Feifei Wei, PhD

To evaluate the effects of providing appropriateness criteria through guideline-based electronic health record (EHR) decision support for high-tech diagnostic imaging (HTDI) procedures.

Chart audits were performed on a random sample of adult primary care orders for 3 HTDI procedures (computed tomography [CT] and magnetic resonance imaging [MRI] of the head, and MRI of the lumbar spine) before and after implementation of an EHR decision support system. 

Level of appropriateness, abnormal findings, and apparent effects on patient care.

A total of 299 eligible audits were performed. Decision support was associated with a 20% to 36% drop in spine MRI and head CT orders, but head MRI order volume was unaffected. Combined results for the 3 procedures showed that a larger proportion of studies ordered after implementing decision support (89.2% vs 79.5%, 

 = .02) fit appropriateness criteria, and more postimplementation studies had A ratings (highest utility rating) (81.8% vs 70%, 

 = .04). However, there were no differences in the proportion of tests with positive findings (42/132 vs 28/120, 

criteria) or the proportion with a likely impact on patients (6.6% vs 10.8%,

 These data support the feasibility of using chart audits to assess the relationship between appropriateness criteria and HTDI orders. Although introduction of EHR clinical decision support for diagnostic imaging orders was associated with reduced volume and increased appropriateness of orders, there was little apparent impact on either findings or patients.

Implementation of decision support in the electronic medical record system of a large medical group reduced the volume and increased the appropriateness of high-tech diagnostic imaging orders for head and spine scans, although the effects varied by procedure.

There was no increase in the proportion of scans with positive findings.

Few scans before or after implementation appeared likely to have affected patient outcomes.

Scans were as likely to affect the patient care process (usually by adding services) as to improve patient outcomes.

Many policymakers suggest that conversion to electronic health records (EHRs) will improve the quality and cost of medical care.

 Others argue that such benefits will require that EHRs include at least electronic decision support.

 However, there still is not much evidence about how much value electronic decision support adds. The systematic review of controlled trials of computerized decision support systems by Garg et al concluded that two-thirds of 97 trials showed some improvement in care processes, but only 7 of 52 showed improvement in patient outcomes.

One area in which electronic decision support is likely to be helpful is in high-tech diagnostic imaging (HTDI) test orders. These very costly tests have been increasing at a rapid rate nationally over the past 15 years.

 Published studies suggest that this increase is occurring among tests ordered by both radiologists and nonradiologists and that it is associated with wide geographic variation in rates. As a result, there is increasing pressure to encourage adherence to appropriateness criteria, a situation for which electronic decision support seems well suited.

Rosenthal et al reported preliminary results from the implementation of an electronic decision support system for HTDI procedures at Massachusetts General Hospital, showing that it was acceptable to physicians and appeared to change ordering habits.

 However, this conclusion was based on changes in concordance with appropriateness criteria in the decision support system over time, with no data comparing such concordance before and after the system began. Moreover, there is little direct evidence on which to base appropriateness criteria. Thus, we know little about the effects on patient care processes or outcomes of applying these criteria. To better understand these issues, we studied the impact of implementing a decision support system for HTDI in the EHR of a large multispecialty medical group. The following questions were addressed: 

1. Was there sufficient information in the medical chart to permit measurement of appropriateness criteria for HTDI test orders?

2. Did compliance with appropriateness criteria change?

3. Did positive findings or effects on patients change?

This study was conducted within a 600-physician multispecialty medical group in the Twin Cities region of Minnesota that serves an active patient population of 400,000 patients. Since 2004, its primary care clinics have used the Epic paperless EHR that includes computerized orders for all tests and incorporates either electronic or scanned reports of those test results.

In February 2007, the medical group rapidly implemented a decision support system within Epic for HTDI tests. The system used appropriateness criteria from the American College of Radiology that have ratings of A (high utility), B (intermediate utility), and C (low utility). Because the EHR ordering system required that a reason be entered for all orders, and because these criteria could not be expected to cover all circumstances, an “other” alternative also was available, which collected unstructured information for about 15% (8%-19%) of orders for the procedures we studied. Only completed orders were recorded in the EHR.

The system did not prevent orders, regardless of criteria fit. Physicians received little feedback on their order results (although system use identified the appropriateness of each order). There were no financial incentives or disincentives associated with ordering patterns.

To avoid the preparation or acclimatization periods before or after implementation for this study, as well as to avoid any seasonal effects on procedure orders, we selected 2-month windows (6 months before and 6 months after the decision support system was implemented). We studied 3 of the more commonly ordered HTDI procedures: computed tomography (CT) and magnetic resonance imaging (MRI) of the head, and MRI of the lumbar spine, because these procedures have relatively fewer and less complex ordering reasons than many others.

 Our study was limited to original orders (ie, not follow-up of a previous test) by a primary care clinician for adults (age 19 years and over) in our primary care clinics to further simplify and standardize the analysis.

The implemented system contained 72 criteria for CT of the head and 68 for MRI of the head, but only 27 for MRI of the lumbar spine. “A” ratings were associated with 65% of these criteria for CT of the head, whereas 85% of the criteria for the other 2 procedures had A ratings. If the apparent reason in the chart for ordering a test did not resemble any of the existing criteria, we recorded this test as lacking a criterion fit.

Fifty cases of each completed procedure were drawn randomly during the selected months before and after implementation of decision support. They were excluded if the order had actually come indirectly from a specialty consultant, or was a follow-up to an earlier test.

Audits were performed by 2 experienced nurse auditors using predetermined definitions. The 2 physicians in our group (LIS, JB) separately reviewed every case with positive findings to confirm the audit results and decide on the patient impact, followed by discussion between them until agreement was reached. During the training period for the audit, all auditors reviewed the same 15 cases, and we as a group discussed the issues raised by the cases to attain a common approach to data collection. Blinding either nurse or physician reviewers to the dates of the procedure was impossible, because they had to search the EHR for information needed.

Results of a test were called positive when an abnormality either seemed to explain the patient’s problem or required a medical decision. The effect on care was categorized as 1 of the following:

• Probable impact on patient health or function 

• Impact on the care process (usually additional consultation or testing)

The analysis was limited to descriptive statistics, although there were enough cases with adequate information in the record so that there was sufficient power to detect large differences, especially when we combined the cases for all 3 procedures. To evaluate the association between the pre- and postimplementation periods for the fit with appropriateness criteria, compliance rating, tests with positive findings, test results within the normal range, and test results that led to changes in care decisions, we performed 2-sided Fisher exact tests.

During the 2-month study period in 2007 compared with 2006, the volume of completed test orders for these 3 procedures dropped for head CT and spine MRI by 36.5% and 20%, respectively. However, completed test orders for head MRI increased by 3.3%. A total of 38 cases were excluded, leaving 151 cases before and 148 cases after implementation. Sixty-two percent of the cases involved women and the mean age was 57.8 years, with no pre/post differences. Only 6% of the procedures were ordered by nurse practitioners.

 shows that the great majority of orders could be definitely rated by the criteria in both the pre—decision support and post–decision support periods, but only MRI of the head and spine showed an increase in definitely meeting appropriateness criteria after decision support was present. Only MRI of the head showed an increase in the proportion of orders rated as A in the postperiod. More than 90% of the head CTs were ordered for headache, trauma, or a neurologic deficit, and nearly all the spine MRIs were ordered for back pain or radiculopathy. Head MRIs also were ordered for headache or neurologic deficits, but many were ordered for evaluation of vertigo or other conditions.

 shows that neither of the head procedures had many positive findings, although MRI of the head did have an increase in positive findings in the postperiod, as well as an increase in the proportion clearly related to the specific indication for doing the test. Implementation of decision support did not change the very low frequency with which either type of head HTDI test impacted patients (2.5% of the time), but the greater impact of spine MRI increased even more in the postperiod (from 14% to 30%, 

 = .18). Except for MRI of the spine in the postperiod, all audits revealed a high proportion of incidental findings. Spine MRI orders that did not meet appropriateness criteria greatly increased the proportion of spine MRI results that had an impact on patients (from 7% to 62%) in the postperiod. Head HTDI orders that did not meet appropriateness criteria had no impact on patients in either the pre- or postperiod (data not shown in the table).

When we combined all 3 procedures, 10% more cases had a definite rating of appropriateness after implementation and 12% more cases with a definite rating of appropriateness had an A rating. No other differences were significant between the 2 time periods, and there was no evidence of an effect on positive findings or patients.

This study showed that it is possible to use chart audits to assess the fit between recorded case information and appropriateness criteria for most cases involving these 3 procedures. It also suggests that there may have been a small increase in definite fit with the criteria and an increase in the proportion with an A rating, at least for some procedures. However, none of these increases were large or homogeneous across procedures, and for only 1 procedure (MRI of the head) was this better fit associated with an increase in the proportion of positive findings. Impact on patients was very limited for head studies, but larger for spine MRI, especially in the postperiod, suggesting there may be problems with the validity of the appropriateness criteria.

The number of HTDI studies dropped substantially for CT of the head and MRI of the spine in association with the introduction of this decision support system, whereas the volume of completed head MRIs rose slightly. This finding may reflect ordering redirection from CT to MRI. In comparison, the total number of HTDI of all types declined in Minnesota by 1.5% during this time period (based on claims data from all Minnesota payers) after increasing by 8.3% per year over the 3 years before that.

The American College of Radiology first published its criteria in 1995, and Martin et al have shown that they could be applied to 76% of the imaging procedure requests made from a general internal medicine clinic.

 The problem with these criteria is that there are a very large number of tests and possible indications and there is relatively little evidence, so the criteria are based on the opinions of expert panels.

 The variable results of this study for different procedures and the lack of increased positive findings or effects on patients suggest the need for more valid criteria based on evidence from more research.

 at Massachusetts General Hospital and Sanders and Aronsky

 at Vanderbilt have shown that a computerized order entry with decision support can be widely accepted by clinicians and can affect ordering behavior. On the other hand, Freeborn et al found that simply distributing guidelines for lumbar spine imaging or providing feedback about ordering patterns had no effect on use rates.

 Hadley et al reviewed charts of trauma patients with imaging studies and found that (1) it was possible to apply American College of Radiology criteria to the chart information, and (2) if the criteria had been applied to these patients, there would have been a 39% reduction in imaging costs and a 44% reduction in radiation dose.

 Bradley et al found that preevaluation MRI of chronic shoulder pain patients did not appear to have any effect on the treatment or outcome.

 Kaups et al studied repeat head CT studies on patients with head injury and found that in the absence of clinical deterioration, these studies did not alter management.

It remains unclear whether a new requirement for fit with criteria that results in fewer imaging studies differentially reduces unnecessary tests. Although we found that relatively few of these orders had positive findings or impact on patients, further studies on larger and more varied orders with a control or comparison group are needed to verify these results and to assess the overall impact on patient care and costs.

This quasi-experimental study clearly has limitations. These include relatively small sample sizes from a single medical group and audits based on relatively subjective judgments. We also have no information about possible changes in the volume of patients requiring testing or changes in indications. However, our study suggests that similar studies can be based on chart audits and that there may be considerable variability among procedures in the extent to which decision support affects either fit with appropriateness criteria or impact on ordering volume or patient outcomes. We also clearly need a better evidence base behind the development of appropriateness criteria, with more evidence for the specific value of various combinations of imaging tests, ordering criteria, and medical conditions.

From the HealthPartners Research Foundation (LIS, FW, JCB, KJP, CAV, MAM), HealthPartners Medical Group, Minneapolis, MN.

This study was supported by Partnership grant 07-070 from HealthPartners Research Foundation. It originated with the HTDI Steering Committee of the Institute for Clinical Systems Improvement and its constituent health plans and medical groups, which wanted to learn the effects of their new approach to improving the quality and cost of high-tech diagnostic imaging. The Steering Committee was responsible for coordinating the implementation

of electronic health record decision support among 5 large medical groups in the state, and it provided helpful input during our frequent updates on this study and its findings.

Ms Vinz reports receiving honoraria to speak and funds to attend conferences/meetings related to decision support initiatives. The other authors (LIS, FW, JCB, KJP, MAM) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Concept and design (LIS, FW, JCB, KJP, CAV, MAM); acquisition of data (LIS, JCB, KJP); analysis and interpretation of data (LIS, FW, JCB, KJP, CAV); drafting of the manuscript (LIS, FW, CAV); critical revision of the manuscript for important intellectual content (LIS, FW, CAV, MAM); statistical analysis (FW); obtaining funding (LIS); and administrative, technical, or logistic support (KJP, MAM).

Leif I. Solberg, MD, HealthPartners Research Foundation, HealthPartners Medical Group, PO Box 1524, MS #21111R, Minneapolis, MN 55440-1524. E-mail: leif.i.solberg@healthpartners.org.

1. Corrigan JM, Eden J, Smith BM, eds, for the Institute of Medicine. 

Leadership by Example: Coordinating Government Roles in Improving Health Care Quality. 

Washington, DC: National Academies Press; 2003.

2. Hillestad R, Bigelow J, Bower A, et al.

 Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. 

3. Miller RH, West C, Brown TM, Sim I, Ganchoff C.

 The value of electronic health records in solo or small group practices. 

Articles

Electronic decision support for high-tech diagnostic imaging was associated with reduced volume and increased appropriateness, but had little impact on findings or patients.

Effects of Electronic Decision Support on High-Tech Diagnostic Imaging Orders and Patients

To identify factors associated with delayed radiotherapy (RT) in older women with early-stage breast cancer.

We studied 541 women age ≥65 years diagnosed with early-stage breast cancer in 1990-1994 at 5 integrated healthcare delivery systems and treated with breast-conserving surgery and RT, but not chemotherapy. We examined whether demographic, tumor, or treatment characteristics were associated with RT delays of >8 weeks postsurgery using χ2 tests and multivariable logistic regression.

Seventy-six women (14%) had delayed RT, with a median delay of 14 weeks. Even though they had insurance and access to care, nonwhite and Hispanic women were much more likely than white women to have delayed RT (odds ratio = 3.3; 95% confidence interval = 1.7, 10) in multivariable analyses that controlled for demographic and clinical variables.

Timely RT should be facilitated through physician and patient education, navigation, and notification programs to improve quality of care. Queues for RT appointments should be evaluated on an ongoing basis to ensure adequate access. Future research should examine modifiable barriers to RT timeliness and whether delays impact long-term outcomes.

This study identified factors associated with delayed radiotherapy (RT) for women age ≥65 years with breast cancer in 5 integrated healthcare delivery systems.

Delayed RT was experienced by 14% of the study subjects, even though they had insurance and access to care.

Women receiving delayed care were more likely to be nonwhite or Hispanic.

Queues for RT appointments should be evaluated on an ongoing basis to ensure adequate access.

Breast cancer, diagnosed in 180,000 women in the United States in 2008,

 usually is treated with mastectomy or breast-conserving surgery (BCS) with or without radiotherapy (RT).

 Treatment characteristics vary by race/ethnicity, socioeconomic status, geographic region, healthcare access, and insurance status.

Quality of care, including timing and completeness of RT and chemotherapy, can affect recurrence and survival rates.

 The start of RT is time sensitive, yet many factors can affect whether a patient experiences a delayed start. If she has positive nodes, she may be more likely to have longer discussions about treatment options because more options are available.

 As breast cancer treatment requires extensive coordination of care across multiple providers and specialties, patients may experience difficulties navigating the healthcare system.

 Typical treatment starts with surgery and lymph node assessment, followed by recovery time after surgery, subsequent RT to the whole breast for 5 to 6 weeks, and a 1-week RT boost to the tumor bed.

 Positive lymph nodes are an indication for chemotherapy to be part of the treatment.

There is conflicting evidence regarding the optimal timing of RT following BCS.

 One study suggests that RT delay does not negatively affect breast cancer—related health outcomes,

 A systematic review concluded that RT delay was associated with higher rates of local recurrence.

 Delay also may be associated with psychological distress and anxiety, which can negatively impact the care process and outcomes.

This study assesses patient-level predictors of delayed RT in an insured population of older women with early-stage breast cancer treated in integrated healthcare delivery systems, a group that has not been studied for timeliness in receiving RT.

This study is nested in the Breast Cancer Treatment Effectiveness in Older Women Study cohort.

 We included 541 women age ≥65 years diagnosed with unilateral, early-stage breast cancer (American Joint Commission on Cancer stage I or II22) enrolled in 5 integrated delivery systems in the Cancer Research Network, a  collaborative group of research programs, enrolled populations, and data systems of 14 health systems nationwide.

 Women diagnosed from 1990 through 1994 were followed for 10 years. All subjects in this nested cohort received BCS with a full course of whole breast RT, but no chemotherapy. Subjects from the original cohort receiving BCS without RT (23%) or mastectomy (53%) were excluded.

 We restricted the sample to women not receiving chemotherapy to eliminate variations in RT timing related to chemotherapy sequencing. We used automated administrative databases, medical record review, and tumor registries to ascertain age, race/ethnicity, Charlson Comorbidity Index score,

 lymph node status, tumor size, histology, estrogen receptor (ER) or progesterone receptor (PR) status, and any use of tamoxifen. To calculate the Charlson Comorbidity Index score, information on comorbid conditions was collected from medical records for all healthcare services provided in the year before diagnosis. This index includes 18 conditions weighted to predict mortality. We excluded cancer diagnoses from the index and categorized the remaining score as 0, 1, or 2+. Because others have shown that an 8-week delay can impact health outcomes, we defined RT delay as starting RT more than 8 weeks following BCS, based on medical record data.

All research was conducted following approval from local institutional review boards and in accordance with the ethical standards of those boards.

We calculated the percentage of women with delayed RT and the percentage with timely RT by demographic and clinical characteristics. We used 

 tests and fit multivariable logistic regression models to identify factors associated with delayed RT, controlling for integrated delivery system site.

The sample included 541 women, of whom 76 (14%) had a delay of more than 8 weeks (

). Among women with RT delayed more than 8 weeks, the median delay was 14 weeks, and 21 subjects (4%) had delays of more than 12 weeks. More than two-thirds of the sample was younger than age 75 years, and few (3.3%) scored more than 1 on the Charlson Comorbidity Index. Almost 7% were ER−/PR−, and 13% of subjects had positive nodes (

), women of nonwhite race or Hispanic ethnicity had significantly higher odds of delayed RT (odds ratio [OR] = 3.3, 95% confidence interval [CI] = 1.7, 10). We had limited precision in general, as indicated by some wide CIs (eg, tumor size >2 cm [OR = 1.9, 95% CI = 0.9, 4.1] and ER−/PR− status [OR = 1.8, 95% CI = 0.7, 4.9]).

In this fully insured population of older women with breast cancer who had access to integrated delivery systems, about 1 in 7 women experienced delayed RT following BCS, a rate similar to that reported in other studies.

 Surprisingly, in this population, the proportion with RT delays of more than 8 weeks was about the same as that found in a fee-for-service Medicare population (14% vs 16%, respectively).

 One might expect better coordinated, and therefore less delayed, care in integrated healthcare delivery systems, yet delays of more than 8 weeks may be due to healthcare access issues such as transportation problems, language concordance or communication issues, or limited patient knowledge about the importance of timely RT—all barriers that are not mediated by insurance status. Alternatively, delays may be caused by poor care coordination by clinicians and the healthcare system or patient-related barriers, concerns, or anxiety regarding decision making and treatment

The only predictor of delayed RT was nonwhite or Hispanic race/ethnicity, consistent with previous research.11,18 Indeed, delayed RT, which may represent uncoordinated care (among other possible factors), was not affected significantly by other included clinical or demographic factors. Whether these delays in turn affect mortality for patients in integrated delivery systems could not be shown because of limited statistical power.

All eligible women were included in our study population, and we had no loss to follow-up in the treatment period. This design advantage substantially limited the potential for selection bias. Another strength of this study is that all subjects in the cohort had equal access to complete treatment by having access to care in an integrated healthcare delivery system. In addition to sociodemographic information (beyond age), we had important information about the tumors themselves that helped explain treatment receipt and reduced confounding in our analyses. Nonetheless, we did not have information on characteristics such as patient educational attainment, income, transportation availability, radiation oncologist availability, language barriers, or prefer ences for care; doctor-patient communication; or healthcare system programs such as care coordination—all of which may be greater barriers to or facilitators of care for older women diagnosed with breast cancer.

 These characteristics may directly or indirectly influence care-seeking behavior and care receipt among nonwhite and Hispanic women, who are more likely to be of lower socioeconomic status.

 All subjects had Medicare coverage in these healthcare systems with the same benefits for RT and Medicare paying for RT in inpatient, outpatient, and freestanding clinics. Outpatient RT requires a set copayment, which was usually lower than fee-for-service copayments, thereby reducing barriers to care even more. It is unlikely that coverage levels affected timeliness of RT.

We have shown that access to healthcare, as reflected by a patient’s membership in an integrated healthcare delivery system, does not in itself ensure timely therapy. Rates of RT delay were similar between integrated delivery systems and the fee-for-service sector11 and appear to be affected by race/ethnicity. Healthcare delivery systems and providers should consider targeting care coordination efforts to traditionally underserved patients to improve timeliness of RT.

: From the Department of Public Health (HTG), Weill Cornell Medical College, New York, NY; the Department of Geriatrics (SST, RAS) and the Department of Epidemiology (TLL), Boston University Medical Center, Boston, MA; Group Health Research Institute (DSMB), Group Health Cooperative, Seattle, WA; Meyers Primary Care Institute (TSF), Worcester, MA; HealthPartners Research Foundation (FW), Bloomington, MN; Department of Epidemiology & Public Health (MUY), Yale University, New Haven, CT; Henry Ford Health System (MUY), Detroit, MI; Research & Evaluation (VPQ), Kaiser Permanente Southern California, Pasadena, CA; and the Department of Social Services and Health Policy (AMG), Wake Forest University, Winston-Salem, NC.

: Funded by the National Cancer Institute (R01 CA093772; Dr Silliman, principal investigator) and in part by the American Cancer Society (MRSGT-04-002-01-CPHPS; Dr Gold, principal investigator).

: The authors (HTG, SST, DSMB, TSF, FW, MUY, TLL, VPQ, AMG, RAS) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

: Concept and design (HTG, SST, DSMB, TSF, FW, MUY, TLL, RAS); acquisition of data (SST, DSMB, TSF, FW, MUY, VPQ, AMG, RAS); analysis and interpretation of data (HTG, SST, DSMB, TLL, VPQ, AMG, RAS); drafting of the manuscript (HTG, DSMB, VPQ); critical revision of the manuscript for important intellectual content (HTG, SST, DSMB, TSF, FW, MUY, TLL, AMG, RAS); statistical analysis (HTG, SST); provision of study materials or patients (DSMB, MUY, VPQ, RAS); obtaining funding (HTG, MUY, VPQ, AMG, RAS); administrative, technical, or logistic support (DSMB, RAS); and supervision (DSMB, FW).

: Heather Taffet Gold, PhD, Department of Public Health, Weill Cornell Medical College, 402 E 67th St, New York, NY 10065-6304. E-mail: heg2001@med.cornell.edu.

Cancer Facts and Figures 2008. http://www.cancer.org/docroot/STT/content/STT_1x_Cancer_Facts_and_Figures_2008.asp. Accessed September 22, 2009.

. NCCN Clinical Practice Guidelines in Oncology: Breast Cancer. http://www.nccn.org/professionals/physician_gls/PDF/breast.pdf. December 2, 2008. Vol 1. 2009. Accessed May 13, 2009.

3. Nattinger AB, Kneusel RT, Hoffmann RG, Gilligan MA

. Relationship of distance from a radiotherapy facility and initial breast cancer treatment. 

4. Nattinger AB, Gottlieb MS, Veum J, Yahnke D, Goodwin JS

. Geographic variation in the use of breast-conserving treatment for breast cancer. 

. Variations in treatment for ductal carcinoma in situ in elderly women. 

6. Ballard-Barbash R, Potosky AL, Harlan LC, Nayfield SG, Kessler LG

. Factors associated with surgical and radiation therapy for early stage breast cancer in older women. 

. Race, ethnicity, and disparities in breast cancer: victories and challenges. 

8. Riley GF, Potosky AL, Klabunde CN, Warren JL, Ballard-Barbash R

. Stage at diagnosis and treatment patterns among older women with breast cancer: an HMO and fee-for-service comparison. 

Impact of treatment and socioeconomic status on racial disparities in survival among older women with breast cancer. 

. Race, socioeconomic status, and breast cancer treatment and survival. 

 Correlates and effect of suboptimal radiotherapy in women with ductal carcinoma in situ or early invasive breast cancer. 

12. Hershman D, McBride R, Jacobson JS, et al.

 Racial disparities in treatment and survival among women with early-stage breast cancer. 

13. Lash TL, Silliman RA, Guadagnoli E, Mor V

. The effect of less than definitive care on breast carcinoma recurrence and mortality. 

. Coordination of care for early-stage breast cancer patients. 

15. Buchholz TA, Austin-Seymour MM, Moe RE, et al.

 Effect of delay in radiation in the combined modality treatment of breast cancer. 

16. Hébert-Croteau N, Freeman CR, Latreille J, Rivard M, Brisson J

. A population-based study of the impact of delaying radiotherapy after conservative surgery for breast cancer. 

17. Huang J, Barbera L, Brouwers M, Browman G, Mackillop WJ

. Does delay in starting treatment affect the outcomes of radiotherapy? A systematic review [published correction appears in 

18. Hershman DL, Wang X, McBride R, Jacobson JS, Grann VR, Neugut AI.

 Delay in initiating adjuvant radiotherapy following breast conservation surgery and its impact on survival. 

19. Benchalal M, de Lafontan B, Berton-Rigaud D, et al

. Influence of the time between surgery and radiotherapy on local recurrence in patients with lymph node-positive, early-stage, invasive breast carcinoma undergoing breast-conserving surgery: results of the French Adjuvant Study Group. 

20. Brintzenhofe-Szoc KM, Levin TT, Li Y, Kissane DW, Zabora JR.

 Mixed anxiety/depression symptoms in a large cancer cohort: prevalence by cancer type. 

21. Enger SM, Thwin SS, Buist DSM, et al. 

Breast cancer treatment of older women in integrated health care settings. 

5th ed. Philadelphia, PA: Lippincott Williams & Wilkins; 1997.

23. Wagner EH, Greene SM, Hart G, et al. 

Building a research consortium of large health systems: the Cancer Research Network. 

24. Charlson ME, Pompei P, Ales KL, MacKenzie CR

. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. 

 A tracking and feedback registry to reduce racial disparities in breast cancer care. 

Understanding reasons for underuse: an approach to improve quality and reduce disparities in breast cancer treatment. 

Income, Earnings, and Poverty Data From the 2006 American Community Survey.

 American Community Survey Reports. August 2007:1-40. US Census Bureau. http://www.census.gov/prod/2007pubs/acs-08.pdf. Accessed August 21, 2009.

Among older women in 5 integrated delivery systems, 14% experienced delayed radiotherapy for breast cancer, even though they had insurance and access to care.


Feifei Wei, PhD

Articles

Effects of Electronic Decision Support on High-Tech Diagnostic Imaging Orders and Patients

Delayed Radiotherapy for Breast Cancer Patients in Integrated Delivery Systems

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