Clinical practice guidelines and quality metrics often emphasize effectiveness over patient-centered care. In this article, the authors offer three approaches to personalizing quality measurement to ensure patient preferences and values guide all clinical decisions.

1. The Joint Commission Journal on Quality and Patient Safety
Volume 42 Number 3March 2016132
In its landmark report Crossing the Quality Chasm, the Institute
of Medicine (IOM) identified six aims for shaping the future of
health care.1
The report argued that care should be safe, effective,
patient-centered, timely, efficient, and equitable. Some of these
aims necessitate trade-offs with each other. For example, prior-
itizing effectiveness may constrain efficiency, or efficiency may
compromise timeliness. Although there is no inherent conflict
between effective care and patient-centered care, clinical practice
guidelines and quality metrics often emphasize effectiveness over
patient-centered care. In this article, in lieu of “patient-centered
care,” which the IOM defined as “providing care that is respect-
ful of and responsive to individual patient preferences, needs,
and values and ensuring that patient values guide all clinical
decisions,”1(p. 6)
we use the term personalized care.
Quality of care organizations generally advocate for the prac-
tice of evidence-based medicine. Recommendations often flow
from professional society guidelines, which predominantly focus
on effectiveness. Although there is increasing interest in measur-
ing patients’ experiences and perspectives, our quality metrics
have not kept up, as they continue to emphasize whether effec-
tive treatments are given, while the magnitude of the benefit and
the relative value of accompanying harms are less often consid-
ered. After desirable practices are identified, uniformity—doing
things the same way every time—is enhanced through measure-
ment and continuous improvement. Strategies for improving
reliability include reminder systems, checklists, and care bun-
dles. An increasing focus on process measures has improved the
use of, for example, aspirin and statins for coronary artery dis-
ease, angiotensin-converting enzyme (ACE) inhibitors for heart
failure, and appropriate antibiotics for pneumonia.2,3
A one-size-fits-all approach to health care, however, is the
opposite of personalization.4
For situations in which benefits
far outweigh the harms, standardization is appropriate. There
is neither opportunity nor necessity to ask septic patients about
preferences for fluid resuscitation. Although personalized and
uniform care are not mutually exclusive,5
most recommended
interventions are not in the uniform category, and the best that
our current quality metrics do is to allow patients to “decline”
care, which is not the same as supporting informed, personal-
ized decisions. In addition, although clinicians do not always
adhere to recommended services,6
many recommended services
are minimally effective and may come with substantial harms or
costs. In these cases, pursuit of uniform care incorrectly assumes
that all patients share the same values and preferences. We pres-
ent two examples of outpatient quality measures that should be
personalized and explore ways to do so (Sidebar 1, page 133).
In our view, both examples reflect an underlying assumption
that if an intervention is even marginally effective for a popu-
lation, then all patients should get it, while patient preferences,
burden, and cost are typically prioritized lower, or not consid-
ered at all. Hospital leaders across the United States share the
concern over the meaningfulness and unintended consequences
of current quality measurement.7
One advantage of assuming
that all patients share the same values is that it simplifies mea-
suring the quality of care. However, this assumption violates the
aim of personalized care by treating all patients the same.
Three Fundamental Approaches to
Personalizing Quality Measurement
How might we personalize quality measurement to ensure that
care is truly addressing patients’ values, preferences, and experi-
ences? While arguments for personalizing quality measurement
have recently been made,8–10
we explore some of the suggested
approaches for doing so in greater depth and detail. In our view,
the following three fundamental approaches have emerged as the
most promising strategies to personalize quality measurement:
1. Patient-reported measures
2. Patients’ and clinicians’ cogeneration of medical records
3. Decision quality measures
In all three approaches, patients and families should be
heavily involved from start to finish in their design and imple-
mentation, and we call for rapid physician payment reform to
incentivize their use. Although the three approaches are closely
intertwined, we address each separately in turn.
1. PATIENT-REPORTED MEASURES
Integrating patient-reported outcome measures (PROMs)
and patient-reported experience measures (PREMs) into clini-
cal work flow represents an important potential strategy toward
personalizing quality measurement. PROMs focus on symp-
Forum
Time for Quality Measures to Get Personal
John N. Mafi, MD, MPH; Michael B. Rothberg, MD, MPH; Karen R. Sepucha, PhD; Michael J. Barry, MD
Copyright 2016 The Joint Commission

2. The Joint Commission Journal on Quality and Patient Safety
Volume 42 Number 3March 2016 133
toms, functional status, and quality of life as related to health
from the patient’s perspective, while PREMs focus on more gen-
eral aspects of care, such as the humanity of care provided (for
example, treated with respect, waiting times), communication
of information, and engagement in care decisions.11
Medicare’s
HCAHPS (Hospital Consumer Assessment of Healthcare Pro-
viders and Systems) surveys—probably the most widely used
PREMs—are well validated and widely accepted patient experi-
ence measures.12
Routine use of PROMs and PREMs, which we
jointly refer to as patient-reported measures [PRMs]) in clinical
practice can improve processes of care (for example, accurately
diagnosing depression) as well as certain health outcomes (for
example, functional status).13,14
As we have learned from nearly 40 years of health services
research, health status can be measured.15–18
Returning to our
diabetic case example (Sidebar 1), why can’t we periodically
measure health status using validated PRMs, such as the diabe-
tes quality-of-life measure?19
For example, instead of rewarding
organizations that aim to simply maximize the proportion of
patients whose glycosylated hemoglobin (A1C) values are < 7%,
let’s reward organizations that enhance patients’ lives; for exam-
ple, by improving answers to “How often do you feel physically
ill?”; or “How often does your diabetes interfere with your
exercising?” Using the principles of user-centered design,20
we
should create PRM interfaces that use seamless, mobile-ready,
and user-friendly platforms. We also need to engage patients in
survey development from the outset by having them enter data
electronically—whether from home, via a device such as a tab-
let, or in the office waiting room, via a waiting-room kiosk—
which would be fed back into the medical record.21
To ensure
equitable benefit for vulnerable groups, increasing participation
may require targeted strategies such as face-to-face teaching in
office waiting rooms; use of text messaging–based forms; and
use of culturally appropriate language, at the appropriate read-
Sidebar 1. Personalizing Outpatient Quality Measures
References
1. Pace LE, Keating NL. A systematic assessment of benefits and risks to guide breast cancer screening decisions. JAMA. 2014 Apr 2;311(13):1327–1335.
2. Miller AB, et al. Canadian National Breast Screening Study-2: 13-year results of a randomized trial in women aged 50-59 years. J Natl Cancer Inst. 2000 Sep 20;
92:1490–1499.
3. Miller AB, et al. Twenty five year follow-up for breast cancer incidence and mortality of the Canadian National Breast Screening Study: Randomised screening trial.
BMJ. 2014 Feb 11;348:g366.
4. Centers for Medicare & Medicaid Services. 2015 Physician Quality Reporting System (PQRS) Measure Specifications Manual for Claims and Registry
Reporting of Individual Measures, version 9.0. Oct 10, 2014. Accessed Feb 3, 2016. http://www.acr.org/~/media/ACR/Documents/PDF/QualitySafety/Quality%20
Measurement/2015%20PQRS/2015_PQRS_IndividualMeasureSpec_ClaimsRegistry_111014.pdf.
5. Vijan S, et al. Effect of patients’ risks and preferences on health gains with plasma glucose level lowering in type 2 diabetes mellitus. JAMA Intern Med.
2014;174:1227–1234.
Example 1. Screening Mammography
Considerable evidence suggests that screening mammography can
prevent death from breast cancer. The extent of mortality reduction
is less certain, as recent data suggest a lack of mortality reduction
among younger age groups, benefits vary on the basis of risk
profiles, and the number of average-risk women needed to screen to
prevent one death from breast cancer is large—approximately 1,300
women screened for 10 years, beginning at age 50.1–3
Mammography
also entails harms, including a high likelihood of a false-positive
mammogram, along with a smaller risk of overdiagnosis.1
The balance
of benefits and harms is so unclear that professional organizations’
recommendations conflict regarding what age to begin screening,
although the organizations all agree that women should be informed
of the potential benefits and harms. At the same time, quality reports,
including those of Medicare’s Physician Quality Reporting System,
include the percentage of women 50–74 years of age who had a
screening mammogram,4
with the implication that more screening is
better quality. Although no recommended treatment can ever have
absolute and unequivocal benefit, an honest conversation about
the risk-benefit ratio of screening should be discussed with patients
and families to help ensure that screening decisions match patients’
values and preferences. However, clinicians have a disincentive to
discuss potential harms—encouraging informed decision making
might result in fewer women being screened, thus negatively affecting
measured “quality.”
Example 2. Blood Glucose Targets
Poor control of blood glucose increases the risk of long-term
complications of diabetes. Randomized trials have shown that tighter
glycemic control can prevent some of these. However, achieving tight
control, particularly for patients with long-standing disease, can be
challenging. Reducing glycosolated hemoglobin (A1C) below 7%, a
widely used quality metric, often requires multiple oral medications
or insulin, which carry substantial treatment burden. Moreover, for
older patients, there are risks associated with tight glycemic control
and the expected benefits fewer.5
Recently, Medicare added the
percentage of patients aged 18-75 with a hemoglobin A1C of > 9% as
a quality measure.4
Again, while clinical guidelines generally allow for
some flexibility, cruder quality metrics (1) assume that lower is better,
regardless of treatment burden, comorbid diseases, or ability to afford
medications; and (2) implies that a goal of < 9% is low enough, even
though many younger patients and those with new-onset disease
would benefit from tighter control. Many private insurance plans have
A1C quality goals that are set at 8% or even 7%, which leaves health
care providers scrambling to manage different goals for subsets of
their patient population. In each case, measures tied to fixed glucose
targets incentivize the use of more medication with little regard for
what is best for the individual patient.
Copyright 2016 The Joint Commission

3. The Joint Commission Journal on Quality and Patient Safety
Volume 42 Number 3March 2016134
ing level, and in the preferred language.22,23
Despite decades of use of PRMs in research and insurance
company quality assurance, the health care system in the Unit-
ed States has failed to routinely integrate PRM metrics into dai-
ly work flow. By far the biggest barrier to using PRMs is a lack
of any financial incentive, or any penalty for not using them.
There is evidence that this situation may be changing soon.24
Policies that provide direct financial incentives to use PRMs and
also demonstrate clinically meaningful improvements in PRMs
should stimulate acceleration of PRMs into daily clinical and
technological work flows.
2. PATIENTS’ AND CLINICIANS’ COGENERATION OF
MEDICAL RECORDS
Patients’ and clinicians’ collaboration on the visit note rep-
resents a new opportunity for personalization of quality mea-
surement. Some researchers have argued that the note itself
could evolve into jointly generated personal care plans be-
tween patients and clinicians, which may themselves serve as
personalized measures of quality.25,26
Between 40% and 80%
of the information that the clinician provides the patient
during the visit is forgotten, and of the information that is
actually remembered, half of it is factually incorrect.27,28
Part-
ly in response to these deficiencies in patient-clinician com-
munication, the OpenNotes project led a multisite effort in
which 105 primary care physicians invited their 20,000 on-
line portal–registered patients to view their visit notes. Initial
findings revealed that approximately three of four patients re-
ported that they remembered the plan of care better, felt more
in control of their care, and began taking their medications
better; while, on the physician side, there was little impact or
disruption on work flow.25
Since the trial’s completion, most
of the patients have continued to read their notes as long as
they were accompanied by e-mail alerts, but further efforts
are needed to engage nonwhites, who are less likely to view
notes.29
In just three years after the trial’s publication, Open-
Notes has evolved into a national movement: more than five
million patients have gained access to their visit notes.30
Evidence suggests that patients fear challenging clinicians and
want a safe and inviting forum to discuss medical errors and qual-
ity concerns with their providers.31,32
Transparency and patient
commentary in the note could have enormous benefit in this re-
gard. In the case of breast cancer screening, consider the following
patient quotation from an OpenNotes trial participant:
If [OpenNotes] had been available years ago, I would have had my
breast cancer diagnosed earlier, as a previous doctor had written
in my chart and had marked the exact area but never informed
me.33(p. 380)
On the basis of these sentiments, our group has designed
a collaborative OpenNotes–based patient safety reporting tool
in which patients are invited to comment on any errors in the
note, so that, in effect, patients become a part of the process of
quality measurement and assurance. As other members of our
group have reported, the tool, which has shown early feasibili-
ty and success, could make a substantial impact on quality and
patient safety.34
The next phase of OpenNotes will involve patients’ more ac-
tive participation in their own medical records. In an effort we
are calling “OurNotes,” which is intended to improve patient
engagement, patient-clinician communication, and the quality
of care, patients will be invited to cogenerate the medical note
alongside their providers.35
Research has demonstrated that when
patients enter historical data, it improves the quality of the his-
tory of present illness, data accuracy (particularly regarding sen-
sitive topics such as sexual history), and patient satisfaction,36–38
and also saves time during the encounter.21
Cogenerated medical
records such as OpenNotes allow for the first time the patient’s
voice to be entered directly into the electronic health record, in-
cluding setting the agenda for the visit, updating the medical
history, and providing commentary and insight into the medical
plan. Patients also should be able to respond to patient educa-
tion and decision support materials prescribed either at the visit
or separately, with information on their preferences and values,
as well as any questions they may have.
Quality measurement could also involve patients’ and clini-
cians’ cogeneration of personalized care goals, with the clinician
and/or the care team assessing at, say, 3-, 6-, and 12-month in-
tervals whether the patient and clinician achieved their goals.
For example, for an unemployed patient with poorly controlled
diabetes, the clinician’s goal could be to obtain community re-
sources to provide discounted medications and free services to
deliver fresh produce to his or her home, while the patient’s goal
could be adequate adherence to long-acting insulin. Their cosig-
nature of the note could represent mutual pledges to complete
each task as part of a personalized and shared care plan. Person-
alized quality measurement could assess whether the provider
and patient completed each task after, say, 3 or 6 months. To
dissuade clinicians and patients from setting low goals, and to
enable comparisons among clinicians, associated quality metrics
could assess medication adherence through pharmacy claims
data or measure emergency department utilization for diabe-
tes-related encounters. Patients could potentially be reimbursed
their copayment in the form of a check as a way to share savings
and incentivize medication adherence.
Copyright 2016 The Joint Commission

4. The Joint Commission Journal on Quality and Patient Safety
Volume 42 Number 3March 2016 135
3. DECISION QUALITY MEASURES
Shared decision making (SDM), a process in which clinicians
communicate evidence about options and outcomes and elicit
patient preferences, with patients and clinicians jointly making
decisions, has several advantages. First, SDM respects patient au-
tonomy. By treating each patient as an individual, rather than
as a quality statistic, clinicians acknowledge their obligation as
clinicians to maximize the ratio of benefit to harm. Because this
ratio depends on a patient’s unique values, it behooves the cli-
nician to understand those values. Second, care becomes more
efficient because patients are treated according to their clinical
characteristics and wishes rather than reflecting overtreatment
of some patients and undertreatment of others. Third, of the
three approaches to personalizing quality measurement, SDM
has the strongest evidence of benefit; meta-analyses of random-
ized controlled trials have demonstrated treatment decisions
more in line with patients’ values and lower utilization of po-
tentially unnecessary tests and procedures.39
Thus, instead of
measuring how many patients receive an intervention, a more
personalized measure would assess decision quality (for exam-
ple, how many patients were well informed about and received
treatments that matched their preferences). Patients and families
are and should continue to be partners in creating decision aids
and other important strategies to enhance and promote SDM.
Capturing decision quality presents its own challenges, but the
measurement science for this domain is developing rapidly.40–43
In the case of breast cancer screening, measuring decision quality
among women who are 40 to 49 years of age would be preferable
to using screening rates, as the benefits of mammography are less
certain for this age group.44
Decision quality can be measured
with knowledge questions (for example, assessing understanding
of the benefits and risks of mammography) and questions that
elicit women’s goals and screening preferences. For example, in a
randomized trial, using a decision aid among 40-year-old wom-
en improved knowledge and reduced indecision about mam-
mography for breast cancer screening, without causing anxiety.45
Measures of decision quality can be applied to other situations,
such as elective joint surgery,46
and such validated measurement
tools will be paramount. Evaluating providers on their ability to
inform their patients and tailor screening programs to patients’
preferences would encourage personalized care. The use of pa-
tient decision aids, which have been proven to increase decision
quality,45
and their routine use would help achieve this in prac-
tice. Moreover, depending on the clinical context, the screening
decision may be revisited periodically, as effective SDM requires
monitoring to ensure that interventions continue to match pa-
tients’ values and preferences.47
Several obstacles prevent clinicians from routinely engaging
in SDM. Aside from adequate patient and clinician education,
the most critical barrier is a lack of any financial incentive to
motivate change in this direction. We must therefore rapidly re-
form physician payment to realign incentives. In a predominant-
ly fee-for-service system, we need a way to document and bill for
these discussions—otherwise, time-intensive interventions such
as counseling on treatments, which compete for time in an al-
ready overburdened office visit,48,49
will happen less often. The
recent Centers for Medicare & Medicaid Services lung cancer
screening regulations, which entail reimbursement for SDM and
require the use of a decision aid, is a step in the right direction.50
Conclusion
Personalized care is frequently touted in health care these days,
even while guidelines, quality measures, and practice styles still
encourage one-size-fits-all medicine. Although it is not self-evi-
dent that personalizing quality measurement in these ways will
sufficiently personalize care, we believe the three fundamental
approaches to personalizing quality measurement that we have
briefly described represent the most promising path toward a
health care system that better serves the patient’s voice. Person-
alized care, unlike “personalized medicine,” which often refers
to treatment customized to a person’s genome and proteome,
should start with a person’s values and preferences rather than
his or her molecules. J
Dr. Rothberg and Dr. Mafi report no financial conflicts of interest. Dr. Barry is the
chief science officer of Healthwise and president of the Informed Medical Decisions
Foundation, a division of Healthwise, and Dr. Sepucha receives salary support as a
medical editor for Healthwise.
John N. Mafi, MD, MPH, formerly Fellow, General Internal Medicine,
Beth Israel Deaconess Medical Center and Harvard Medical School,
Boston, is a Primary Care Physician and Assistant Professor of Med-
icine, Division of General Internal Medicine and Health Services Re-
search, David Geffen School of Medicine at UCLA, Los Angeles, and
Affiliated Adjunct in Health Policy, RAND Corporation, Santa Monica,
California. Michael B. Rothberg, MD, MPH, is Vice Chair for Re-
search, Medicine Institute, and Director, Center for Value-Based Care
Research, Cleveland Clinic, and Professor of Medicine, Cleveland
Clinic Lerner College of Medicine, Case Western Reserve Univer-
sity. Karen R. Sepucha, PhD, is Director, Health Decision Sciences
Center, Division of General Internal Medicine, Massachusetts Gen-
eral Hospital, Boston, and Assistant Professor in Medicine, Harvard
Medical School. Michael J. Barry, MD, is President, Informed Medi-
cal Decisions Foundation, Boston, and Chief Science Officer, Health-
wise, Boston; Medical Director, John D. Stoeckle Center for Primary
Care Innovation, Massachusetts General Hospital; and Professor of
Medicine, Harvard Medical School. Please address correspondence
to John N. Mafi, jmafi@mednet.ucla.edu.
Copyright 2016 The Joint Commission

5. The Joint Commission Journal on Quality and Patient Safety
Volume 42 Number 3March 2016136
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Copyright 2016 The Joint Commission