Articles

LIST OF PEER REVIEWERS

List of Peer Reviewers (2007)

It&#8217;s axiomatic. If you ask the wrong question, you&#8217;ll probably get the wrong answer. That assumes, of course, that you&#8217;re not asking the wrong question because you want the wrong answer. And therein lies the essence of the entire debate, both inside our industry and out, about whether disease management works.

The question is usually put like this: &#8220;Does disease management work?&#8221; To answer that question requires that there be some common, agreed upon understanding of both &#8220;disease management&#8221; and &#8220;work.&#8221; Of course, as the readers of this journal know full well, there is no common, agreed upon understanding of either. And so, the answer to the question, &#8220;Does disease management work?&#8221; is most often a reflection of the prior bias of the person asking the question, or of those who are paying for that person to answer the question.

So, before we can answer the question of whether it works, we must first agree on what disease management is. The DMAA: The Care Continuum Alliance defines disease management as follows:

Disease management is a system of coordinated health care interventions and communications for populations with conditions in which patient self-care efforts are significant.

&#8226; Supports the physician or practitioner/patient relationship and plan of care;

&#8226; Evaluates clinical, humanistic, and economic outcomes on an on-going basis with the goal of improving overall health.

&#8226; Evidence-based practice guidelines;

&#8226; Patient self-management education (may include primary prevention, behavior modification programs, and compliance / surveillance);

&#8226; Routine reporting/feedback loop (may include communication with patient, physician, health plan and ancillary providers, and practice profiling).

It is interesting to note that, in most public presentations of the definition, the section relating to &#8220;disease management components&#8221; and the categorization of what constitutes comprehensive disease management programs is usually left out. And the first part of the definition is so general that it enables just about any entity that interacts with a patient to claim they are providing disease management services. Come to think of it, maybe that&#8217;s why the second part of the definition is so often omitted.

In any event, absent the application of a rigorous definition, disease management has become a meaningless, homogeneous phrase that means whatever the person&#8212;or entity - asking the question wants it to mean. But, even if there were a generally accepted definition of disease management, the entire process falls off the cliff when we get to the second key word in our original question&#8212;&#8220;work.&#8221; As we all know, even though there is general agreement about the elements of disease management&#8217;s value proposition&#8212; improved health, improved satisfaction with the care experience, and reduced cost of care&#8212;there is no agreement on how to measure the performance of a disease management program across those elements.

 And so, those asking and answering the question are free to apply whatever methodology they want to reach whatever conclusion advances their cause, or that of their clients.

As a result, the answer to the question, &#8220;Does disease management work?&#8221; is at best neutral

And that doesn&#8217;t even count the &#8220;studies&#8221; that selectively review published outcomes papers to draw whatever conclusion their authors wanted to reach in the first place.

This whole situation is further complicated by two unpleasant realities. First, not all disease management programs&#8212;even those that would qualify as comprehensive under the DMAA definition&#8212;are created equal. Second, execution matters.

It might be argued that there is a certain common recipe for disease management programs which would include such ingredients as identification and stratification algorithms, assessment logic, intervention processes, data management systems and the like. But even if all of these ingredients were alike&#8212;which they&#8217;re not&#8212;how they&#8217;re applied will inevitably lead to differences in results. And, since financial performance is one of the dimensions against which the success of programs is evaluated, the cost related to how all of these are configured for delivery for large populations, makes a difference as well.

In this regard, disease management is a lot like cooking; millions of cookbooks are sold every year, but very few of the purchasers become fine chefs. At the risk of torturing this analogy even further, we also can&#8217;t forget those who want to buy the meal but insist that the chef change the ingredients or proportions and then are unhappy with its final taste.

We know that disease management works because there are countless studies validating the underlying standards of care on which the disease management translation effort is based. Accordingly, we are seeking the disease management solution that offers the most effective translation.

Given these realities, the only reasonable question for a purchaser, policy maker, or research institution to ask is, &#8220;Whose disease management program works?&#8221; While the simple truth is not all of them do, the literature is replete with third-party peer-review studies and meta-analyses that clearly prove that well-designed, well-implemented, and well-executed programs do, in fact, work. The fact that these studies come from only a very small number of organizations who purport to offer disease management services is unfortunate; it is, however, the case.

Of course, if that&#8217;s not the answer for which you&#8217;re looking, selective research can provide the answer you want.

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components are disease management support services.

 Evidence for the effect of disease management: is $1 billion a year a good investment? 

LETTERS TO THE EDITORS

Is Disease Management Effective? Well...Some is.

: To assess the use of complementary and alternative medicine (CAM) providers and the associated expenditures by specific treatment phases among patients with cancer.

: Cross-sectional analysis of medical services utilization and expenditures during the 3 therapeutic phases of initial, continuing, and end-of-life life treatment.

: Analysis of an insurance claims database that had been matched to the Washington State Surveillance, Epidemiology, and End Results cancer registry.

: Of 2900 registry-matched patients, 63.2% were female, the median age was 54 years, and 92.7% were of white race/ethnicity. Breast cancer was the most frequent diagnosis (52.7%), followed by prostate cancer (24.7%), lung cancer (10.1%), colon cancer (7.0%), and hematologic malignancies (5.6%). Patients using CAM providers represented 26.5%. The proportion of patients using CAM was similar during each treatment phase. All patients used some conventional care. Age, female sex, breast cancer diagnosis, and white race/ethnicity were significant predictors of CAM use. Diagnosis of a musculoskeletal problem occurred at some time during the study for 72.1% of patients. CAM provider visits represented 7.2% of total outpatient medical visits, and 85.1% of CAM visits resulted in a musculoskeletal diagnosis. Expenditures for CAM providers were 0.3%, 1.0%, and 0.1% of all expenditures during the initial, continuing, and end-of-life phases, respectively.

: For patients with cancer, musculoskeletal issues were the most commonly listed diagnosis made by a CAM provider. Although expenditures associated with CAM are a small proportion of the total, additional studies are necessary to determine the importance that patients place on access to these services.

Under a system in which complementary and alternative medicine (CAM) providers are covered by insurance, 26.5% of patients with cancer will see a licensed alternative provider during the course of their therapy. 

The proportion of patients seeing CAM providers does not significantly vary among the 3 therapeutic phases of initial, continuing, and end-of-life treatment. 

Expenditures for CAM services are a small proportion of overall expenditures.

Cancer is the second leading cause of death in the United States.

 estimated that in 2007 more than 1.4 million new cancer cases would be diagnosed and that more than 500,000 persons would die of this disease. Cancer treatment costs are substantial because of the direct cost of new increasingly expensive treatments

 and the enormous indirect costs associated with time spent during treatment for patients and their family members.

 Patient surveys have shown that some of these expenditures are for complementary and alternative medicine (CAM) providers, as more than 70% of patients with cancer will use some form of CAM after the diagnosis of cancer and at least 16% of patients with cancer visit a CAM provider.

 The use of CAM by patients with cancer is associated with treatment of specific symptoms,

female sex,  diagnosis of breast cancer, and white race/ethnicity.

Previous health services research studies have grouped conventional provider utilization and expenditures for cancer care based on intervals after diagnosis. To accomplish this, Medicare datasets have been matched to cancer registry data.

This adds diagnostic detail and mortality end points that are present in registry data to claim-based administrative data.  These registry-matched cancer studies have documented healthcare utilization by treatment phase (during the initial 12 months following diagnosis, during the last year of life, and during the continuing period between these 2 phases).

 Utilization analysis during each phase captures a different picture of cancer care than a single cross-sectional evaluation. To our knowledge, this type of evaluation has not been performed to assess the use of CAM providers for cancer treatment.

Historically, third-party payment records have been a poor choice for studying CAM provider services because CAM care has traditionally been paid for out-of-pocket

 and because large public payers (such as Medicare) do not cover most CAM provider services. Some states have taken legislative steps to change this and to integrate CAM providers into mainstream healthcare finance.

 In 1996, Washington State passed legislation requiring every category of licensed healthcare provider to be covered by private insurance. This mandated the inclusion of acupuncturists, naturopathic physicians, and massage therapists into commercial insurance products; legislative mandates in 1983 had already covered chiropractors.

 Therefore, Washington State&#8217;s mandate for CAM coverage created a large bank of insurance claims data in which CAM providers have been consistently covered since 2000. Matching enrollment and claims data from a subset of individuals who were privately insured from 2000 to 2004 to the Fred Hutchinson Cancer Research Center&#8217;s western Washington State Surveillance, Epidemiology, and End Results (SEER) cancer registry identified a cohort of patients with insurance claims and registry information. These data were used to assess the factors that predict CAM use by distinct therapeutic phases, the medical reasons for CAM use during these phases, and the proportion of expenditures resulting from CAM provider care.

This study was approved by the institutional review boards of the University of Washington and the Fred Hutchinson Cancer Research Center. Eligible participants consisted of registry-matched patients with cancer aged 18 to 64 years who were diagnosed as having breast, colorectal, hematologic, lung, or prostate cancer from January 2000 through December 2003 and had no prior cancer diagnosis. In addition, eligibility criteria included having insurance coverage at the time of diagnosis and maintaining coverage at least 12 months after diagnosis or (in the situation of survival of <1 year) maintaining contiguous coverage from diagnosis to death. A large private provider of multiple insurance product types supplied the insurance data.

Logistic regression analysis assessed the potential predictors of CAM use during each study phase. Separate regression analyses were performed for each predictor of interest because no adjustments were made for other covariates. Odds ratios were considered statistically significant at P <.05, corresponding to a 95% confidence interval (CI) not containing 1.0. No  adjustments were made for multiple comparisons. All analyses were performed using commercially available statistical software (STATA version 9.2; StataCorp LP, College Station, Texas).

Of 900,000 persons with private insurance coverage from 2000 to 2004, 2900 were registry-matched patients with cancer who met the study criteria. The study population is summarized in 

. In the cohort, 93.0%, 84.8%, and 10.7% of participants had data for the initial, continuing, and end-of-life phases, respectively. Most participants were urban, female, and of white race/ethnicity. Breast cancer accounted for more than 52.7% of all cancer diagnoses, followed by prostate, lung, colorectal, and hematologic malignancies. Patients with lung cancer accounted for 58.7% of patients in the end-of-life treatment phase. The median age of all patients at diagnosis was 54 years, and almost two-thirds of participants were diagnosed as having localized cancer. Point-of-service plans, preferred provider organizations, and group policies were the predominant forms of coverage. Patients with at least 1 claim from a CAM provider accounted for 26.5% of the cumulative cohort of patients with cancer. The use of different CAM provider types was not mutually exclusive. Chiropractors were the most commonly used CAM provider. The prevalence of CAM claims was higher, although not statistically significantly so, in the continuing phase (21.8%) and in the initial phase (19.5%) compared with the end-of-life phase (17.7%). All patients used some form of conventional care.

 gives potential predictors of CAM use during each of the 3 treatment phases. The estimated odds ratio (OR) of CAM use for patients diagnosed as having breast cancer was significantly higher than that for patients diagnosed as having colorectal cancer during the initial phase (OR, 1.85; 95% CI, 1.19-2.87) and during the continuing phase (OR, 2.04; 95% CI, 1.29-3.22). The estimated OR of CAM use was not significantly different for patients with hematologic malignancies, prostate cancer, lung cancer, and colorectal cancer. No significant differences were found between the estimated OR of CAM use for patients presenting with local cancer compared with that of patients presenting with nonlocal cancer during the initial phase (OR, 1.17; 95% CI, 0.96-1.43). The estimated OR for CAM utilization was marginally lower for older patients during the initial and continuing phases. Patients having group policies had higher odds of CAM use compared with patients having individual policies during the continuing phase only (OR, 1.58; 95% CI, 1.14-2.18). Patients with an insurance product other than a preferred provider organization or a point-of-service plan had slightly higher CAM use in the continuing phase and for the aggregated phases than patients with a point-of-service plan. CAM use did not vary significantly between individuals residing in urban vs nonurban settings.

Among 895 patients receiving chemotherapy, 455 (50.8%) had a diagnosis of nausea or vomiting; 26 of these (5.7%) visited an acupuncturist (data not shown). For patients receiving chemotherapy, the estimated OR of receiving acupuncture was not significantly different for individuals with nausea or vomiting vs individuals without nausea or vomiting (OR, 1.51; 95% CI, 0.81-2.82). Approximately 3.9% of all study participants&#8217; visits to an acupuncturist were for nausea or vomiting.

Diagnostic codes for lymphedema were recorded for 240 women (15.7%) with breast cancer. Of these, 29 (12.1%) were treated by a massage therapist and 155 (64.6%) by a physical or occupational therapist. Comparing women with and without lymphedema, the estimated OR of massage therapy was 58% higher for women with lymphedema (OR, 1.58; 95% CI, 1.02-2.45). Among women with breast cancer, the OR of visiting a massage therapist was estimated to be 3.71 (95% CI, 2.78-4.94) times higher for women with lymphedema compared with women without lymphedema. 

Expenditures and Use of CAM and Conventional Services 

The mean per capita expenditures and the relative contribution of selected services for each treatment phase are shown in the 

. The least expensive phase for cancer treatment was the continuing phase, which we estimated to result in $12,429 of annual expenditures. Expenditures for the initial phase were $38,587, and the most costly interval (the end-of-life phase) resulted in $115,994 per capita expenditures. Inpatient hospital and outpatient provider expenditures for conventional treatments accounted for more than 50% of all expenditures during each treatment phase. Expenditures for inpatient and hospice care were proportionately higher during the end-of-life phase than they were in the other treatment phases. Although 179 patients (57.7%) who died used hospice at the end of life, only 1.6% of end-of-life expenditures were for this service.  Expenditures for CAM providers were a small portion of overall expenditures, as shown in the Figure. CAM providers accounted for 1% or less of total healthcare expenditures during all 3 treatment phases. As a proportion of outpatient provider charges only, CAM services accounted for 1.2%, 3.1%, and 0.4% of the initial, continuing, and end-of-life phases, respectively (data not shown). CAM outpatient provider expenditures were 1.5% of the total outpatient provider expenditures during the entire study period.

. Cancer Facts & Figures: 2007. Atlanta, GA: American Cancer Society; 2007.

 Patient time costs associated with cancer care. 

6. Ezzo JM, Richardson MA,Vickers A, et al.

 Acupuncture-point stimulation for chemotherapy-induced nausea or vomiting. 

8. Berman BM, Lao L, Langenberg P, Lee WL, Gilpin AM, Hochberg MC.

 Effectiveness of acupuncture as adjunctive therapy in osteoarthritis of the knee: a randomized, controlled trial. 

10. Cherkin DC, Sherman KJ, Deyo RA, Shekelle PG

. A review of the evidence for the effectiveness, safety, and cost of acupuncture, massage therapy, and spinal manipulation for back pain. 

12. Lafferty WE, Bellas A, Corage Baden A,Tyree PT, Standish LJ, Patterson R. 

The use of complementary and alternative medical providers by insured cancer patients in Washington State. 

14. Keating NL, Landrum MB, Meara E, Ganz PA, Guadagnoli E.

 Managed care market share and primary treatment for cancer. 

. Health care workforce for the twenty-first century: the impact of nonphysician clinicians.

. The effect of mandating complementary and alternative medicine services on insurance benefits in Washington State. 

 Challenges of using medical insurance claims data for utilization analysis. 

 The Johns Hopkins Adjusted Clinical Groupings Case-Mix System. 8th ed. Baltimore, MD: Johns Hopkins University; 2006.

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28. Gray CM,Tan AW, Pronk NP, Oâ€™Connor PJ.

 Complementary and alternative medicine use among health plan members: a cross-sectional survey. 

30. Gourmelen J, Chastang JF, Ozguler A, LanoÃ« JL, Ravaud JF, Leclerc A.

 Frequency of low back pain among men and women aged 30 to 64 years in France: results of two national surveys. 

32. Haake M, MÃ¼ller HH, Schade-Brittinger C, et al.

 German Acupuncture Trials (GERAC) for chronic low back pain: randomized, multicenter, blinded, parallel-group trial with 3 groups [published correction appears in 

 Off-label or out of bounds? Prescriber and marketer liability for unapproved uses of FDA-approved drugs. 

CLINICAL

More than 26% of cancer patients see CAM providers, primarily for musculoskeletal problems; use does not vary by treatment phase, and associated expenditures are low.

Complementary and Alternative Medicine Provider Use and Expenditures by Cancer Treatment Phase

: To examine the economic burden of skeletal-related events (SREs) and to assess the frequency of different types of SREs in this population.

: Data were obtained from i3â€™s Lab Rx Database from May 1, 2000, through March 31, 2005. Patients included had at least 2 claims with a diagnosis of prostate cancer, at least 2 subsequent claims with a diagnosis of bone metastasis, and at least 1 SRE on or after the date of the initial diagnosis of bone metastasis. Descriptive statistics for 342 patients who fit all inclusion and exclusion criteria are provided, along with Kaplan-Meier curves, which were used to estimate annual costs, adjusting for the censoring of the data.

: Patients most frequently had radiation therapy (89%), followed by pathologic fracture (23%) and bone surgery (12%). Among patients diagnosed as having at least 1 SRE, 78% experienced 1 type of SRE, 17% had 2 types of SREs, and 5% had 3 or more distinct types of SREs. The mean costs associated with SREs in the year after the initial diagnosis of an SRE, adjusted for the censoring of the data, was $12,469, with the highest costs associated with radiation therapy ($5930), followed by pathologic fracture ($3179) and bone surgery ($2218). 

: This study of patients with prostate cancer and bone metastases revealed that the annual economic effect of medically treating SREs for these patients was $12,469.

Prostate cancer has the highest incidence of any nonskin cancer in the United States, and bone metastasis is a common form of metastatic disease among patients with prostate cancer. Bone metastasis is complicated by the occurrence of skeletal-related events (SREs).

This research quantifies the costs of SREs among patients with prostate cancer and bone metastases. Results reveal that the mean annualized costs of SREs for this population was $12,469.

The research illustrates the relative frequency at which different types of SREs occur, with patients most likely to undergo therapeutic radiology or to have a pathologic fracture.

Prostate cancer has the highest incidence of any nonskin cancer in the United States and is the second leading cause of cancer-related deaths among US men.

 It was projected that 218,890 new cases of prostate cancer would be identified in the United States in 2007 and that an estimated 27,050 men would die of the disease.

Bone metastasis is a common form of metastatic disease among patients with prostate cancer. Yuen et al

 report that 80% of men with metastatic disease have bone metastasis, and others report percentages between 65% and 70%.6,7 In addition, as many as 20% of men who are newly diagnosed as having prostate cancer already have bone metastases.

 Bone metastases are complicated by significant morbidity, including skeletal-related events (SREs), which are local irreversible changes and include pathologic fracture, bone surgery, radiation therapy to the bone, and spinal cord compression.

These events negatively affect quality of life

and present a challenge for the goals of palliative therapy, which include managing these patients&#8217; pain, preventing further deterioration, and preserving quality of life.

Although the humanistic burden of SREs is well documented, little research has focused on the economic effects of medically treating SREs. To our knowledge, only one study

 has looked specifically at the costs of SREs in patients with prostate cancer and bone metastases; the sample of patients in the analysis was small (n = 28), and the costs were based on care in The Netherlands. However, studies of SREs among patients with lung cancer

have consistently shown SREs to be common and costly.

In the present study, the objectives were to elucidate further the US costs associated with SREs among patients with prostate cancer and bone metastases and to determine the relative frequency at which the different types of SREs occur. To this end, we conducted a retrospective analysis using a large US health insurance claims database to examine the incidence and costs associated with SREs among patients with bone metastases secondary to prostate cancer.

Half of all patients with at least 2 claims for prostate cancer (n = 342) and at least 2 claims for bone metastasis (n = 878) experienced 1 or more SREs (n = 435). For men who had at least 1 SRE, 87% were identified as having had an SRE within 1 year of their initial diagnosis of bone metastasis. Their mean age was 68.1 years, with most men residing in the South (49%) or in the Midwest (32%) (

), which is consistent with the population in the database. Most patients were commercially insured (66%), and the most common type of insurance was a health maintenance organization (52%). An examination of prior resource utilization, the severity score by Charlson et al,

 and comorbidities revealed that, as expected, patients in this study were severely ill. For example, the mean (SD) Charlson severity score was 1.43 (1.55), and the patients had been hospitalized in the 6 months before the index date for a mean of 6.6 days and had a mean number of office visits of 14.2. In addition, 37% of patients included in the study had also been diagnosed as having cardiovascular disease, and 16.1% had been diagnosed as having comorbid diabetes mellitus.

Most patients received radiation therapy (89%), as shown in 

. Approximately 23% experienced a pathologic fracture, and 12% had bone surgery. Some SREs occurred in clusters, meaning that it was possible that someone had a fracture that required bone surgery, and in this case both events are captured as separate types of SREs.

 shows the Kaplan-Meier estimated probability of continuation in the database (survival proxy) for the population from the time of initial diagnosis of an SRE. After 2 years, the proxy survival rate was 16%, and the median proxy survival was approximately 8.5 months. 

As a test of the robustness of the results, we reexamined the analysis, omitting service from a radiation oncologist as a criteria in defining therapeutic radiology. Although the percentage of individuals with an SRE identified as having therapeutic radiology declined from 89% to 78%, the general results were consistent with those reported herein. For example, this analysis found 1-year costs associated with an SRE to be $12,649, while omitting service from a radiation oncologist reduces this amount to $12,007. In addition, this analysis estimated the 2-year survival rate to be 15%, while the reexamined subset of data resulted in an estimated 2-year survival rate of 16%.

Many patients with prostate cancer metastasized to the bone experience SREs. In addition to physical adversity associated with these events, these patients will also incur high costs for medical treatment of the SREs. The findings of this analysis suggest that safe measures to mitigate or prevent SREs have the potential to yield cost offsets. Although earlier studies looking at costs of SREs in cancer paients

 have come to similar conclusions, this analysis uses the largest sample of patients with prostate cancer, bone metastases, and SREs examined to date, to our knowledge.

We thank Patricia Platt for her help with the manuscript.

: HealthMetrics Outcomes Research, LLC (MJL), Groton CT; Department of Global Health Economics (BLB, DJH) and Department of Clinical Development (SJ), Amgen Inc, Thousand Oaks, CA.

: Drs Lage and Jun consult for Amgen and received payment for their involvement in the preparation of this manuscript. Drs Barber and Harrison are employees of Amgen and report owning stock in that company.

: Concept and design (BLB, DJH); acquisition of data (BLB); analysis and interpretation of data (MJL,BLB, DJH, SJ); drafting of the manuscript (MJL, BLB, SJ); critical revision of the manuscript for important intellectual content (MJL, BLB, DJH, SJ); statistical analysis (MJL); obtaining funding (BLB); administrative, technical, or logistic support (BLB); and supervision (BLB).

: Maureen J. Lage, PhD, HealthMetrics Outcomes Research, LLC, 120 Anchorage Cir, Groton CT 06340. E-mail: 

3. Stewart AB, Lwaleed BA, Douglas DA, Birch BR

. Current drug therapy for prostate cancer: an overview. 

5. Yuen KK, Shelley M, Sze WM, Wilt T, Mason MD

. Bisphosphonates for advanced prostate cancer. 

. Higher costs may justify prophylaxis in prostate and lung cancer patients with bone metastases. 

9. Delea T, McKiernan J, Brandman J, et al

. Retrospective study of the effect of skeletal complications on total medical care costs in patients with bone metastases of breast cancer seen in typical clinical practice. 

. Analysis of skeletal-related events in breast cancer and response to therapy. 

13. Clemons MJ, Dranitsaris G, Ooi WS, et al

. Phase II trial evaluating the palliative benefit of second-line zoledronic acid in breast cancer patients with either a skeletal-related event or progressive bone metastases despite first-line bisphosphonate therapy. 

15. Karamouzis MV, Ioannidis G, Rigatos G

. Quality of life in metastatic breast cancer patients under chemotherapy or supportive care: a single-institution comparative study. 

. Chemotherapy for metastatic breast cancer [in Japanese]. 

19. Groot MT, Boeken Kruger CG, Pelger RC, Uyl-de Groot CA

. Costs of prostate cancer, metastatic to the bone, in the Netherlands. 

21. Botteman M, Barghout V, Stephens J, Hay J, Brandman J, Aapro M

. Cost effectiveness of bisphosphonates in the management of breast cancer patients with bone metastases. 

23. Lamy O, Jenzer-Closuit A, Burckhardt P

. Hypercalcemia of malignancy: an undiagnosed and undertreated disease. 

25. Rex DK, Imperiale TF, Latinovich DR, Bratcher LL

. Impact of bowel preparation on efficiency and costs of colonoscopy. 

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

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

29. DePuy V, Anstrom KJ, Castel LD, Schulman KA, Weinfurt KP, Saad F

. Effects of skeletal morbidities on longitudinal patient-reported outcomes and survival in patients with metastatic prostate cancer. 

31. Gonzalez EC, Ferrante JM,Van Durme DJ, Pal N, Roetzheim RG

. Comorbid illness and the early detection of cancer. 

33. Guralnik JM, Alecxih L, Branch LG, Wiener JM

. Medical and longterm care costs when older persons become more dependent. 

The analysis examines the annual economic effect of medically treating patients with prostate cancer and subsequent bone metastases.

The Cost of Treating Skeletal-Related Events in Patients With Prostate Cancer

: To implement an automated pharmacy dispensing prioritization system and to evaluate its effect on the timing of dispensing and administration of chemotherapy.

: An electronic chemotherapy dispensing system that prioritized orders for pharmacy processing based on anticipated patient arrival at the oncology outpatient unit was implemented, followed by an educational intervention for pharmacy staff.

: A time-controlled study evaluating the effect of the electronic chemotherapy dispensing system on late, early, and “within target" dispensing and administration of chemotherapy was conducted.

: A total of 13,138 chemotherapies were prepared and released pending medical clearance based on laboratory results (hereafter referred to as pending counts) (8677 [66.0%]) or pending arrival of the patient (hereafter referred to as pending arrival) (4461 [34.0%]) from March 1, 2005, to March 2, 2006. Chemotherapy dispensing and administration times were retrospectively compared with chemotherapy appointment times after adjustment for late patient arrival. Dispensing times continuously decreased from a mean delay in dispensing of 12 minutes after the adjusted chemotherapy appointment time at baseline to dispensing a mean of 5 minutes ahead of the scheduled time by the end of the study. Chemotherapy treatments dispensed within target increased from 62.9% to 74.7% (pending arrival) and from 53.4% to 68.1% (pending counts), and those administered within target increased from 64.9% to 71.8% (pending arrival) and from 56.0% to 70.1% (pending counts).

: An automated intervention for synchronizing chemotherapy preparation with anticipated times for administration was associated with significant reduction in wait times.

Extensive wait times for chemotherapy are a major cause of dissatisfaction for oncology patients. Waiting periods have been linked to anxiety-related problems such as anticipatory nausea and vomiting.

This vulnerable population with decreased immunity and little time to waste is often required to wait in public areas for chemotherapy to be prepared.

We describe a simple technique that involved the reordering of the chemotherapy queue in an outpatient pharmacy system that reduced wait times substantially.

The number and complexity of chemotherapy dispensed and administered in outpatient settings continue to increase,

 with added intricacies to its delivery process. Reducing wait time and associated treatment burden has been recognized as an important contributor to patient satisfaction and well-being.

Patients with cancer experience significant emotional distress, with rates of depression ranging from 14% to 33%.

 Although the effect of a cancer diagnosis and its implications for mortality are critical, one must consider the contribution of the disease burden on the patient’s quality of life.

 Uncomfortable waiting areas and the length of wait time for medical test results, appointments, and dispensing of chemotherapy have been identified as a major source of dissatisfaction.

 Waiting periods have been linked to a multitude of anxiety-related problems such as anticipatory nausea and vomiting.

 Long unoccupied wait times may contribute to patient discomfort unnecessarily, thereby increasing psychological burden and contributing to a sense of rejection or abandonment.

 The reduced life expectancy of patients with cancer aggravates this issue.

The use of pagers among patients undergoing outpatient chemotherapy had positive results such as reduction in boredom and increased peace of mind.

 Patients judge waiting periods of more than 30 minutes as unacceptable.

Realizing the negative effect of wait time on oncology patients, our goals were to implement an automated pharmacy dispensing prioritization system and to evaluate its effect on the timing of dispensing and administration of chemotherapy.

We evaluated the effects of modifications in the adult chemotherapy pharmacy dispensing process from March 1, 2005, through March 2, 2006, at the Sidney Kimmel Comprehensive Cancer Center (SKCCC) at the Johns Hopkins Hospital. The SKCCC sees 2300 new patients with cancer annually and provides more than 12,000 outpatient chemotherapy treatments. Most outpatient chemotherapy administered at the SKCCC is prepared on the day of administration to address chemotherapy stability limitations and financial concerns associated with wasting expensive pharmaceuticals because of late treatment plan changes.

Preparing chemotherapy on the day of administration requires a trigger. The outpatient chemotherapy pharmacy uses 2 triggers, pending counts (PC) and pending arrival (PA). Chemotherapy orders are classified as PC if the medical clearance based on same-day laboratory results will trigger chemotherapy preparation. Chemotherapy orders are classified as PA if physical arrival of the patient to the oncology center is sufficient to trigger preparation. All patients visiting the oncology outpatient unit with PC and PA chemotherapy were included in this study.

During the baseline period from March 1, 2005, to May 5, 2005, chemotherapy orders were prepared in the oncology pharmacy in chronologic order based on patient arrival time (PA) or the time of medical clearance (PC). This chronologic order was communicated to the pharmacist electronically via a point-of-service computer system. This system avoided the preparation of chemotherapy for patients who failed to show for an appointment or who failed medical clearance.

After preparation and quality control, the pharmacist logged the chemotherapy as dispensed in the information system, and it was transported to the oncology outpatient unit via a conveyer belt. At the oncology unit, the chemotherapy administration followed an appointment schedule. A chemotherapy appointment time was assigned to each patient in advance based on protocol requirements, patient preference, and provider and space availability. To reduce the number of SKCCC visits, patients were frequently scheduled to see their medical provider, undergo diagnostic testing, or receive radiation therapy on the day of chemotherapy administration.

In the baseline first-come, first-serve system, chemotherapy delivered to the oncology outpatient unit in the morning was often not administered until late in the afternoon because of patient unavailability. Meanwhile, chemotherapy needed early for patients waiting at the oncology unit often would not arrive until hours later. This system resulted in prolonged patient wait times, decreased patient satisfaction, frequent telephone calls, and increased stress levels for nurses and pharmacists.

To reduce wait times and intervals between dispensing and administration of chemotherapy, an electronic chemotherapy dispensing system was introduced that prioritized dispensing based on anticipated patient arrival at the oncology outpatient unit. Previously, dispensing had been prioritized based on the chronologic order of the patient’s arrival to the institution and the time of medical clearance to receive chemotherapy. Following the modification of the order queue in the electronic dispensing system, the pharmacy staff was educated in a 1-hour session on the importance of avoiding order batching and on the need to prioritize chemotherapy orders as indicated by the electronic dispensing system.

 Visual reminders were posted in the work area as reinforcement.

The implementation and evaluation order was as follows. Baseline performance was measured from March 1, 2005, to May 5, 2005. The intervention was implemented in 3 phases. Phase 1 began May 6, 2005, and included introduction of a basic version of the electronic chemotherapy dispensing system that prioritized chemotherapy orders for pharmacist processing based on the rescheduled appointment time without accounting for late patient arrival. Phase 2 began September 29, 2005, when the electronic dispensing system was refined to account for late patient arrival and to modify chemotherapy order priority accordingly. Phase 3 began February 6, 2006, when the already described educational intervention for pharmacy staff was conducted.

 shows the final design of the electronic chemotherapy dispensing system. On arrival (PA) or medical clearance (PC), the patient’s order is ranked according to an adjusted chemotherapy appointment time calculated as the original chemotherapy appointment time plus an adjustment period for late patient arrival. The adjusted period is calculated as the difference between a patient’s first appointment of the day and his or her registration time on arrival. (For example, if the patient’s first appointment is at 8:30 AM and his or her registration time is at 8:40 AM, the adjusted chemotherapy appointment time would be 10 minutes later than the prescheduled chemotherapy appointment time.) No adjustment is made when a patient arrives or is registered early or on time.

To evaluate the effectiveness of the interventions, 2 outcome measures were used throughout. These included chemotherapy dispense interval (dispense time minus adjusted chemotherapy appointment time) and chemotherapy administration interval (administration time minus adjusted chemotherapy appointment time). A negative value indicates that the chemotherapy was dispensed or administered before the adjusted chemotherapy appointment time. Chemotherapy dispense time was defined as the time the pharmacist electronically labeled the chemotherapy as dispensed. Chemotherapy administration time was defined as the start time of the first chemotherapy infusion.

The oncology clinical information system (OCIS) at the Johns Hopkins Hospital

 tracks appointment times, patient arrivals, medical clearance times (if applicable), and chemotherapy dispensing and administration times. Data are logged automatically (on arrival through bar-coded card swipe at a kiosk) and by the staff in real time. The OCIS functions as an electronic communication tool among admission, pharmacy, and oncology staff providing up-to-the-minute information on patient status.

From March 1, 2005, to March 2, 2006, all patients with PC and PA chemotherapy orders were included. Data were retrieved from the OCIS for each outpatient visit, including the patient’s appointments, time of registration, time of medical clearance if applicable, time of chemotherapy dispensing, and time of chemotherapy administration. All visits missing 1 or more of the data points were excluded from the study sample.

No patient identifiers or information on staff performance was collected. This quality improvement project was performed with departmental and institutional leadership approval.

Data were retrieved retrospectively from the OCIS. The chemotherapy administration and dispense intervals were calculated for each visit. Dispense time earlier than 90 minutes before the adjusted chemotherapy appointment time was considered early and more than 30 minutes after the adjusted chemotherapy appointment time was considered late. Administration time earlier than 45 minutes before the adjusted chemotherapy appointment time was considered early and more than 75 minutes after the adjusted chemotherapy appointment time was considered late. Administration and dispense intervals that did not meet time criteria for early or late were considered “within target.”

A daily mean of dispense intervals was calculated for all chemotherapy orders (PC and PA), and a regression analysis across the study period was performed. Data were analyzed using Minitab statistical software (Minitab Inc, State College,  Pennsylvania).

From March 1, 2005, to March 31, 2006, 13,138 chemotherapies were prepared in the SKCCC pharmacy. These were released PC 8677 [66.0%]) or PA (4461 [34.0%]) (

One hundred twelve different chemotherapy agents or chemotherapy combinations were dispensed for 1807 unique patients. Combined chemotherapy agents (such as cyclophosphamide, doxorubicin, vincristine, and prednisone) accounted for 1595 (12.1%) of all dispensed chemotherapies. The number of chemotherapies dispensed ranged from 1 to 57 per patient (mean, 7.3; median, 5).

Times for appointment, registration, dispensing, and chemotherapy administration were available for 8976 chemotherapies (68.3%). There was no significant difference between the distribution of all chemotherapies and of those for which  administration and dispense times were available.

From March 1, 2005, to March 31, 2006, chemotherapy dispense intervals decreased from a mean delay in dispensing of 12 minutes after the adjusted chemotherapy appointment time at baseline to dispensing 5 minutes ahead of the scheduled time by the end of the study. 

 shows the reduction in dispense intervals based on daily means (regression coefficient [SE], −0.05 [0.01]; P <.001).

Further analysis was performed in the 2 major groups (PA and PC). The outcome measures for all interventions were the proportions of chemotherapy dispensed and administered within the desired period (within target).

 gives the PA dispense intervals. There was a significant difference in the dispense interval between groups (c2 6 = 39.9, P <.001). 

 shows the dispense intervals by phase. Each subsequent phase was associated with increases in percentages for chemotherapy dispensed within target.

Table 2 also gives the PA administration intervals. There was no difference in the administration interval for chemotherapy between baseline, phase 1, and phase 2. There was a significant difference between phase 3 and baseline (c2 2 = 39.7, P <.001).

 gives the PC dispense intervals. There was a significant difference in the dispense interval between groups (c2 6 = 59.9, P <.001). Figure 3 shows the dispense intervals by phase. While at baseline only one-half of the chemotherapies were dispensed within target, during phase 3 this had increased to more than two-thirds.

Table 3 also gives the PC administration intervals. There was a significant difference in the administration interval between groups (c2 6 = 43.7, P <.001). 

 shows the administration intervals by phase. As with chemotherapy dispense intervals, we saw an improvement in chemotherapy administration intervals from baseline to phase 1, a slight drop in performance during phase 2, and again a significant performance improvement during phase 3.

Chemotherapy delivery in the ambulatory setting is becoming increasingly complex.

 The high numbers of patients and progressively more intricate treatment regimens lead to increased demand on pharmacy services that may translate into prolonged patient waiting. Waiting has been associated with decreased patient satisfaction and can be a source of anxiety and stress to oncology patients.

Patient wait times during chemotherapy treatment are the result of therapeutic indications (eg, infusion times) and process delays. Chemotherapy administration is always associated with variable periods of waiting dependent on the time required for hydration and chemotherapy infusions. As complicated combination regimens become the norm for most oncologic diagnoses, these issues will become more problematic in the future.

Ideally, oncology patients should not need to wait for their chemotherapy to be prepared for administration. However, in view of high drug costs

Using a prioritization algorithm in an oncology pharmacy system at the Johns Hopkins University, patient wait times for chemotherapy administration were significantly decreased.

May 2008 - Theme Issue

List of Peer Reviewers (2007)

Is Disease Management Effective? Well...Some is.

Complementary and Alternative Medicine Provider Use and Expenditures by Cancer Treatment Phase

The Cost of Treating Skeletal-Related Events in Patients With Prostate Cancer

No Time to Waste: Decreasing Patient Wait Times for Chemotherapy Administration Using Automated Prioritization in an Oncology Pharmacy System

Second-line and Third-line Chemotherapy for Lung Cancer: Use and Cost

Managed Care and Cancer Outcomes for Medicare Beneficiaries With Disabilities

The Effect of HEDIS Measurement of Colorectal Cancer Screening on Insurance Plans in Pennsylvania

Cancer Screening Across the Aging Continuum

Optimal Cancer Care Across the Spectrum of Life and Disease