA {jerry.rolia, sujoy.basu, sharad.singhal}@hp.com Abstract. Rapidly growing patient interests in participating in their care process and accessing their health data has motivated health organizations to provide patient-oriented care delivery both in clinical and homecare settings. With the goal of giving patients a more proactive role in their own care, we motivate and propose a formal process-driven framework for streamlining patient-centric care and improving patient-provider communication. It will lead to patients having better access health services and taking more responsibility in their health management. At the same time the burden on healthcare professionals is reduced, while enabling greater efficiency, improved safety and higher quality. We also developed an architecture for system implementation. Finally, we demonstrate a next generation health care delivery system with a use case. Keywords: patient-centric care, process-driven, clinical pathway, medical guideline, healthcare, patient communication, metrics 1 Introduction Despite the advances in life expectancy and quality of life, the current healthcare delivery system faces significant challenges in terms of cost, accessibility and quality [1]. One of the goals established by the Institute of Medicine in 2001 is that healthcare delivery should be patient-centric [2], which means it should provide care that is respectful of and responsive to individual patient preferences, needs and val- ues. Another goal is to ensure that patient values guide all clinical judgments and decisions. As mobile devices become pervasive, and patients become more informed with ease of access to health information, it is reasonable to assume that they will play a more interactive role in decision making about their health matters. Hence, there is a need to develop a formal methodology to foster patient-centric care service delivery. A clinical workflow delineates a path for a patient through the various steps in in- teracting with a clinic, lab, pharmacy and other participants in the healthcare system, as shown in Figure 1. In this care process, the patient is the only constant who is in- volved in all the steps and communications among a large number of participants in
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Towards a Formal Process-driven Framework for
Streamlining Patient-centric Care
Wen Yao1, Akhil Kumar
2, Jerome Rolia
3, Sujoy Basu
3, Sharad Singhal
3
1College of Information Sciences and Technology, Penn State University 2Smeal College of Business, Penn State University, University Park, PA 16802
{wxy119,akhilkumar}@psu.edu 3Services Research Lab, Hewlett-Packard Laboratories, Palo Alto, CA 94304, USA
{jerry.rolia, sujoy.basu, sharad.singhal}@hp.com
Abstract. Rapidly growing patient interests in participating in their care process
and accessing their health data has motivated health organizations to provide
patient-oriented care delivery both in clinical and homecare settings. With the
goal of giving patients a more proactive role in their own care, we motivate and
propose a formal process-driven framework for streamlining patient-centric
care and improving patient-provider communication. It will lead to patients
having better access health services and taking more responsibility in their
health management. At the same time the burden on healthcare professionals is
reduced, while enabling greater efficiency, improved safety and higher quality.
We also developed an architecture for system implementation. Finally, we
demonstrate a next generation health care delivery system with a use case.
Keywords: patient-centric care, process-driven, clinical pathway, medical
Fig. 2. Overview of process-driven framework for patient-centric care
3.2 Patient Information Model
Definition 1 (Patient information model). A patient information model (PIM) is
comprised of three parts: PIM = (PHR, PPP, PPC), where
PHR represents personal health records
PPP reflects the personal preferences of an individual
PCP summarizes personal clinical pathways
A personal health record (PHR) is a patient’s lifelong health information that she is
allowed to access, coordinate, and share with her other parties [10]. The PHR might
include patient-reported symptoms, lab results uploaded by patients, or even data
from smart devices. Usually, it is maintained by patients themselves and can include
data from many health organizations that they have visited. Here, we assume that the
PHR is electronic, and is accessible online at any time.
A personal preference profile (PPP) captures an individual’s preferences, needs,
and values pertaining to her current situation. A personal clinical pathway (PCP) uses
a clinical pathway as a template and documents the decisions, actions and outcome
organized in chronological order pertaining to a specific patient. It allows deviations
from best practice to satisfy a patient’s preferences. We discuss the details shortly.
Example 1 (patient preference profile). The matrix in Figure 3 shows the preference
profiles for three patients P1, P2 and P3, on a 1 – N scale while applying heart failure
guidelines. A larger number indicates a stronger preference. In this way, the system is
aware of patients’ preferences of treatment methods (e.g., medication or surgery),
quality-of-life aspects (e.g., exercise- or diet-based rehabilitation program), etc. A
PPP is acquired from context-building to be discussed at length in Section 4.
Obviously, the medical conditions of patients should take priority over their prefer-
ences whenever there is a conflict since patient safety is more important. For example,
although surgery is the least preferred treatment method of patient P1, if she has re-
peated hospitalization because of heart failure despite aggressive medical therapy, she
should be considered to have a cardiac transplant.
Item Applied strategy Treatment method Rehabilitation program
Choice Normal Aggressive Medication Surgery Exercise Diet Education
P1 1 2 1 2 3 2 1
P2 2 1 2 1 3 1 1
P3 1 1 2 1 1 2 3
Fig. 3. Matrix of patient preference profiles (partial)
3.3 Medical Guidelines and Clinical Pathways
A medical guideline (a.k.a. clinical protocol or clinical practice guideline) is a docu-
ment that guides decisions and criteria regarding diagnosis, management and treat-
ment in specific areas of healthcare. For example, guidelines can be categorized by
disease types like pediatrics, pulmonary or heart diseases. This is naturally aligned
with the way they are developed, i.e., by medical staff with different expertise areas.
Definition 2 (Medical guideline). Let be a clinical guideline,
where T is the set of all tasks, N is the set of all control nodes, E is the set of all edges
connecting tasks and control nodes, and goal is the objective of this guideline. Based
on common structures identified by Peleg et al. [3], we allow the task node denote various task types, and the
control node denote the four basic control-
flow constructs.
Selecting a guideline for implementation at a particular clinic or hospital requires
an agreement among care professionals at that site and its patients, since different
kinds of guidelines exist with different sources and goals, and sometimes even have
conflicts [13]. Thus, in practice, guidelines are adapted to a specific clinical setting.
A clinical pathway (or treatment plan) implements medical guidelines after they
are tailored to local and individual circumstances, e.g., availability of resources. In
the clinical pathway, different tasks (performed by clinicians involved in the care) are
defined and optimized in a logical time sequence. Outcomes are tied to specific inter-
ventions, e.g. taking medication for a week or an angioplasty procedure might reduce
blood pressure. In general, a clinical pathway may be developed from many different
guidelines. Ideally, a clinical pathway is the result of evidence-based medicine that
drives the treatment of a specific group of patients with a predictable outcome.
Definition 3 (Clinical pathway). A clinical pathway can be represented as a collec-
tion of guidelines:
, , where is a guideline
and the adaptation of made based on local settings. Alternatively, a pathway can
be represented as where . A clinical pathway is basically a template from
which concrete patient treatment cases (i.e., process instances) are derived. It is repre-
sented in a flow chart as shown in Example 2.
Fig. 4. A clinical pathway incorporating two guidelines from AHRQ [14]
Example 2 (A clinical pathway for heart failure management). Figure 4 depicts a
clinical pathway that associates two medical guidelines from AHRQ for heart failure
management [14]. The first guideline is a general one for evaluation and care of pa-
tients with heart failure, and it invokes a second guideline for initial evaluation. For
detection and treatment tasks, other guidelines can be triggered depending on patient
conditions and choices made by the attending doctors. In this way, Figure 4 guides the
treatment of patients with heart failure in a structured, process-driven manner. When
this pathway is initialized for an actual case, human still take control.
Using Figure 4 as a template, a personal clinical pathway (PCP) documents the
actual execution for a specific patient along. Thus, it keeps track of medical decisions
(e.g., prescribe ACE inhibitor or Beta blocker), actions (e.g., dosage for ACE inhibi-
tor medication), and patient outcomes (e.g., normal body temperature) in chronologi-
cal order for each patient situation. As noted above, deviations are allowed. A final
outcome, e.g., the patient is cured, or ultimately passes away, indicates the end of a
PCP. The PCP is a result of clinical decision making to be discussed in Section 4.
Definition 4 (Personal clinical pathway). A PCP is the execution log of a clinical
pathway (e.g., Figure 2). where is a decision, i.e., , is an action, i.e., , and
indicates patient outcome or state, i.e., . Each task is associated with a
time t to denote the time of occurrence. Other elements such as assessment and vari-
ance are captured as well in a similar way but not shown here.
4 Patient-centric Decision Making Process
In this section, we describe patient-centric decision making. Medical knowledge for
decision points is formulated in rules to derive recommendations based on best prac-
tice or patient choices. Patient preferences are collected through context-building with
a patient conversation model and giving patients more responsibility.
4.1 Medical Rules
Rules embody medical knowledge and are used to help make complex decisions in
clinical pathways through logical reasoning. For example in Figure 4, N2 is a decision
node to decide the next step (treatment or further evaluation) based on patient diagno-
sis results. A number of medical rules can be associated with node N3 (to be dis-
cussed in Table 3). Integrating these rules and applying results from rule-based rea-
soning into a clinical pathway is critical for implementing evidence-based practice.
In addition, each rule is associated with a strength of evidence (SOE) value to indi-
cate its reliability. AHRQ has developed a three-level quality-rating system for classi-
fying SOE into levels A, B and C (see Table 2). Further, we add another category D to
include latest evidence from new research results. Rules from all categories are auto-
matically triggered and provide results to patients and care providers. We describe the
algorithm for rule-based reasoning in Section 4.3.
Table 2. Classification criteria for strength of evidence (SOE)
Symbol Description Source of evidence Level of evidence*
A Good evidence Evidence from well-conducted random-ized controlled trials’ or cohort studies
Levels I-III
B Fair evidence Evidence from other types of studies Levels IV-VI
C Expert opinion N/A Level VII
D New evidence Latest research results N/A
*Note: please refer to AHRQ quality of rating system for details
Example 3 (Medical rules for heart failure). Table 3 shows several example rules
acquired from AHRQ guidelines [14]. Their reliability is indicated by the SOE value
in the last column. They are categorized in a way that each rule is associated with a
decision node in the clinical pathway in Figure 4. For example, rule R1 is used to
decide whether a patient needs further examination and diagnostic testing (T14) or not
(End node). Similarly, R2 and R3 are associated with two different decision nodes in
T14, which refer to another guideline (or subplan) which is not expanded in Figure 4.
R3 is more complex and has two levels of reasoning. Finally, the medication rules
R4-R6 show which medication therapy is appropriate along with their SOE levels.
Table 3. Example rules for heart failure based on AHRQ guidelines [14]
Category # Description of guideline rules SOE
Initial evaluation
R1 (N5)
If a patient has the following symptoms: awakening from sleep with shortness of breath or shortness of breath upon lying down or new-
onset dyspnea on exertion
Then the patient should undergo evaluation for heart failure
Unless PHR and physical exam clearly indicates a noncardiac cause
B
Physical
examination
R2
(T14)
If a patient shows symptoms that are highly suggestive of heart failure
Then the patient should undergo ECHO or RVG test to measure EF Even if physical signs of heart failure are absent
C
Diagnostic testing
R3 (T14)
If a patient is suspected of clinically evident heart failure Then practitioners should perform: a chest x-ray; ECG; complete
blood count; serum electrolytes … liver function tests; and urinalysis;
If the patient is over 65 with no obvious etiology
Then a T4 and TSH level should be checked
C
Medication R4
(T10)
If a patient’s systolic blood pressures < 90 mmHg and
there is a higher risk of complications Then prescribe ACE inhibitors managed by an experienced physician
A
R5 (T10)
If a heart failure patient has symptoms: fatigue or mild Dyspnea on exertion; and he has no other signs or symptoms of volume overload
Then ACE inhibitors may be considered
C
R6
(T10)
If a patient has heart failure and signs of significant volume overload
Then the patient should be started immediately on a diuretic
C
4.2 Context-building through a Patient Conversation Model (PCM)
A medical decision is context-dependent, where context is patient specific and can be
collected and summarized by pre-asking questions of a patient at a previous point of
time. For example, in Figure 4, context that is used at node T3 (detection and treat-
ment) can be collected prior to that point, e.g., at node T1 (pre-admission) or T2 (pa-
tient admission). Then, depending on the patient answers, the subsequent questions
need to be adjusted or customized. We use a patient conversation model (PCM) to
describe all questions used at a specific point of care for a specific disease. This mod-
el is process-aware since context becomes increasingly available as the care process
proceeds. It is obviously knowledge-dependent since the questions for managing heart
failure and managing pediatrics will be totally different.
Example 4 (Patient conversation model for heart failure). Fig. 5 shows an exam-
ple patient conversation model represented in a decision tree. The top part is derived
from medical guidelines or rules in Table 3. The other two parts are developed based
on practical experience and patient needs. These questions are available for patients to
answer any time prior to T3 (detection and treatment) in the clinical pathway of Fig-
ure 4. For example, a patient can enter her answers at home or while waiting for ex-
amination. As a result, the answers are stored in PIM. Some answers become a part of
PHR while some of PPP (see Example 1). In future, we aim to interview clinicians
and patients to get a complete set of questions. In a strict sense, they are context de-
pendent within a clinical pathway. The models can be built, shared and adapted to
specific healthcare settings in the same way that medical guidelines are adapted. We
also aim to automate the techniques for constructing conversation model trees.
Fig. 5. A partial patient conversation model in a decision tree
Via the conversation model, we also give an opportunity to the patient to ask ques-
tions of the provider. For example, if the system suspects a patient of hypertensive
heart failure, a patient may ask the treatment options she may have and details associ-
ated with each option (e.g., expected recovery time, cost, side effects, and success
rate). In general, when patients take the steps to proactively seek information regard-
ing their illness, they are in a more advantageous position to share their specific
knowledge and concerns with their doctors. Thus care providers spend less time to
make patients aware of readily accessible information while ensuring a patient-centric
decision. In practice, we should also consider other aspects of context, such as those
related to clinical staff (e.g., expertise level), resources (e.g., availability), etc.
4.3 The Decision Algorithm
Medical decision making should follow best practice through medical rules (in Table
3) and take into account patient information from context-building (in Figure 5). Fig-
ure 6 describes the algorithm for patient-oriented decision making. When a decision
node D is reached, we retrieve the rule set RS associated with D, run them against
PHR and get results. Other options not triggered by rules will still be presented (with
notation SOE = “N/A”) to patients who know that no guideline supports this option.
In addition, SOE values and patient preference levels (from PPP) are shown. Exam-
ple 5 shows how this algorithm works continuing with the above examples.
Algorithm: patient-oriented decision making (when decision node D is reached)
Input: personal health record PHR, personal preference profile PPP, decision node D
Output: options list options
1. Put all the action nodes from the outgoing branches of D into a list option_list
2. Define rule set RS = rules associated with decision node D
3. Run RS against PHR and get the result vector result_list //a subset of option_list 4. Define a temporary tuple <option, rule, pref>
5. FOR each item op in option_list
6. tuple.option = op.name //assign the option name
7. tuple.pref = preference_table.findPref(op.name) //assign preference from Figure 3 8. If op is in result_list //this option is a result rule-based reasoning or best practice
9. tuple.rule = the content of the rule that trigger this action op (with SOE value)
10. Else //op is not option from guideline, no rule is applied but this is still an option
11. tuple.rule=“N/A” 12. Insert tuple into options
13. END
14. Rank options first based on SOE (AC), then based on patient preference (highlow)
15. Return options
Fig. 6. Algorithm for patient-oriented decision making
Example 5 (Patient-centric decision making). Fig. 7 shows an example of decision
making at node N2 (Diagnosis). During initial evaluation, this patient underwent a
physical exam and diagnostic testing. Her signs indicate that she might have had heart
failure. Specifically, her systolic blood pressure is 85 mmHg and there is a high risk
of complications. Her previous symptoms (a part of PHR) include shortness of breath
upon exertion and while sleeping, volume overload, and a little chest pain. According
to the decision algorithm in Figure 6, we get the option_list = {T3, T5, T6, T7, T8,
T9} and rule set RS (line 1-2). The result of rule-based reasoning against PHR is re-
sult_list = {T5, T7} (line 3), since only two rules are triggered for her PHR: R4 and
R6 (see Table 3). Then we go through each option in option_list, if an option (such as
R4 and R5) is triggered by rules, assign its SOE value from Table 2 and preference
value from Figure 3 (line 5-9). Otherwise, if a rule is not triggered such as R5, the
value for its SOE will be “N/A”, indicating it is not applied (line 10-11). The output
of the decision algorithm is shown in the table below. Thus, ACE inhibitor and Diu-
retics are recommended based on best practice (SOE equals A and C respectively).
Nevertheless, patients and doctors are allowed to choose other options. For example,
if the actual action is a Beta-blocker therapy, then the system will detect such devia-
tion in real-time and ask the physician to enter reasons for it, perhaps at a later time.
Medication therapy (T2)
Patient and family consulting (T3)
Beta blocker (T6)
Diuretics
(T7)CABG (T8) PTCA (T9)
Heart failure
patients with angina,
or history of MI
Patients with signs
of heart failure
Patient
information
model
Diagnosis? (N2)
Surgery treatment (T4)
R4,R5… …
R4, R5, R6 …
R1, R2, R3…Initial evaluation
(T1)
No signs of heart failure
Options Pros Side effect/Risk Prcd. Cost SOE Pref
ACE
inhibitor
Reduce mortality in severe heart
failure…
Decrease in blood pressure,
increase in potassium …
… 1.5k R4 (A) 2
Diuretics Treat various conditions, e.g., high BP Nausea, dizziness, fatigue... … 0.8k R6 (C) 2