Medication prescription patterns in cancer rehabilitation physiatry outpatients: A multicenter retrospective study

Abstract

Background and Objective

Previous work from this dataset found 45% prescribing frequency of any medications (35.1%, 33.5%, and 56.8% at institutions 1, 2, and 3 respectively), without significant difference between in‐person and telemedicine. The current study examines medication prescription patterns in greater depth by type of medications.

Design

Multicenter retrospective study.

Setting

Three academic medical centers in the United States.

Participants

Patients with cancer or history of cancer.

Interventions

Cancer rehabilitation physiatry outpatient visits.

Main Outcome Measures

Frequency of medication types prescribed.

Results

A total of 1234 unique patients underwent 3006 visits (institutions 1, 2, and 3 with 614, 1638, and 754 visits, respectively). Most frequently prescribed medication types (percentage of total encounters followed by 95% confidence interval) included opioids (14.5%; 8.1%–21.1%), membrane stabilizers (11.3.%; 5.2%–17.3%), topical agents (8.7%; 2.7%–14.7%), antidepressants (8.3%; 5%–11.8%), muscle relaxants (5%; 1.8%–8.2%), analgesics (nonsteroidal anti‐inflammatory agents or acetaminophen) (3.7%; 3.4%–4.1%), and injected agents (5%;1.7%–8.3%). Less frequent categories included bowel medications (2.4%; 0.9%–3.9%), benzodiazepines (2.6%; 0.7%–4.5%), antiemetics (1.8%; 0.3%–3.3%), antibiotics (0.7%; 0.4%–1.0%), sleep aids (1%; 0.5%–1.5%), antipsychotics (1%; 0.1–1.2%), oral corticosteroids (0.5%; 0.3%–0.7%), and cognitive medications (0.6%; 0.2%–1%). Other medications were prescribed in 9.2% (3.5%–14.9%) of encounters. Interinstitutional differences were seen for all medication categories (p < .001), except for nonopioid analgesics (p = .640), antibiotics (p = .4), and corticosteroids (p = .337).

Conclusion

Treating pain appears to be a predominant but not sole focus of medication prescription in this population. Results illuminate prescribing trends seen in actual practice and may serve to inform individual cancer rehabilitation physiatrist practice, as well as to provide insight into possible directions for future research, such as focusing on frequently prescribed medications in controlled trials, and further examination of “other” medications, which were prescribed at a higher frequency than anticipated. Limitations include that factors underlying the observations could not be determined, including overall acuity; cancer type, stage, or phase; probable impact of differences in dispensation of refills depending on medication type; variability in clinically intended purposes of medications for specific cases; and possible regulatory or institution‐specific contexts.

INTRODUCTION

Cancer remains one of the leading causes of morbidity and mortality worldwide, with an estimated 2 million newly diagnosed cases and 600,000 deaths in the United States in 2024. ¹ Advances in cancer treatment have significantly improved survival rates, but they often come at a cost, leading to survivors of cancer experiencing physical, cognitive, and psychosocial challenges that negatively affect their quality of life. ² Examples of these impairments include chemotherapy‐induced neuropathy, cancer‐related fatigue, and lymphedema, to name a few. Comprehensive multidisciplinary care is often needed, sometimes long term, with outpatient cancer rehabilitation physiatry playing a vital role.

Cancer rehabilitation focuses on a multidisciplinary approach that works to optimize function and quality of life throughout the cancer care continuum. ³ These interventions target physical, cognitive, and psychological impairments patients may face during cancer treatment and survivorship, enabling individuals to return to meaningful activities and improve their overall well‐being. ⁴ Cancer rehabilitation physiatrists are uniquely equipped to evaluate and manage multifactorial sequelae of cancer and its treatments, often coordinating with other rehabilitation disciplines and medical specialists and providing interventions such as pharmacologic treatments, procedures, education, and addressing equipment needs. Despite the benefits, cancer rehabilitation physiatry remains underused in clinical practice and guidelines. ⁵ , ⁶ Training gaps also persist, with one survey reporting that less than half of physical medicine and rehabilitation residencies had dedicated faculty for cancer rehabilitation and only a quarter of them had outpatient clinics focused on rehabilitation needs for cancer patients. ⁷ There is still work to be done so patients with cancer can receive adequate care for their cancer‐related symptoms, impairments, and functional limitations.

Medication management is a critical, yet underexplored, area in the cancer rehabilitation realm. Pharmacological treatments are commonly used to manage cancer‐related pain, as well as other issues such as depression, anxiety, sleep disturbance, spasticity, fatigue, etc. ² , ⁸ , ⁹ , ¹⁰ , ¹¹ Previous studies focused primarily on medication management of these individual symptoms. For example, the National Comprehensive Cancer Network and American Society of Interventional Pain Physicians both have guidelines for addressing cancer‐related pain, which can include opioid management. ⁹ , ¹² However, opioid prescribing among cancer rehabilitation physiatrists appears mixed, with one survey finding cancer rehabilitation physiatrists to be evenly divided as to whether they incorporate opioid management into their own practice or not, and with 54% reporting that they believe complex opioid management is better suited for palliative care providers. ³ Although opioids remain a cornerstone for managing moderate to severe cancer‐related pain, concerns regarding long‐term opioid use, risk of dependency, and regulatory scrutiny may influence physician prescription behaviors. Some physiatrists may feel that their role should focus more on restoring functional status, nonopioid pharmacologics, and rehabilitation‐based strategies, while reserving complex opioid titration for palliative care specialists. Conversely, others may argue that comprehensive cancer rehabilitation should include the capacity to manage opioids when appropriate, especially if the patient's functional goals are impeded due to inadequately controlled pain.

Even though the highly specialized knowledge, skill, and experience of cancer rehabilitation physiatrists have been recognized in appropriately evaluating and managing symptoms and physical impairments in this complex population, ¹⁴ there is a lack of research examining overall medication prescription patterns seen in cancer rehabilitation settings, including commonalities and variances. While understanding that many factors can underlie medication prescribing patterns, which can reasonably vary between providers and between settings, the availability of such data would inform physicians and serve as a point of reference as the field evolves and as best practices are developed in the future.

Previous retrospective work examining outpatient cancer rehabilitation physiatry practices at four institutions found that the overall medication prescribing frequency at in‐person and telemedicine outpatient cancer rehabilitation encounters did not significantly differ, at 38.9% of telemedicine visits versus 36.7% of in‐person random visits, which included both new and follow‐up encounters. ¹⁵ This current study aims to look more closely at specific trends in medication prescribing by type of medication and also to examine possible interinstitutional variations, in a comprehensive and real‐world based context. The clinical relevance of examining medication prescribing patterns in cancer rehabilitation physiatry is heightened by the growing population of cancer survivors and the rapid expansion of the field as a subspecialty. By providing insights into the landscape of current practice, this study aims to better characterize a major aspect of care delivery, which can provide a touchstone for clinicians currently in practice, as well as a platform which can inform future research.

METHODS

Retrospective data were gathered from three academic outpatient cancer rehabilitation physiatry clinics. These clinics corresponded to three of the four institutions in the original study by Leong et al., for which sufficient detail about medications was available. The fourth institution provided information about whether medications were prescribed, but the specific medications (or medication categories) were not available. A data use agreement between all three institutions was acquired before sharing information. Participant identifiers were pseudonymized to ensure confidentiality. Patients 18 years or older were included in our analysis. Outpatient visits from March 23, 2020 to August 31, 2021 (which was the time frame of our original data set) with a cancer diagnosis coded in the encounter were included. Noncancer‐related and procedure‐only encounters were excluded. Patient demographics (age, gender, race), encounter type (new vs. follow up), and all medications prescribed were collected.

The prescribing frequency by cancer rehabilitation physiatrists of individual medication categories was assessed in a binary fashion per encounter, rather than total number of medications within that category or the total dosing or quantity of the medications prescribed. For example, if a long‐acting and a short‐acting opioid were prescribed within the same encounter, this intervention would track as positive for the opioid category within that encounter but would not be double counted. However, all of the individual medications were separately tallied such that overall prescribing trends could be assessed, with regard to the medication categories and also with regard to individual medications that were prescribed more or less frequently within this cohort. If multiple medications were prescribed within an encounter from different categories, these would each be credited to their respective medication categories.

Medications were categorized into the following groups: opioids, membrane stabilizers, topical agents, antidepressants, muscle relaxants, nonopioid analgesics, injections, bowel medications, benzodiazepines, antiemetics, antibiotics, oral corticosteroids, sleep aids, cognitive enhancers, and antipsychotics. Neuropathic pain agents, such as gabapentin and pregabalin, are referred as membrane stabilizers in pain literature. ¹² Any medications prescribed that did not fit into these predefined categories were grouped under an “other” category. Topically applied agents that act systemically (such as opioid or stimulant medication patches) were counted within their respective medication categories rather than as topical agents, with the topical agent category presumed to be for focal anatomic treatment. The categories were selected by consensus of the authors as most likely to be relevant to cancer rehabilitation physiatry practice.

Prescribing frequencies by medication category were reported descriptively, as well as with 95% confidence intervals (CIs) for interinstitutional variances in prescribing, by each medication category. Inter‐institutional differences by medication category were also evaluated with chi‐square analyses. Chi‐square analysis was done on Sigmaplot 13.0, and standard deviation and confidence intervals were performed in Microsoft Excel (2019).

RESULTS

Results showed that 1234 unique patients underwent 3006 encounters (Table 1). There were a total of 614, 1638, and 754 visits at institutions 1, 2, and 3, respectively. The most frequently prescribed medication types were opioids and membrane stabilizers, at 14.5% (95% CI 8.1%–21.1%) and 11.3% (95% CI 5.2%–17.3% of encounters, respectively, followed by other medications (9.2%; 95% CI 3.5–14.9%), topicals (8.7%; 95% CI 2.7%–14.7%), antidepressants (8.3%; 95% CI 5%–11.8%), injections (5%; 95% CI 1.7%–8.3%) and muscle relaxants (5%; 95% CI 1.8%–8.2%). Medications from each remaining category were prescribed at ≤5% of the visits. A summary of the findings can be found in Tables 1 and 2. Chi‐square analysis revealed statistically significant interinstitutional differences for all medication categories, except for nonopioid analgesics (p = .64), antibiotics (p = .4), and corticosteroids (p = .337). A summary of the findings can be found in Figure 1. A summary of total breakdown of individual medication prescriptions can be found in Tables 3 and 4.

TABLE 1.

Sample characteristics.

Parameter	Total sample	Institution 1	Institution 2	Institution 3
Number of encounters	3006	614	1638	754
Unique patients	1234	326	573	335
Age, y (SD)	59.9 (13.1)	62.0 (11.5)	59.3 (13.0)	58.7 (12.9)
Male (%)	339 (27.5)	117 (35.9)	121 (21.1)	101 (30.1)
Female (%)	895 (72.5)	209 (64.1)	452 (78.9)	234 (69.9)
Asian (%)	157 (5.2)	7 (2.1)	48 (8.4)	14 (4.2)
Black (%)	653 (21.7)	117 (35.9)	42 (7.3)	100 (29.9)
White (%)	1994 (66.3)	177 (54.3)	428 (74.7)	208 (62.1)
Other (%)	202 (6.7)	25 (7.7)	55 (9.6)	13 (3.9)
New patient visits (%)	809 (26.9)	200 (32.6)	411 (25.1)	198 (26.3)
Follow‐up visits (%)	2197 (73.1)	414 (67.4)	1227 (74.9)	556 (73.7)

TABLE 2.

Medication prescriptions rates by medication category.

Medication class	% of visits prescribed Average (Inst 1, Inst 2, Inst 3)	p value	Confidence interval (%)
Opioids	14.5 (6.0, 15.4, 19.7)	<.001	8.1–21.1
Membrane stabilizers	11.3 (13.2, 6.7, 19.8)	<.001	5.2–17.3
Other	9.2 (1.1, 13.5, 6.5)	<.001	3.5–14.9
Topical agents	8.7 (9.4, 4.5, 17.2)	<.001	2.7–14.7
Antidepressants	8.3 (3.5, 9.7, 10.5)	<.001	5–11.8
Injections	5 (5.8, 6.9, 0.3)	<.001	1.7–8.3
Muscle relaxants	5 (2.6, 3.8, 9.1)	<.001	1.8–8.2
Nonopioid analgesics	3.7 (3.5, 3.5, 4.2)	.640	3.4–4.1
Benzodiazepines	2.6 (0.2, 4.1, 1.2)	<.001	0.7–4.5
Bowel medications	2.4 (0.2, 3.5, 1.8)	<.001	0.9–3.9
Antiemetics	1.8 (0.2, 3.1, 0.3)	<.001	0.3–3.3
Antipsychotics	1 (0, 1.9, 0)	<.001	0.1–1.2
Sleep aids	1 (0.2, 1.0, 0.9)	.042	0.4–1.5
Antibiotics	0.7 (0.3, 0.7, 0.9)	.400	0.4–1.0
Cognitive enhancers	0.6 (0.5, 0.8, 0)	.034	0.2–1
Corticosteroids	0.5 (0, 0.7, 0.5)	.337	0.3–0.7

FIGURE 1.

Average prescription rates for combined institutions with analysis with 95% confidence intervals.

TABLE 3.

Breakdown of medications prescribed in each class.

Medication class	Medication	Institution 1	Institution 2	Institution 3	Total medications
Oral opioids	Oxycodone	6	103	97	206
Oral opioids	Morphine	0	54	13	67
Oral opioids	Tramadol	12	28	19	59
Oral opioids	Hydrocodone	1	35	8	44
Oral opioids	Oxycodone‐acetaminophen	16	7	10	33
Oral opioids	Hydromorphone	0	16	7	23
Oral opioids	Levorphanol	0	8	0	8
Oral opioids	Acetaminophen‐codeine	0	2	4	6
Oral opioids	Methadone	2	3	0	5
Total oral opioids		37	256	158	451
Membrane stabilizers	Gabapentin	68	87	97	252
Membrane stabilizers	Pregabalin	13	22	59	94
Membrane stabilizers	Topiramate	0	1	0	1
Total membrane stabilizers		81	110	156	347
Topical agents (local)	Lidocaine	31	21	77	129
Topical agents (local)	Diclofenac	24	16	45	85
Topical agents (local)	Capsaicin	1	0	7	8
Topical agents (local)	Ketamine	0	0	4	4
Topical agents (local)	Nystatin	2	0	0	2
Topical agents (local)	Desonide	0	0	2	2
Topical agents (local)	Ammonium lactate	1	0	0	1
Topical agents (local)	Nystatin‐triamcinolone	1	0	0	1
Topical agents (local)	Lidocaine‐prilocaine	1	0	0	1
Topical agents (local)	Fluorouracil	0	0	1	1
Topical agents (local)	Mupirocin	0	0	1	1
Topical agents (local)	Ketoconazole	0	0	1	1
Topical agents (local)	Imiquimod	0	0	1	1
Topical agents (local)	Clotrimazole	0	1	1	2
Topical agents (local)	Metronidazole	0	0	1	1
Topical agents (local)	Gentamicin	0	0	1	1
Topical agents (local)	Diphenhydramine‐zinc acetate	1	0	0	1
Topical agents (local)	Silver sulfadiazine	0	1	0	1
Topical agents (local)	Amitriptyline‐baclofen	0	0	1	1
Total topical agents (local)		62	39	143	243
Topical agents (systemic)	Fentanyl patch	0	27	0	27
Topical agents (systemic)	Clonidine	0	1	0	1
Topical agents (systemic)	Methylphenidate	0	1	0	1
Total topical agents (systemic)		0	29	0	29
Antidepressants	Duloxetine	15	56	50	121
Antidepressants	Bupropion	0	21	3	24
Antidepressants	Trazodone	1	16	3	20
Antidepressants	Nortriptyline	0	4	15	19
Antidepressants	Venlafaxine	0	13	6	19
Antidepressants	Amitriptyline	1	5	11	17
Antidepressants	Escitalopram	0	17	0	17
Antidepressants	Mirtazapine	5	6	0	11
Antidepressants	Doxepin	0	7	0	7
Antidepressants	Sertraline	1	4	1	6
Antidepressants	Buspirone	0	4	0	4
Antidepressants	Fluoxetine	0	4	0	4
Antidepressants	Citalopram	0	3	0	3
Total antidepressants		23	160	89	272
Muscle relaxants	Cyclobenzaprine	9	38	33	80
Muscle relaxants	Baclofen	0	6	26	32
Muscle relaxants	Methocarbamol	4	15	11	30
Muscle relaxants	Tizanidine	3	4	6	13
Total muscle relaxants		16	63	76	155
Nonopioid analgesics	Meloxicam	10	23	9	42
Nonopioid analgesics	Ibuprofen	3	12	4	19
Nonopioid analgesics	Acetaminophen	4	0	14	18
Nonopioid analgesics	Celecoxib	0	7	1	8
Nonopioid analgesics	Diclofenac oral	3	1	4	8
Nonopioid analgesics	Nabumetone	0	1	1	2
Nonopioid analgesics	Indomethacin	1	0	0	1
Nonopioid analgesics	Sulindac	0	1	0	1
Nonopioid analgesics	Naproxen	0	0	1	1
Nonopioid analgesics	Aspirin	0	0	1	1
Total nonopioid analgesics		21	45	35	101
Injections	Lidocaine	18	70	0	88
Injections	Triamcinolone	10	32	0	42
Injections	Methylprednisolone	2	0	0	2
Injections	Onabotulinumtoxina	0	2	0	2
Injections	Ketorolac	1	0	0	1
Injections	Denosumab	1	0	0	1
Injections	Octreotide	0	1	0	1
Injections	Epinephrine	0	0	1	1
Injections	Prolia	0	0	1	1
Injections	Flucelvax	0	0	1	1
Total injections		32	105	3	140
Bowel medications	Senna	0	33	11	44
Bowel medications	Polyethylene glycol	0	7	9	16
Bowel medications	Bisacodyl	0	6	5	11
Bowel medications	Docusate sodium	0	6	4	10
Bowel medications	Diphenoxylate‐atropine	0	6	0	6
Bowel medications	Magnesium hydroxide/citrate	0	1	3	4
Bowel medications	Lactulose	0	4	0	4
Bowel medications	Naloxegol	0	3	0	3
Bowel medications	Loperamide	0	3	0	3
Bowel medications	Enema mineral oil	0	0	2	2
Bowel medications	Lactobacillus	0	2	0	2
Bowel medications	Linaclotide	0	0	1	1
Bowel medications	Dicyclomine	0	1	0	1
Total bowel medications		0	72	35	107
Benzodiazepines	Lorazepam	0	22	6	28
Benzodiazepines	Clonazepam	0	23	0	23
Benzodiazepines	Alprazolam	0	13	2	15
Benzodiazepines	Diazepam	0	5	1	6
Benzodiazepines	Temazepam	0	4	1	5
Benzodiazepines	Chlordiazepoxide	0	1	0	1
Total benzodiazepines		0	68	10	78
Antiemetics	Ondansetron	1	34	1	36
Antiemetics	Prochlorperazine	0	9	0	9
Antiemetics	Granisetron	0	7	0	7
Antiemetics	Metoclopramide	0	5	0	5
Total antiemetics		1	55	1	57
Antibiotics	Sulfamethoxazole‐Trimethoprim	1	2	2	5
Antibiotics	Cephalexin	1	1	2	4
Antibiotics	Amoxicillin	0	1	2	3
Antibiotics	Clindamycin	0	0	3	3
Antibiotics	Doxycycline	0	2	0	2
Antibiotics	Nitrofurantoin	0	1	0	1
Antibiotics	Azithromycin	0	1	0	1
Total antibiotics		2	8	9	19
Oral corticosteroids	Methylprednisolone	0	2	1	3
Oral corticosteroids	Prednisone	0	1	2	3
Oral corticosteroids	Dexamethasone	0	5	1	6
Total oral corticosteroids		0	8	4	12
Sleep aids	Zolpidem	0	5	1	6
Sleep aids	Eszopiclone	0	6	0	6
Sleep aids	Ramelteon	0	3	0	3
Sleep aids	Melatonin	1	1	1	3
Sleep aids	Zaleplon	0	1	0	1
Total sleep aids		1	16	2	19
Cognitive enhancers	Methylphenidate	1	11	0	12
Cognitive enhancers	Dextroamphetamine	0	4	0	4
Cognitive enhancers	Modafinil	0	3	0	3
Total cognitive enhancers		1	18	0	19
Antipsychotics	Olanzapine	0	19	0	19
Antipsychotics	Quetiapine	0	2	0	2
Total antipsychotics		0	21	0	21

TABLE 4.

Medication breakdown in Other category.

Other	Institution 1	Institution 2	Institution 3	Total
Vitamin group	0	34	14	48
Miscellaneous medications prescribed once	0	28	16	44
Naloxone	2	29	6	37
Omeprazole	0	14	1	15
Hyoscyamine	0	13	0	13
Dronabinol	0	12	0	12
Pentoxifylline	0	6	5	11
Magnesium oxide	0	10	0	10
Mucinex	3	5	0	8
Scopolamine patch	0	5	1	6
Calcium carbonate	0	5	1	6
Ferrous sulfate	0	4	1	5
Alpha linoleic acid	0	5	0	5
Levothyroxine	0	1	3	4
Famotidine	0	4	0	4
Oxybutynin	0	4	0	4
Furosemide	1	1	1	3
Losartan	0	1	2	3
Nitroglycerin	0	3	0	3
Zinc	0	3	0	3
Tamoxifen	0	3	0	3
Thyroid (pork)	0	3	0	3
Alpha lipoic acid	0	0	2	2
Psyllium	0	0	2	2
Hydrochlorothiazide	0	1	1	2
Anastrozole	0	1	1	2
Nifedipine	0	1	1	2
Colchicine	0	1	1	2
Benzonatate	0	2	0	2
Fluticasone	0	2	0	2
Loratadine	0	2	0	2
Cetirizine	0	2	0	2
Glipizide	0	2	0	2
Metformin	0	2	0	2
Propanolol	0	2	0	2
Sumitriptan	0	2	0	2
Total other	6	213	59	278

DISCUSSION

This retrospective analysis of medication prescription patterns in outpatient cancer rehabilitation medicine clinics examines the diverse pharmacologic approaches physiatrists take for their patients. Across three academic institutions, notable differences were observed in prescribing frequency within nearly all medication categories. Of note, the patients in all three of these institutions (except for a negligible number of upper gastrointestinal and pancreatic cancer patients in institution 1 who were seen with a prehabilitation protocol) had been referred per usual course of care and not automatic multidisciplinary protocols. But, despite these significant interinstitutional differences in prescribing frequencies within most medication categories, an overarching picture emerged in which medication categories typically related to pain control are represented more heavily than medication categories for other symptom control or impairment needs.

With regard to the interinstitutional differences, these variances may be expected and appropriate, based on such factors as differences in patient mix (ie, cancer or impairment type, sociodemographic influences), availability of other supportive care providers at the institution, and reasonable discretionary differences in clinical practice, reflecting clinician autonomy and hyperspecialized areas of expertise. However, the differences seen within the data may also be influenced by the limitations of the study, as detailed later in the article, as well as by practice variations that might benefit from future attention, to evaluate and optimize alignment with evidence‐based practices. Such efforts could include examination of adherence to existing guidelines for treatment of particular symptoms or impairments, including both pharmacological and nonpharmacological aspects of care, as well as development of new guidelines which more cohesively incorporate rehabilitation principles and functional outcomes. ⁵ Of course, medication selection is complex, with patient factors such as comorbidities and polypharmacy concerns affecting clinical decision‐making, adding to the challenge of developing generalized treatment recommendations.

Opioids and membrane stabilizers were the most frequently prescribed medication classes, accounting for 14.5% and 11.3% of total encounters, respectively. These patterns appear consistent with the high prevalence of pain and neuropathic complications seen in patients going through treatment or experiencing long‐term sequelae. ⁹ One recent meta‐analysis reported prevalence of pain as 44.5% of patients through all phases of treatment, including moderate to severe pain in 30.6% of patients; lowest rates of pain were found in patients subsequent to curative treatment (35.8%), and highest rates were seen in those with advanced, metastatic or terminal disease (54.6%). ¹⁶ Our data showed substantial interinstitutional variability in prescribing rates in virtually all medication categories, including ranges of between 6% and 19.7% for opioids and 6.7%–19.8% for membrane stabilizers. From an opioid standpoint, the results may align with existing survey data of variability in opioid prescribing practices among cancer rehabilitation physiatrists. ¹³ The fact that opioids are such a prominent category may have confounding elements (see limitations), but, in spite of that concern, our findings support that opioid prescribing remains in the mix of pharmacological interventions among cancer rehabilitation physiatrists. In an era of declining opioid prescribing, even among cancer patients, and increased prescription of gabapentinoids in this population, ¹⁷ , ¹⁸ further studies examining the clinical context in which physiatrists are prescribing opioids would be of interest. In addition, the frequent use of topical agents (8.7%), antidepressants (8.3%) and muscle relaxants (5%) suggest the need to address localized pain, mood disorders, and muscular, myofascial, or spastic etiologies. ¹⁹

Antibiotics (0.7%), cognitive enhancers (0.6%), and corticosteroids (0.5%) were among the lowest rates of being prescribed. This may reflect more targeted use of these agents within a specific subset of patients with cancer. Antibiotics and corticosteroids were also found to have no interinstitutional difference in prescription rates, suggesting a possibly more uniform approach to their use, but it is also possible that statistical significance was not achieved due to low numbers. Other categories such as bowel medications, benzodiazepines, and antiemetics, exhibited marked variability, which likely reflects different institutional (or provider) approaches to being involved in managing systemic side effects of cancer treatment, or that different types of cancer treatments being offered at an institution that would create differences in common side effects. The low rates of sleep aids being prescribed was surprising, averaging just 1% of visits, though it is almost certain that medications in other categories were functioning as sleep aids in some cases and also that some patients were taking nonprescription medications for sleep.

Of particular note is that a relatively high percentage of encounters (9.2%) involved medications prescribed outside of our predefined categories. Upon inspection, many of these medications related to primary care needs, such as hypertension, thyroid disease, hyperlipidemia, inhalers, etc., suggesting that some cancer rehabilitation physiatrists are managing primary care issues, or, perhaps more likely, serving in a care coordination role for patients to help them avoid experiencing gaps in their needed medications. It is also possible that some of these medications, such as certain blood pressure medications, may actually have been prescribed for other issues such as anxiety, headache, or limb swelling. The “other” category also contained vitamins and supplements, suggesting that this is a more active focus of prescribing practice among cancer rehabilitation physiatrists than we had anticipated; and, furthermore, like the sleep aids, these substances may actually be underrepresented in our data because most are available over the counter.

There are limitations in this retrospective study. Underlying factors that could possibly influence medication prescriptions could not be determined. Such possible factors include patient acuity, cancer type, variability in contextually intended purposes of medications for specific clinical situations, and possible institution‐specific circumstances. Furthermore, the extent of refills given with each prescription could not be analyzed, potentially skewing the data and probably resulting in overrepresentation of opioids, because they are monitored closely and typically require more frequent patient visits. In some instances, patients were prescribed more than one opioid in an encounter, such as a long‐acting agent as the mainstay and a short‐acting agent for breakthrough pain, further weighting this category. Conversely, medications available over the counter, especially nonprescription analgesics, are likely underrepresented. Given the multidisciplinary management of cancer pain and other symptom needs, medications evaluated in this study could have been prescribed by other physicians of the team such as palliative care specialists or oncologists and not captured in our data. ²⁰

In addition, although we had a large data set with numerous encounters, we were limited by a small number of providers that are represented within each institution. Furthermore, the clinical practices of the providers represented in this sample may or may not be representative of all or most cancer rehabilitation practices. Our original sample also had a mix of in‐person and telemedicine encounters, but due to the configuration of our available datasets we did not have the information formatted for specific medications (or medication categories) by telemedicine versus in‐ person encounters for all of the institutions, therefore we were not able to include this dimension in our analysis. This dataset is from the pandemic time period, and, although we learned from our parent study that medication prescribing frequency did not significantly differ between in‐person or virtual visits, it is possible that the pandemic period itself might have influenced medication prescribing in unknown other ways.

Lastly it is worth noting that polypharmacy has been documented in the population of cancer survivors. One study revealed a significantly higher percentage of survivors of cancer who were prescribed more than five unique medications at a time compared to controls without cancer. ²¹ Specifically, they found significant differences between central nervous system agents and opioids for this population. These prescriptions could potentially exacerbate the risk of adverse drug events, financial toxicity, poor adherence, and drug–drug interactions for survivors of cancer. It is possible that for some of the medication categories with lower prescribing rates in this study (for example, cognitive medications or sleep aids), avoidance was intentional in the interest of minimizing polypharmacy.

The information obtained in this study offers value from multiple perspectives. Cancer rehabilitation physiatrists may I have a natural interest in the practice patterns of their peers. Although the results do not permit clinical recommendations, they may serve as a reference point for clinicians evaluating their own prescribing patterns. Furthermore, in some settings there may be limited awareness of the availability and value of cancer rehabilitation physiatry in oncology care, as distinct from other rehabilitation disciplines. This study provides information regarding one aspect of the physiatric role in cancer rehabilitation care, raising awareness.

The results of this study have implications for future research. For example, we have identified the medication types most commonly prescribed in three cancer rehabilitation physiatry clinics; this can inform decision‐making regarding which types of medications might be most suitable for evaluation in future cancer rehabilitation studies, including prospective controlled studies that evaluate outcomes in this population. Other potential future directions could include evaluating medication use by cancer type or other subcategories such as patient age, phase of disease, or type of impairment. The large and varied “other” medication category warrants further examination of the scope of care delivery in the clinics, for example, the possibility that cancer rehabilitation physiatrists are helping to bridge care needs typically managed by other specialists, or are incorporating significant elements of integrative medicine. The medication categories with lower prescribing frequencies are also of interest for future work, as we question the extent to which our findings are representative for these categories or vary with different settings.

CONCLUSION

Pain management and other aspects of symptomatic control appeared to be the primary focus of prescriptions for these patients. This study demonstrates significant interinstitutional variability in medication prescribing practices in outpatient cancer rehabilitation physiatry settings. Many factors could be contributing to interinstitutional variability, some of which may relate to site‐specific clinical and demographic factors affecting focus of practice, whereas other differences may be related to possible confounding elements due to the retrospective nature of this study.

DISCLOSURE

The authors report no conflict of interest.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.