The association between the problem list and chronic pain management

doi:10.21203/rs.3.rs-2252394/v1

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Research Article

The association between the problem list and chronic pain management

https://doi.org/10.21203/rs.3.rs-2252394/v1

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Background

Roughly a quarter of the US population suffers from moderate to severe chronic pain for at least six months in any given year. The complexity of managing chronic pain has encouraged providers to use innovative methods to address it. Research has shown that problem lists are potential tools that support the care of patients with diabetes and chronic kidney disease.

Objectives

To examine the extent to which the inclusion of chronic pain in a problem list is associated with follow-up specialty pain care.

Methods

The association between chronic pain documentation on the problem list and specialty pain care was investigated in this retrospective cohort study using 4,531 patient records.

Results

Chronic pain documentation in the problem list was associated with higher odds of receiving specialty pain care. The most common diagnosis was chronic pain (69.7%). A migraine diagnosis was associated with decreased odds of receiving specialty care, and chronic pain syndrome was associated with increased odds of receiving specialty care compared with the other chronic pain groups.

Conclusion

Documenting chronic pain on the problem list was associated with a higher likelihood of patients receiving specialty pain care.

Chronic pain management

problem list

EHR documentation

quality

chronic pain

The prevalence of chronic non-cancer pain is estimated to be approximately 20% in the United States [1]. Furthermore, chronic pain costs $635 billion a year for direct and indirect expenses, which is more than cancer ($243 billion), heart disease ($309 billion), or diabetes ($188 billion) [2]. Research indicates that chronic pain is associated with mental health issues, including anxiety and mood disorders [3]. Additionally, as a complex health condition, chronic pain negatively impacts sleep, cognitive processes such as memory and attention, cardiovascular health, and the overall quality of life [4].

Management of complex health conditions requires a multifaceted approach. For example, research has shown that early physical therapy in the treatment of chronic pain is associated with a lower probability of using opioids [5–7]. Furthermore, cognitive-behavioral therapeutic strategies can restructure and reframe a patient’s pain experience while simultaneously alleviating mood disorders and pain-associated disabilities [8]. Pain management specialists are trained to evaluate, diagnose, and treat various types of chronic pain [9]. Although pain specialists are trained to utilize a multimodal approach to address pain, patients typically need referrals from their primary care provider to receive treatment from specialty pain providers.

Problem lists have been developed to improve providers’ awareness of relevant health problems or symptoms and to assist in treatment decisions [10]. Keeping the problem list up to date with the patient's current health problems can improve communication between visits and increase evidence-based treatment for chronic conditions [11–14]. Unfortunately, the documentation of serious chronic conditions such as diabetes and heart failure are incomplete and only ranges from 10–50% [11, 13, 15]. It is not surprising that no research supports the benefits of documenting chronic pain in the problem list.

Prior research discovered that 54.4% of heart failure patients accurately had heart failure documented on their problem list [15]. Although a diagnosis may be reported elsewhere in a patient's record, having a diagnosis of heart failure on the problem list may allow for more efficient and complete care [10]. For example, heart failure on a patient's problem list was associated with a higher usage of medications demonstrated to benefit patients with systolic dysfunction. Additional studies found that chronic kidney disease was accurately documented in the problem list only 11–16% of the time [11, 13]. Furthermore, failure to include chronic kidney disease on the problem list was associated with providers ordering fewer chronic kidney disease-specific laboratory studies [11, 13]. In addition, the group without chronic kidney disease included in the Problem List was statistically less likely to have a nephrology visit than those whose problem list included chronic kidney disease [11]. Evidence suggests that using a problem list is associated with improved evidence-based care. Unfortunately, there is little information regarding the documentation of chronic pain in the problem list or the relationship between such documentation and patient care. Therefore, this study aimed to examine the extent to which chronic pain on a problem list is associated with the utilization of pain specialty care.

This observational study utilized a retrospective cohort design to examine electronic health record (EHR) data collected from a large Western healthcare network between 2017 and 2019. Owing to the limited follow-up data for most clinics in this health system, this study focused primarily on a subset of clinics in the Sacramento region of California. To control for possible differences between the clinics due to demographic or geographical differences, and because certain clinics had as few as one patient who qualified for the study, the authors grouped the 29 clinics that made up the study population's index visit into four clinic groupings based on the nearest reporting hospital of the clinics. The network practices use the same EHR, Cerner, which includes a problem list that allows for manual entry of patient problems based on Snowmed Clinical Terms. Clinicians can select the problem through a standardized drop-down list or search for the term and select the results. The healthcare system does not have a single pain management or chronic pain management policy across all healthcare centers. There is no automated clinical decision supports or alerts that utilize the problem list to facilitate pain treatment.

2.1 Study population

The study sample comprised 4,531 patients with chronic pain. The authors identified patients with at least one documented qualifying chronic pain diagnosis in the EHR within six-months before the study or throughout the study period, January 01, 2017, through December 31, 2019 (see Table 1). The authors selected these specific diagnoses based on prior literature that used EHR records to identify chronic pain patients [16–21]. Diagnoses were selected if used in two or more published reports from 2013 to 2019. The selection of diagnoses for this study was limited in scope and did not include a comprehensive list of other chronic diseases, with chronic pain being a prominent symptom. Although several diseases are associated with chronic pain, it is difficult to determine whether a patient visit is due to chronic pain or an issue associated with the disease itself. Therefore, we excluded patients whose medical records did not indicate chronic pain as the primary diagnosis.

Table 1

Chronic pain ICD-10 codes [16–21]
ICD-10		Description
G89.21		Other chronic pain
G89.22		Chronic pain due to trauma
G89.28		Chronic post-thoracotomy pain
G89.29		Other chronic postoperative pain
G89.4		Chronic pain syndrome
G43.109		Migraine with aura
G43.709		Chronic migraine without aura, not intractable, without status migrainosus
G43.719		Chronic migraine without aura, intractable without status migrainosus
G43.701		Chronic migraine without aura, not intractable, with status migrainosus
G43.711		Chronic migraine without aura, intractable with status migrainosus

Further inclusion criteria were patients aged 18 years or older. Since the goal of the study was to study how primary care providers document and treat chronic pain in an outpatient setting, the authors excluded patients with alternative goals to pain management such as “comfort care” in the context of palliative or hospice care. In addition, the study excluded patients with cancer, burns over 10% of the body, surgery within three months of the study, or pregnancy because the care they receive for their condition is typically from a specialist and not primary care.

2.2 Measures

The authors extracted patient data from the EHR records. First, we identified patients who had a qualifying diagnosis of chronic pain. Second, we determined whether the patients had chronic pain documented on their problem list during their first visit during the study period. Third, we followed the patients in time to figure out how chronic pain was treated with opioids or specialty pain treatment. For a full list of problems that qualify as chronic pain, see Table 2. To summarize, the authors included any problem with chronic and the word pain together in any format, or if a patient had a migraine on their problem list.

Table 2

Problem list entry and associated categorization
Problem	Category
Abdominal migraine	Migraine
Adenolymphoma	Adenolymphoma
Basilar migraine	Back Pain
Chronic Pain	Chronic pain
Chronic abdominal pain	Abdominal Pain
Chronic back pain	Back Pain
Chronic low back pain	Back Pain
Chronic pain	Chronic pain
Chronic pain control	Chronic pain
Chronic pain syndrome	Chronic pain
Chronic pelvic pain of female	Abdominal Pain
Classical migraine	Migraine
Common migraine	Migraine
Complicated migraine	Migraine
Generalized chronic body pains	Chronic pain
Herniation of rectum into vagina	Abdominal Pain
Menstrual migraine	Migraine
Migraine	Migraine
Migraine aura without headache	Migraine
Migraine variants	Migraine
Migraine with aura	Migraine
Migraine with typical aura	Migraine
Migraine without aura	Migraine
Ophthalmic migraine	Migraine
Ophthalmoplegic migraine	Migraine
Retinal migraine	Migraine
Unilateral mixed conductive and sensorineural hear	Migraine
Vertebrobasilar migraine	Migraine

Based on the chronic pain diagnosis that qualified the patient, the authors created a variable with the values other chronic pain, migraine, and chronic pain syndrome. Although the list of qualifying chronic pain diagnoses was more extensive than this list, the data indicated that only a handful of patients were diagnosed with chronic pain due to trauma, chronic post-thoracotomy pain, or other postoperative pain. These diagnoses alone were inadequate for statistical analysis. Therefore, we combined patients with these three diagnoses into the "other chronic pain" group.

From the patient records, the authors collected information regarding the patients’ race and ethnicity, coded as white non-Hispanic, black non-Hispanic, Hispanic of any race, and others. Sex was documented as male or female. Age was recorded as a continuous variable in the EHR. Research indicates that patients with chronic pain older than 60 years suffer for longer periods of time, have more comorbidities, and obtain chronic pain treatments more often [22, 23]. The authors analyzed age as a categorical variable of less than 60 and greater than or equal to 60. Insurance indicated whether a patient had publicly or privately funded insurance. The authors collected opioid prescription information and coded the variable as current prescription or non-current prescription.

The following measures were developed based on patient EHR data and were relevant only to patients who utilized pain specialty care. The authors measured and categorized the number of primary care provider visits patients had before they were seen by a pain specialist (one visit, two visits, three or more visits). The number of days it took patients to see a specialist from their last visit to their primary care provider was collected and measured as a continuous variable.

The outcome of interest was whether a patient utilized specialty pain care within 365 days of their index visit or not. Such utilization included visits with the primary diagnosis of chronic pain and care received from a behavioral therapist, pain specialist, or surgery. For statistical analysis, the outcome was measured as a dichotomous variable indicating whether the patient utilized specialty pain care regardless of the type of pain.

2.3 Statistical Analysis

Data were analyzed using Microsoft Excel and SAS University Edition. The first step in the analysis was to identify the patient cohort with chronic pain documented in their problem list (Table 1). Using this information, the authors calculated the proportion of patients with chronic pain in their problem list. The proportions were reported for the total sample and were stratified according to patient age, sex, race and ethnicity, and insurance status. The authors also reported the proportion of primary care provider visits before specialty care utilization, type of specialty visit, and days from the last primary care visit to the specialist.

Preliminary data analysis demonstrated minimal intraclass correlations within the different clinic groups. Therefore, there were no similarities between clinics within a cluster or group. Based on this result, the authors used clinic groups as covariates to adjust all models, rather than clustering. The authors fitted a logistic regression model to assess potential confounders and adjusted for them to examine the relationship between chronic pain documentation on the problem list and utilization of specialty care. The authors conducted unadjusted logistic regressions and then, using backward selection, an adjusted model. The final model included the covariates of age, sex, race, and clinic groups, while excluding insurance status and opioids. Although opioids met the significance criteria to remain in the model, the descriptive statistics showed a possible multicollinearity issue between opioids and specialty pain follow-up and were therefore excluded. All unadjusted and adjusted estimates are presented. This study was approved by our institutional review board.

The authors extracted the medical records of 4,531 patients with chronic pain. The sample characteristics are listed in Table 3. The sample was predominantly female (68.2%), with an average age of 56.7 (SD 15.8). The majority were white non-Hispanics (54.1%), followed by Hispanics (22.5%), other unclassified groups (17.5%), and black non-Hispanics (5.9%). The most common diagnosis of chronic pain was chronic pain (69.7%). The most common type of problem list entry was chronic pain (73.6%) (Table 4). Most participants utilized specialty pain care within 365 days of their index visit (87.1%), with pain specialists being the most common type of utilization (94.8%).

Table 3

Chronic pain patient characteristics
	Total Sample (n = 4,531)	Chronic Pain Not on Problem List (n = 791)	Chronic Pain on Problem List (n = 3,740)	χ2
Study Variables	M(SD) or %	%	%
Age	56.7 (15.8)			1.3
Under 60 years old	50.9%	52.7%	50.5%
Over 60 years old	49.2%	47.3%	49.6%
Gender				5.8*
Female	68.2%	64.6%	69.0%
Male	31.8%	35.4%	31.0%
Race/Ethnicity				28.1***
White Non-Hispanic	54.1%	48.2%	55.4%
Black Non-Hispanic	5.9%	4.7%	6.1%
Hispanic	22.5%	23.8%	22.3%
Other	17.5%	23.4%	16.3%
Specialty Pain Utilization				17.2***
No follow up	87.1%	91.5%	86.1%
Follow up	13.0%	8.5%	13.9%
Diagnosis				63.8***
Chronic pain syndrome	12.5%	4.7%	14.2%
Migraine	17.8%	23.3%	16.6%
Other chronic pain	69.7%	72.1%	69.2%
Insurance				1.8
Subsidized	44.5%	42.4%	45.0%
Private	55.5%	57.7%	55.0%
Opioid prescription				15.0***
No	98.1%	96.3%	98.4%
Yes	1.9%	3.7%	1.6%
Number of PCP visits before specialist (n = 621)
One	44.7%	-	-
Two	22.0%	-	-
Three or more	33.3%	-	-
Specialty Utilization (n = 621)
Behavioral	1.0%	-	-
Pain specialist	94.8%	-	-
Surgery	4.2%	-	-
Days from PCP to specialist visit (n = 621)	61.4 (64.6)	-	-
Notes. p < 0.05, p < 0.01, **p < 0.001; Reporting column percent

Table 4: Type of problem list entry

Problem Categorization	%
Abdominal pain	0.4%
Adenolymphoma	0.1%
Back pain	10.1%
Chronic pain	73.6%
Migraine	15.9%
n = 3740

Table 5: Logistic regression showing the factors associated with pain specialty care utilization

	Unadjusted		Adjusted
Variables	OR	95% CI	OR	95% CI
Chronic pain on problem list
Pain is not documented	Ref	Ref	Ref	Ref
Pain is documented	1.75***	1.34–2.28	1.57**	1.19–2.07
Age
Under 60 years old	Ref	Ref	Ref	Ref
Over 60 years old	1.03	0.87–1.23	0.88	0.73–1.05
Gender
Male	Ref	Ref	Ref	Ref
Female	1.12	0.93–1.35	1.24*	1.02–1.51
Race/Ethnicity
White Non-Hispanic	Ref	Ref	Ref	Ref
Black Non-Hispanic	0.97	0.67–1.40	1.25	0.83–1.89
Hispanic	0.93	0.75–1.16	0.95	0.75–1.19
Other	0.65**	0.50–0.85	0.64**	0.49–0.85
Diagnosis
Chronic pain syndrome	Ref	Ref	Ref	Ref
Migraine	0.33***	0.21–0.53	0.35***	0.22–0.56
Other chronic pain	1.69**	1.27–2.26	1.68**	1.25–2.27
Insurance
Subsidized	Ref	Ref	-	-
Private	0.97	0.81–1.15	-	-
Opioid prescription
Yes	Ref	Ref	-	-
No	0.15**	0.04–0.62	-	-
Notes. Adjusted model selected using backwards selection and controlling for clinic groupings; p < 0.05, p < 0.01, **p < 0.001

3.1 Cohort Characteristics

Of the 4,531 patient records, 3,740 (82.5%) had chronic pain documented on the problem list. Among female patients, 83.5% had chronic pain documented on their problem list, whereas 80.6% of male patients had chronic pain documented on their problem list. Documentation of chronic pain was more common among black non-Hispanics (86.0%) than among other racial groups (76.7%). Among patients who had chronic pain documented on their problem list, 13.9% had a follow-up pain specialist visit, compared to 86.1% with no follow-up. The most common chronic pain diagnoses among the patients with chronic pain documented on their problem list were other/unclassified pain (69.2%), migraine (16.6%), and chronic pain syndrome (14.2%). Among the patients who had chronic pain on their problem list, 98.4% did not have an opioid prescription and 1.6% had a prescription.

3.2 Factors Associated with a Follow-up Visit with Specialty Pain Care

After controlling for all confounders and clinic groups, the logistic regression found that chronic pain documented on the problem list was a significant predictor of specialty pain care utilization; pain documented on the problem list had 57% increased odds of receiving specialty pain care (odds ratio [OR] 1.57; 95% CI 1.19–2.07). Furthermore, females had increased odds of receiving specialty pain care compared to males (OR 1.24; 95% CI 1.02–1.51). Uncategorized racial and ethnic groups had decreased odds of receiving specialty compared to white non-Hispanics (OR 0.64; 95% CI 0.48–0.85). A migraine diagnosis was associated with decreased odds of utilizing specialty care compared with chronic pain syndrome diagnoses (OR 0.35; 95% CI 0.22–0.56). Additionally, having a non-specific diagnosis, codified as other chronic pain diagnosis, was associated with increased odds of receiving specialty pain care compared to a chronic pain syndrome diagnosis (OR 1.68; 95% CI 1.25–2.27).

This study aimed to explore the extent to which documenting chronic pain on a problem list was associated with the utilization of specialty pain care. To date, no study has examined the documentation of chronic pain in the problem list. However, compared to problem list studies examining other chronic diseases, this study has some similarities and differences. This study discovered that providers documented chronic pain in 82.5% of patients’ problem lists. These findings contrast with recent literature suggesting that chronic pain documentation rates are augmented in pain management training programs. In these instances, there was an increase in documenting chronic pain on the problem list from 0.5–2.4% [16]. Although the higher documentation rates found in this study are promising, it is essential to note that this was a single metropolitan area.

The majority of the sample was female although the rates of chronic pain documentation were similar in men and women. Compared to their male counterparts, females are more likely to utilize pain management options [24]. Therefore, higher utilization of pain management options might be associated with an increase in documentation of chronic pain on the problem list. Among patients diagnosed with chronic pain syndrome, 93.5% had chronic pain documented on their problem list, compared to 77.2% of patients diagnosed with migraine. Chronic pain syndrome is associated with psychological and physiological disability [25, 26]. The difference between a diagnosis of chronic pain syndrome and the non-specific other chronic pain diagnoses comes down to the psychological aspect of the pain experience [26]. The additional components of the psychological strain encompassed in the chronic pain syndrome diagnosis may reflect the rate at which providers document the problem. The increase in documentation may be due to several competing symptoms that increase the chance of documentation, rather than pain alone.

Other chronic pain was the most common diagnosis in this study. Similarly, the most frequently documented problem was chronic pain. Disease documentation and treatment are essential steps in the long-term management of chronic diseases. Experts recommend collecting information regarding the patient's pain history, location, severity, duration, and possible causes [27, 28]. However, primary care adherence to pain care standards, documentation, and practice is known to be inadequate [29]. The ambiguity behind diagnosing various issues as a single chronic pain diagnosis makes tracking patient care difficult. On the problem list, 10% of patients had back pain. The treatment of back pain versus chest pain, which was never listed as a problem, is vastly different. However, outside of back pain, there is no specificity for the patient's type of pain. Although guidelines provide the best practices for managing chronic pain through pharmacological or non-pharmacological therapies [30], clarity of the kind of chronic pain the patient may suffer is essential.

Recognition of heart failure and chronic kidney disease on the problem list was associated with more evidence-based management [11, 13, 15], These findings are consistent with those of our study, which showed that documenting chronic pain on the problem list was associated with a higher likelihood of utilizing specialty pain care. The relationship between documenting pain as a problem and receiving follow-up care might be related to the provider. One might postulate that a provider with more chronic pain training may explain the association between these findings. Further research will need to address provider differences and stratify whether the result of a complete problem list is due to the additional awareness of the actual problem or the provision of better comprehensive care in general.

After controlling for all other covariates, including clinic clusters, sex was found to be significantly associated with the utilization of pain specialty care. As stated previously, a 3% difference in the rates of problem list completion between males and females is statistically significant, but minor differences in a large sample may show up as significant and the clinic significance is questionable [31]. The other unclassified chronic pain group had increased utilization of specialty pain care. Migraine sufferers had decreased odds of using specialty pain care. This result is expected since migraine sufferers have clear established pain management guidelines, and specialty care is often unnecessary [32–34].

A limitation of this study was the absence of provider characteristics or information. The association between problem list completion and treatment follow-up may be due to unmeasured provider differences. Research has shown that a provider's age can impact the type of care a person receives [35]. However, there is no literature exploring the variation between provider-to-patient demographic differences. Furthermore, there is the possibility that the better the physician, the more likely they are to document the problem on the problem list and recommend the patient to specialty care. The difference could be due to this confounding factor and not the documentation of chronic pain.

Another limitation is that the healthcare network is an open network system. Therefore, if a patient sees a provider and obtains a referral for a pain specialist, we cannot determine whether the patient saw a pain specialist outside of the network. It is entirely possible that a small portion of patients with chronic pain had a follow-up with a pain specialty clinic outside the hospital network and, therefore, were tracked as not receiving care under this dataset. Owing to the nature of the retrospective review of EHR data, the data are only as reliable and clean as the providers put into the system. Psychosocial factors, such as substance use, are often less complete than other required fields, limiting researchers’ ability to control these factors.

A strength of this study was the inclusion of a large number of clinics and patients. Although these clinics are all in one healthcare network, individual primary care clinics have their own pain management policies and practices. Such variability among these clinics increases their generalizability to other clinics within the California Metropolitan Area. Furthermore, this study is an essential first step toward value-based care. Value-based programs reward healthcare providers with incentives to provide quality care to patients with Medicare [30, 36]. This patient-centered care model prioritizes patient health outcomes [36]. Value-based care focuses on high-value care such as preventative strategies rather than first-line defense such as opioids [37]. Research such as this study helps identify new pathways to provide the best care to patients utilizing our current resources.

4.1 Conclusion

Chronic pain is complex and requires a multifaceted approach to manage it. Often, a lack of clarity in the documentation of chronic pain may lead to unclear treatment options. However, utilizing EHR as a supportive tool in the pain management process may reduce such ambiguity. This is the first study to discover an association between chronic pain documentation on a problem list and patient follow-up with specialty pain care. Further research is required to explore these findings across different health systems and locations. This study may inform future research looking at automating problem list entries based on the relevant problems that the research supports.

EHR

Electronic Health Record

Ethical Approval and Consent to Participate: This research was conducted retrospectively from data obtained for clinical purposes. We consulted extensively with the IRB of Dignity Health and UC Davis Health who approved this study. The IRB waived consent of patients as this research resents no more than minimal risk of harm to subjects.

Consent for publication: The authors certify the accuracy of the content given to the journal. The lead author/corresponding author ensures that all the co-authors have agreed to the contents and will be notified when the manuscript is accepted. The corresponding author is answerable to all inquiries on behalf of the co-authors. All authors have seen and approved the final version of the paper and are aware of the submission of the paper.

Availability of supporting data: The data that support the findings of this study are available from the corresponding author, TR, upon reasonable request.

Competing Interests:

All authors have no conflicts of interest.

Funding:

This work was supported by Gordon and Betty Moore Foundation [#4294]

The funding source funded the educational program but had no influence on the study.

Authors’ Contributions:

All authors contributed to the design of the study. TR conducted all statistical procedures with advisement from CD. TR wrote the manuscript, and all authors reviewed the manuscript.

Acknowledgments:

The authors would like to express their gratitude to the participating site research advisors and data analysts, including Lesly Kelly and Alan Borchardt, for their assistance.

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No competing interests reported.

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Editorial decision: Major revision
29 Nov, 2022
Reviews received at journal
25 Nov, 2022
Reviewers agreed at journal
15 Nov, 2022
Reviewers invited by journal
14 Nov, 2022
Editor assigned by journal
10 Nov, 2022
Submission checks completed at journal
09 Nov, 2022
First submitted to journal
08 Nov, 2022

You are reading this latest preprint version

The association between the problem list and chronic pain management

Status:

Version 1

Abstract

Background

Objectives

Methods

Results

Conclusion

1. Introduction

2. Methods

2.1 Study population

2.2 Measures

2.3 Statistical Analysis

3. Results

3.1 Cohort Characteristics

3.2 Factors Associated with a Follow-up Visit with Specialty Pain Care

4. Discussion

4.1 Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1