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Time for Quality Measures to Get Personal

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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.

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  • Personalized care or evidenced based care? Maybe we should ask the patient. #ptengagement
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Time for Quality Measures to Get Personal

  1. 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. 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. 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. 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. 5. The Joint Commission Journal on Quality and Patient Safety Volume 42 Number 3March 2016136 References 1. Institute of Medicine. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press; 2001. 2. Lindenauer PK, et al. Public reporting and pay for performance in hospital quality improvement. N Engl J Med. 2007 Feb 1;356:486–496. 3. Kale MS, et al. Trends in the overuse of ambulatory health care services in the United States. JAMA Intern Med. 2013 Jan 28;173:142–148. 4. Aron DC, Pogach LM. One size does not fit all: The need for a continuous measure for glycemic control in diabetes. Jt Comm J Qual Patient Saf. 2007; 33:636–643. 5.Timmermans S, Berg M. The Gold Standard : The Challenge of Evidence-Based Medicine and Standardization in Health Care. Philadelphia: Temple University Press, 2003. 6. Mafi JN, et al. Worsening trends in the management and treatment of back pain. JAMA Intern Med. 2013 Sep 23;173:1573–1581. 7. 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Ann Intern Med. 2007 Apr 3;146:511–515. 45. Mathieu E, et al. Helping women make choices about mammography screening: An online randomized trial of a decision aid for 40-year-old women. Patient Educ Couns. 2010;81:63–72. 46. Sepucha K, et al. Factors associated with the quality of patients’ surgi- cal decisions for treatment of hip and knee osteoarthritis. J Am Coll Surg. 2013;217:694–701. 47. Mol A. The Logic of Care: Health and the Problem of Patient Choice. London: Routledge, 2008. 48. Abbo ED, et al. The increasing number of clinical items addressed during the time of adult primary care visits. J Gen Intern Med. 2008;23:2058–2065. 49. Mafi JN, et al. Trends in the ambulatory management of headache: Analysis of NAMCS and NHAMCS data 1999-2010. J Gen Intern Med. 2015;30:548–555. 50. Centers for Medicare & Medicaid Services. Decision Memo for Screening for Lung Cancer with Low Dose Computed Tomography (LDCT) (CAG- 00439N). Feb 5, 2015. 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