Ateev Mehrotra

Many Emergency Department Visits Could Be Managed At Urgent Care Centers And Retail Clinics

Americans seek a large amount of nonemergency care in emergency departments, where they often encounter long waits to be seen. Urgent care centers and retail clinics have emerged as alternatives to the emergency department for nonemergency care. We estimate that 13.7-27.1 percent of all emergency department visits could take place at one of these alternative sites, with a potential cost savings of approximately

Dropping the Baton: Specialty Referrals in the United States

4.4 billion annually. The primary conditions that could be treated at these sites include minor acute illnesses, strains, and fractures. There is some evidence that patients can safely direct themselves to these alternative sites. However, more research is needed to ensure that care of equivalent quality is provided at urgent care centers and retail clinics compared to emergency departments.

Pay for performance in the hospital setting: what is the state of the evidence?

CONTEXT In the United States, more than a third of patients are referred to a specialist each year, and specialist visits constitute more than half of outpatient visits. Despite the frequency of referrals and the importance of the specialty-referral process, the process itself has been a long-standing source of frustration among both primary care physicians (PCPs) and specialists. These frustrations, along with a desire to lower costs, have led to numerous strategies to improve the specialty-referral process, such as using gatekeepers and referral guidelines. METHODS This article reviews the literature on the specialty-referral process in order to better understand what is known about current problems with the referral process and what solutions have been proposed. The article first provides a conceptual framework and then reviews prior literature on the referral decision, care coordination including information transfer, and access to specialty care. FINDINGS PCPs vary in their threshold for referring a patient, which results in both the underuse and the overuse of specialists. Many referrals do not include a transfer of information, either to or from the specialist; and when they do, it often contains insufficient data for medical decision making. Care across the primary-specialty interface is poorly integrated; PCPs often do not know whether a patient actually went to the specialist, or what the specialist recommended. PCPs and specialists also frequently disagree on the specialists role during the referral episode (e.g., single consultation or continuing co-management). CONCLUSIONS There are breakdowns and inefficiencies in all components of the specialty-referral process. Despite many promising mechanisms to improve the referral process, rigorous evaluations of these improvements are needed.

The Association Between Health Care Quality and Cost: A Systematic Review

More than 40 private sector hospital pay-for-performance (P4P) programs now exist, and Congress is considering initiating a Medicare hospital P4P program. Given the growing interest in hospital P4P, this systematic review of the literature examines the current state of knowledge about the effect of P4P on clinical process measures, patient outcomes and experience, safety, and resource utilization. Little formal evaluation of hospital P4P has occurred, and most of the 8 published studies have methodological flaws. The most rigorous studies focus on clinical process measures and demonstrate that hospitals participating in the Centers for Medicare and Medicaid Services-Premier Hospital Quality Incentive Demonstration, a P4P program, had a 2- to 4-percentage point greater improvement than the improvement observed in control hospitals. There is a need for more systematic evaluation of hospital P4P to understand its effect and whether the benefits of investing in P4P outweigh the associated costs. (Am J Med Qual 2009;24:19-28)

Nurse-managed health centers and patient-centered medical homes could mitigate expected primary care physician shortage.

BACKGROUND Although there is broad policy consensus that both cost containment and quality improvement are critical, the association between costs and quality is poorly understood. PURPOSE To systematically review evidence of the association between health care quality and cost. DATA SOURCES Electronic literature search of PubMed, EconLit, and EMBASE databases for U.S.-based studies published between 1990 and 2012. STUDY SELECTION Title, abstract, and full-text review to identify relevant studies. DATA EXTRACTION Two reviewers independently abstracted data with differences reconciled by consensus. Studies were categorized by level of analysis, type of quality measure, type of cost measure, and method of addressing confounders. DATA SYNTHESIS Of 61 included studies, 21 (34%) reported a positive or mixed-positive association (higher cost associated with higher quality); 18 (30%) reported a negative or mixed-negative association; and 22 (36%) reported no difference, an imprecise or indeterminate association, or a mixed association. The associations were of low to moderate clinical significance in many studies. Of 9 studies using instrumental variables analysis to address confounding by unobserved patient health status, 7 (78%) reported a positive association, but other characteristics of these studies may have affected their findings. LIMITATIONS Studies used widely heterogeneous methods and measures. The review is limited by the quality of underlying studies. CONCLUSION Evidence of the direction of association between health care cost and quality is inconsistent. Most studies have found that the association between cost and quality is small to moderate, regardless of whether the direction is positive or negative. Future studies should focus on what types of spending are most effective in improving quality and what types of spending represent waste. PRIMARY FUNDING SOURCE Robert Wood Johnson Foundation.

Implementing Open-Access Scheduling of Visits in Primary Care Practices: A Cautionary Tale

Numerous forecasts have predicted shortages of primary care providers, particularly in light of an expected increase in patient demand resulting from the Affordable Care Act. Yet these forecasts could be inaccurate because they generally do not allow for changes in the way primary care is delivered. We analyzed the impact of two emerging models of care--the patient-centered medical home and the nurse-managed health center--both of which use a provider mix that is richer in nurse practitioners and physician assistants than todays predominant models of care delivery. We found that projected physician shortages were substantially reduced in plausible scenarios that envisioned greater reliance on these new models, even without increases in the supply of physicians. Some less plausible scenarios even eliminated the shortage. All of these scenarios, however, may require additional changes, such as liberalized scope-of-practice laws; a larger supply of medical assistants, licensed practical nurses, and aides; and payment changes that reward providers for population health management.

Estimating the Costs of Medicalization

Context Open-access scheduling offers appointments to patients at or close to a time of their choosing. Contribution The authors helped 6 primary care practices implement open-access scheduling. Five of the 6 reduced waiting times for appointments, but none could provide same-day access or sustain the initial reductions over time. Also, patient and staff satisfaction was unchanged and no-show rates remained the same. Caution Implementation of the system differed too much among the practices to formally compare its effects. Implication Open-access scheduling improves appointment access. However, not all practices can implement it and it has variable effects in those that can. The Editors Many primary care practices in the United States seem to be overwhelmed by patient demand (1). Routine appointments with physicians are booked far in advance, and urgent appointments are often added to already full schedules. Precious staff time is devoted to triaging patients. Patients often receive urgent care from physicians other than their own primary physicians, which may disrupt continuity of care (2, 3). Some patients go to hospital emergency departments for nonemergent problems because their primary care providers are not accessible (4). Frustrated by these shortcomings in the current system, many practices are considering open-access scheduling as a way to provide patients better access to care. In open-access scheduling, patients call the practice and are offered a prompt appointment, ideally on the same or next day, no matter what the reason for the visit. After being seen, a patient is given a time frame for follow-up (for example, 2 months), and when the patient calls within that time frame, he or she can be seen the same day or soon after. Proponents contend that open-access scheduling eases work pressures on physicians, improves practice efficiency, increases patient satisfaction, and fulfills the Institute of Medicines call for timely access and more patient-centered care (57). Some health care organizations have described the successful implementation of open-access scheduling. A Kaiser Permanente practice reduced the waiting time for routine appointments from 55 days to 1 day in less than 1 year (5). Other reports of success in implementing open access (810) have prompted some large health care systems, including the U.S. Veterans Administration system and the United Kingdom National Health System, to implement open-access scheduling (11, 12). Despite this interest in open-access scheduling, most published studies have lacked detailed evaluations and relatively few studies have assessed the effect of implementing open-access scheduling on outcomes beyond appointment availability (6, 8, 10, 1320). We evaluated the effect of open-access scheduling on appointment availability, no-show rates, and patient and staff satisfaction with appointment availability in a case series of 6 practices. We also examined potential barriers to implementation to guide others who may consider this increasingly popular approach to primary care scheduling. Methods In 2003, Partners Community Healthcare initiated an internal study to assess the effect of open-access scheduling in 6 practices recruited from the approximately 100 practices in the Partners Community Healthcare network in eastern Massachusetts. The Partners Human Subjects Committee approved the study protocol. Open Access Implementation Each practice selected a leadership team, typically including the medical director, nursing director, and practice manager. This team worked with Partners Community Healthcare support staff to create an implementation and communication plan. Support staff included a full-time open access project manager and 2 assistants, who together provided coaching, training, and data analysis. Outside national experts in implementing open access provided further support as consultants. Table 1 lists the general steps taken to implement the model, which were adapted from previous descriptions of the conceptual framework and keys to implementation of the open-access model (2, 3). After the practices developed an implementation plan, an official go-live or implementation date was set. However, the model was implemented gradually, and many steps were often introduced before this official date. Table 1. Elements of Open Access Implementation Each practice had a monthly team meeting that was facilitated by the open access implementation team. The team also conducted multiple on-site visits that included coaching and training of all practice staff. Real-time feedback was provided to the practices in these meetings and visits, enabling the practices to adapt the implementation plan. Outcomes and Data Collection The practices simplified various appointments into 2 types: short and long. Short appointments were scheduled for 15 minutes and included both urgent and routine follow-up appointments. Long appointments were scheduled for 30 minutes and included preventive health examinations (physicals) and new patient appointments. We also categorized preimplementation appointment types as either short or long to track trends before and after the introduction of open-access scheduling. The primary study outcomes were times to third available short and long appointments. The waiting time for the third available appointment is considered a more accurate and stable reflection of true appointment availability because the first and second available appointments can often be chance openings caused by patient cancellations (2, 21). We calculated the third available appointment measure for each practice by averaging the time to the third available appointment for each provider in the practice, weighted by the number of practice sessions the provider worked each week. In calculating the time to third available appointment, we counted calendar days (including weekends) and days off. Although part of implementing open access includes eliminating carve-out appointments (appointments that are closed for scheduling until they are made available for urgent visits on the day of or the day before those visits), 1 practice retained these appointments, and we did not count those appointments unless they had been released for booking. We also did not count appointments for providers on maternity leave or other extended absences and those for temporary and urgent care providers, although we did include providers on vacation. Three members of the open access implementation team collected all reported measurements of time to third available appointments at a consistent day and time (most often Mondays before the practices opened), either manually or by automated scheduling systems. We checked the data to ensure that the 3 study staff were collecting data in a similar manner. We collected data on time to third available appointments at least once a month during the implementation period. We trained practice staff to measure their own times to third available appointments, and we collected data less frequently when practices began their own data collection or when no changes were being made within the practice. We did not include third available appointment times collected by practices in our analysis. We followed practices for different durations before and after implementation because the start of the planning process was staggered and the duration of the planning process varied. Patient satisfaction, staff satisfaction, and no-show rates were our secondary outcomes. We measured patient satisfaction by using 2 different instruments. At the first 3 practices, all patients in a 1-month period received patient satisfaction surveys when they arrived for their appointment. We could not track response rates because the patients place their completed surveys in a drop box themselves. The question of interest asked respondents to evaluate their satisfaction with the length of time you waited to get your appointment today on a 5-point Likert scale. At the other 3 practices, we used the Ambulatory Care Experiences Survey (22). In this survey, respondents were asked: In the last 6 months, when you scheduled an appointment to see your personal doctor, how often did you get an appointment as soon as you needed it? At each of these 3 practices, a survey in both Spanish and English was mailed to 200 randomly selected patients seen in a 2-week period. Up to 2 reminder letters were sent to nonrespondents. We measured staff satisfaction by using a short written survey administered at staff meetings attended by physicians, nurses, medical assistants, front-office staff, and practice managers, which asked them to evaluate access to appointments at the practice on a 5-point Likert scale. Staff who missed the meetings received a survey to complete confidentially afterward. We administered these surveys at essentially the same time as patient satisfaction surveys at the times noted by vertical arrows in the Figure . Figure. Change in time to third available appointment. Time to third appointment for long and short visits from 180 d before to 720 d after implementation at the 6 practices. Vertical lines indicate the day of implementation; arrows indicate timing of patient and staff surveys. Practice numbering does not correspond to the order of practices in Table 2. We calculated no-show rates by using scheduling system reports and included all patient visits, both before and after implementation, through at least the time of the postimplementation satisfaction surveys. Statistical Analysis We calculated the time to third available appointment in 3 periods: up to 1 month before the official implementation date, 1 month before to 4 months after implementation, and 4 to 24 months after implementation. We used Loess smoothing techniques in SAS, version 9.1 (SAS Institute, Cary, North Carolina) (23), to chart trends in time to third available appointment at each practice. For the other outcome variables, we compare

Visits To Retail Clinics Grew Fourfold From 2007 To 2009, Although Their Share Of Overall Outpatient Visits Remains Low

Medicalization is the process by which non-medical problems become defined and treated as medical problems, usually as illnesses or disorders. There has been growing concern with the possibility that medicalization is driving increased health care costs. In this paper we estimate the medical spending in the U.S. of identified medicalized conditions at approximately

Episode-Based Performance Measurement And Payment: Making It A Reality

77 billion in 2005, 3.9% of total domestic expenditures on health care. This estimate is based on the direct costs associated with twelve medicalized conditions. Although due to data limitations this estimate does not include all medicalized conditions, it can inform future debates about health care spending and medicalization.

Developing a natural language processing application for measuring the quality of colonoscopy procedures

Retail clinics have rapidly become a fixture of the US health care delivery landscape. We studied visits to retail clinics and found that they increased fourfold from 2007 to 2009, with an estimated 5.97 million retail clinic visits in 2009 alone. Compared with retail clinic patients in 2000-06, patients in 2007-09 were more likely to be age sixty-five or older (14.7 percent versus 7.5 percent). Preventive care-in particular, the influenza vaccine-was a larger component of care for patients at retail clinics in 2007-09, compared to patients in 2000-06 (47.5 percent versus 21.8 percent). Across all retail clinic visits, 44.4 percent in 2007-09 were on the weekend or during weekday hours when physician offices are typically closed. The rapid growth of retail clinics makes it clear that they are meeting a patient need. Convenience and after-hours accessibility are possible drivers of this growth. However, retail clinics make up a small share of overall visits in the outpatient setting, which include 117 million visits to emergency departments and 577 million visits to physician offices annually.