Harold S. Luft

Introduction: In a fragmented health care system, research can be challenging when one seeks to follow cancer patients as they seek care which can continue for months or years and may reflect many physician and patient decisions. Claims data track patients, but lack clinical detail. Linking routine electronic health record (EHR) data with clinical registry data allows one to gain a more complete picture of the patient journey through a cancer care episode. However, valid analytical approaches to examining care trajectories must be longitudinal and account for the dynamic nature of what is “seen” in the EHR.Methods: The Oncoshare database combines clinical detail from the California Cancer Registry and EHR data from two large health care organizations in the same catchment area—a multisite community practice and an academic medical center—for all women treated in either organization for breast cancer from 2000 to 2012. We classified EHR encounters data according to typical periods of the cancer care episode (screening, diagnosis, treatment) and posttreatment surveillance, as well as by facility used to better characterize patterns of care for patients seen at both organizations.Findings: We identified a “treated” cohort consisting of women receiving interventions for their initial cancer diagnosis, and classified their encounters over time across multiple dimensions (type of care, provider of care, and timing of care with respect to their cancer diagnosis). Forty-three percent of the patients were treated at the academic center only, 42 percent at the community center only, and 16 percent of the patients obtained care at both health care organizations. Compared to women seen at only one organization, the last group had similar-length initial care episodes, but more frequently had multiple episodes and longer observation periods.Discussion: Linking EHR data from neighboring systems can enhance our information on care trajectories, but careful consideration of the complexity of the treatment process and data generating mechanisms is necessary to make valid inferences.Conclusion/Next Steps: If analyzed as a timeline, and with careful characterization of diagnostic tests, surgical interventions, and type and frequency of physician encounters, the pathways taken by women through their breast cancer episode may lead to better understanding of patient decisions.

To determine if patient clinical conditions need to be considered when assessing primary care physician (PCP) workload in the context of standardizing panel sizes. Work resource value units (wRVUs) were used to standardize PCP panel workload. Standardized panels were created using (1) age and sex- and (2) clinical condition-based risk indicators. Billing data were used for all patients, regardless of insurance, for PCPs in a group practice (n = 190). Weighting methods were assessed for subgroups based on PCP specialty (family medicine, internal medicine, and pediatrics) and patient age (adults vs children) and for different levels of aggregation (patient vs PCP). Groupwide weights based on wRVUs of all primary care services delivered during the year were applied to individual patients and then aggregated to PCP panels. For age/sex weighting, only patient age and sex were taken into account. For condition-based weighting, 1275 disease categories, based on a combination of episode treatment groups (ETGs) and age and/or sex, were used. As expected, at the patient level, condition-based weights were far more discriminative than age/sex. At the PCP level, this discrimination was less important; panel weights varied 1.9- (age/sex-based) to 2.6-fold (condition-based) across PCPs. Correlations between the 2 weighting methods were high (r = 0.93) for child panels and moderate (r = 0.71) for adult panels (all P <.001). The heterogeneity of PCP panels should be considered when assessing PCP workload for panel management. Panel variability in workload is well captured by age/sex-based weights for children, but for adults condition-based adjustment may be necessary.

Comparative effectiveness research is sometimes best done using routinely collected data from multiple real world settings. This raises important issues with respect to the authorizations to use the data, dealing with the multiple IRBs that may have oversight authority, and protecting it from breaches. Careful delineation of what is quality improvement vs. research-driven interventions can help with IRB reviews, especially for low-risk projects. It is impossible to totally de-identify data without markedly reducing its value for research, but creative strategies can markedly reduce the risk of advertent disclosures of patient identities.

Much of health services research seeks to inform particular policy choices and is best characterized as policy-driven research. The reverse, research-driven policy, occurs when studies alter how people perceive reality, which eventually leads to new policy. An example of the latter is nearly four decades of work by John Wennberg and colleagues. Observing variations in practice across small geographic areas led to the notion that some care is preference sensitive, whereas other care is supply constrained. For the former, patient, rather than physician, preferences should be honored, after acquiring and effectively communicating the best available information on the benefits and risks of treatment options. Finding that areas with high use of services have no better quality or outcomes than do areas with lower use led to the notion of accountable care organizations (ACOs). Eventually, both patient engagement and ACOs were written into the Affordable Care Act of 2010.

Advocates for consumer-friendly public reporting on the performance of health care providers anticipate that, at some point, well-vetted and standardized measures will be widely available to help patients choose clinicians who provide the best care. However, achieving that goal would require assembling standardized data from many sources. Such an effort would raise concerns, including privacy considerations about having a single massive data repository; questions of how such an effort would be funded; and potential misuse of the data. This paper proposes creating a public-private data aggregator that would receive patient and provider data from payers that are deidentified in such a way as to remain useful for consumer-reporting and research purposes. The aggregator could be funded through fees charged to commercial users. Meanwhile, registered researchers putting their methods and findings in the public domain could access the data aggregator for free.

Researchers often focus on the data and methods to assess policy changes, but data and methods can also be policy tools. To improve, health care systems need mechanisms and incentives for continually gathering, assessing, and acting on data. This requires (1) more comprehensive data, (2) converting data into information, and (3) incentives to apply that information. Restructured economic incentives can encourage clinicians to increase value (higher quality and/or lower cost) for their patients. While necessary, incentives are not sufficient-information is also needed. Incentives can lead clinicians to demand better information. Much of the necessary data is already used in patient care and billing; some additional variables will come directly from patients. The notion builds on two concepts: collective intelligence and positive deviance. The former characterizes knowledge gained from observing the behavior of many independent actors adapting to changing situations. Positive deviants are those who achieve far better results than expected. By rewarding positive deviants, rather than trying to identify and "correct" those who are problematic, providers will voluntarily identify themselves and their methods for achieving superior outcomes.

Economics influences how medical care is delivered, organized, and progresses. Fee-for-service payment encourages delivery of services. Fee-for-individual-service, however, offers no incentives for clinicians to efficiently organize the care their patients need. Global capitation provides such incentives; it works well in highly integrated practices but not for independent practitioners. The failures of utilization management in the 1990s demonstrated the need for a third alternative to better align incentives, such as bundling payment for an episode of care. Building on Medicare’s approach to hospital payment, one can define expanded diagnosis-related groups that include all hospital, physician, and other costs during the stay and appropriate preadmission and postdischarge periods. Physicians and hospitals voluntarily forming a new entity (a care delivery team) would receive such bundled payments along with complete flexibility in allocating the funds. Modifications to gainsharing and antikickback rules, as well as reforms to malpractice liability laws, will facilitate the functioning of the care delivery teams. The implicit financial incentives encourage efficient care for the patient; the episode focus will facilitate measuring patient outcomes. Payment can be based on the resources used by those care delivery teams achieving superior outcomes, thereby fostering innovation improving outcomes and reducing waste.