nach oben

Erschienen in:

Open Access 01.12.2024 | Research

Impact of patient education by nurse case managers on decision making for out-of-pocket anti-osteoporotic pharmaceutical therapy: a single-center retrospective study

verfasst von: Ya-Lian Deng, Hsu-Tung Lee, Shih-Yi Lin, Tan-Hsiu Liao, Chia-Tien Hsu

Erschienen in: BMC Nursing | Ausgabe 1/2024

Abstract

Background

Osteoporosis is a common condition that increases the risk of fracture and mortality. In Taiwan, clinical guidelines recommend pharmaceutical therapy for patients with a T-score of ≤ − 2.5; however, Taiwan’s National Health Insurance (NHI) only covers these medications for those with a history of fragility fractures. This gap in coverage necessitates a discussion of out-of-pocket treatment options. To address this, we provided an integrated care program with patient education and shared decision-making by nurse case managers specializing in osteoporosis. We evaluated whether education by nurse case managers influences patients with a T-score ≤ − 2.5, who are not covered by the NHI, to choose out-of-pocket pharmaceutical therapy.

Methods

We retrospectively reviewed medical records of patients who underwent bone density scanning at our hospital between January 2014 and December 2021. We identified 4,462 patients with a T-score of ≤ − 2.5 who were ineligible for NHI-covered anti-osteoporotic therapy and analyzed trends in out-of-pocket medication use. Since the integrated care program began in 2018, we evaluated whether education by nurse case managers between 2018 and 2021 influenced patients’ decisions to pay out-of-pocket for therapy. After the implementation of the integrated care program, we identified 2,910 patients with a T-score ≤ -2.5 who were ineligible for NHI-covered anti-osteoporotic therapy. Of these, 640 opted for out-of-pocket treatment, while 2,270 chose conservative care. After a 1:1 propensity score match based on age and sex, logistic regression was used to analyze the impact of nurse case manager education on these decisions.

Results

Between 2014 and 2021, 888 of the 4,462 patients chose out-of-pocket pharmaceutical therapy. Before the implementation of the integrated care program and patient education by nurse case managers (2014–2017), 16% of the patients opted to pay out-of-pocket for anti-osteoporotic therapy. After the program was implemented (2018–2021), the rate increased significantly to 22% (P < 0.001). A multivariate logistic regression model showed that a history of osteoarthritis (adjusted odds ratio = 1.576; P = 0.009) and education provided by the nurse case managers (adjusted odds ratio = 5.044; P < 0.001) were significantly associated with choosing out-of-pocket therapy.

Conclusions

Education from nurse case managers significantly increased the likelihood of patients choosing out-of-pocket anti-osteoporotic therapy in our hospital, thereby bridging the gap between clinical guidelines and NHI reimbursement criteria.

Supplementary Material, Additional File 1: File format: PDF. Title of data: English translation of the patient decision aids. Description of data: English translation of the patient decision aids

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12912-024-02467-x.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Background

Osteoporosis is a prevalent condition worldwide, leading to higher fracture risk, reduced quality of life, and increased mortality [1]. In Taiwan, the prevalence of osteoporosis has increased rapidly owing to the aging population. According to the National Health Insurance (NHI) data, the rate among individuals aged ≥ 50 years increased from 17.4% in 2001 to 25.0% in 2011 [2]. This condition significantly increases the risk of fractures, particularly the spine and hips. Taiwan ranks ninth globally and has the highest rate of hip fractures in the Asia-Pacific region [3]. In 1999, the one-year mortality rate of older individuals with hip fractures was approximately 15% in women (compared with a standard mortality rate of 13%) and 22% in men (compared with a standard mortality rate of 14%). By 2009, these rates had decreased to 11.2% for women and 18% for men. Additionally, the standard mortality rate had declined to 2.8% for women and 3.6% for men by 2010. Despite these improvements, hip fractures continued to have a significant impact on mortality in 2010 [4]. According to several nationwide analyses in Taiwan [5‐10], treating osteoporosis after fractures could lower the overall mortality rates.

Dual-energy X-ray absorptiometry (DXA) is widely recognized as the gold standard for measuring bone mineral density (BMD) [11]. In Taiwan, clinical guidelines [12] recommend that patients with a T-score of ≤ − 2.5 (indicative of osteoporosis) receive osteoporosis treatment along with education on lifestyle modification, fracture prevention, and fall prevention. Osteoporosis treatment includes both non-pharmacological management and anti-osteoporotic pharmaceutical therapies. The goal of anti-osteoporotic pharmaceutical therapy is to prevent fractures by increasing bone strength and improving bone quality [13]. Treatment decisions should be individualized, considering patient preferences, affordability, comorbidities, quality of life, and life expectancy [14]. However, Taiwan’s NHI does not cover anti-osteoporotic pharmaceutical therapy for these patients, unless they have had a fragility fracture [12]. This discrepancy requires discussions about out-of-pocket treatment options in clinical settings, especially for patients at high risk of fractures.

Several studies have highlighted the importance of nurse case managers in improving osteoporosis care and enhancing the patients’ experiences [15‐22]. However, most of these studies focused on patients with a history of fragility fractures and did not address patients with osteoporosis who did not experience fragility fractures. To provide comprehensive osteoporosis care, our hospital established an Osteoporosis Prevention and Treatment Center in 2017. Initially, the primary goal of the center was to provide fracture liaison services (FLS) to patients with a history of fragility fractures [23, 24]. However, we later expanded our services to include patients with osteoporosis who have not yet experienced fractures but are still at high risk. Primary preventive care was offered to these patients. These include fracture risk assessments, fall prevention strategies, shared decision-making (SDM) for prevention and treatment options, and ongoing medication management. Osteoporosis-qualified nurse case managers play crucial roles in patient education, providing cost information, and managing follow-ups. In 2018, our Osteoporosis Prevention and Treatment Center officially implemented this integrated care program. This study aimed to evaluate whether such education by nurse case managers influences patients with a T-score ≤ − 2.5, who are ineligible for NHI coverage, in choosing out-of-pocket anti-osteoporotic pharmaceutical therapy.

Methods

Study design and enrolled participants

This study was a single-center, retrospective analysis conducted at the Taichung Veterans General Hospital in Taiwan. Inclusion criteria of this study encompassed all patients who underwent BMD examinations at our hospital with a T-score of ≤ − 2.5, but who were not eligible for NHI-covered anti-osteoporotic pharmaceutical therapy. Figure 1 shows the flow diagram of our study. To study the trend of out-of-pocket anti-osteoporotic pharmaceutical therapy over time (Fig. 1a), we retrospectively reviewed the electronic medical records of patients who underwent BMD examinations at our hospital between January 1, 2014 and December 31, 2021. We identified 4,462 patients with a T-score of ≤ − 2.5 who were not eligible for NHI-covered anti-osteoporotic pharmaceutical therapy and analyzed trends in the use of out-of-pocket medications.

Since the Osteoporosis Prevention and Treatment Center officially started offering an integrated care program in 2018, which included patient education and SDM facilitated by osteoporosis-qualified nurse case managers, we also examined whether patient education provided by nurse case managers between 2018 and 2021 influenced patients’ decisions to choose out-of-pocket anti-osteoporotic pharmaceutical therapy (Fig. 1b). During this period, there were 5,814 patients with a T-score of ≤ − 2.5. Based on the data reported by our hospital to the Taiwan NHI Administration, we identified 2,904 patients with a T-score of ≤ − 2.5 who received NHI-covered anti-osteoporotic pharmaceutical therapy. After excluding these 2,904 patients, we identified an additional 2,910 patients with a T-score of ≤ − 2.5 who were not eligible for NHI-covered anti-osteoporotic pharmaceutical therapy. Among these 2,910 patients, 640 received out-of-pocket anti-osteoporotic pharmaceutical therapy, while 2,270 opted for conservative treatment. Demographic data, comorbidities, laboratory tests, results of BMD scans, and FRAX^® (Fracture Risk Assessment Tool for the ten-year probability of fracture) [25‐28] scores were then traced retrospectively from the electronic health records. After a 1:1 propensity score match based on age and sex, logistic regression was used to analyze the impact of nurse case manager education on these decisions. This study was approved by the Institutional Review Board of Taichung Veterans General Hospital (TCVGH-IRB No. CE22167A; Date of Approval: April 18, 2022), following the relevant institutional guidelines and regulations. Informed consent from the patients was waived because the study involved a retrospective analysis of the data.

Integrated care program and patient education by nurse case managers

Initially, the primary goal of our Osteoporosis Prevention and Treatment Center was to provide FLS to patients with a history of fragility fractures. Fracture liaison services is a coordinator-based, multidisciplinary approach to prevent secondary fractures, involving a team of specialists, including orthopedic surgeons, neurosurgeons, rheumatologists, neurologists, pulmonologists, endocrinologists, rehabilitation physicians, nephrologists, gynecologists, geriatricians, family medicine physicians, breast surgeons, urologists, and traditional medicine physicians, as well as osteoporosis-qualified nurse case managers, pharmacists, rehabilitation therapists, and dietitians in our hospital. We later expanded our services to include patients with osteoporosis who have not yet experienced fractures, but are still at high risk. Primary preventive care was offered to these patients. These include fracture risk assessments, fall prevention strategies, SDM for prevention and treatment options, and ongoing medication management. By integrating these services with the FLS, we provided comprehensive care through our osteoporosis integrated care program.

We incorporated a computerized physician order entry system [29] to support the integrated care program by streamlining referrals and tests. This module includes orders for laboratory tests, DXA examinations, and referral forms for patient education provided by the nurse case managers. Laboratory tests included the assessment of renal function, calcium, phosphorus, albumin, alkaline phosphatase, intact parathyroid hormone, and other endocrine profiles associated with secondary osteoporosis. In outpatient clinics, physicians provide initial education to patients and discuss the treatment options. They then issued a referral form to the osteoporosis-qualified nurse case manager, who would further educate the patient about osteoporosis, including an overview of the disease, fracture risk assessment, fall prevention, and prevention and treatment options through SDM.

The patient education provided by nurse case managers takes place after each doctor’s visit. As a result, follow-up education sessions are typically conducted every three to six months. During these sessions, nurse case managers monitor patients’ adherence to their prescribed medications. The follow-up education sessions ensure that patients fully understand the information from previous sessions, with a particular focus on fall and fracture prevention. Additionally, important details about medication use are reinforced. For instance, after administering bisphosphonates or denosumab, patients should be monitored for symptoms of osteonecrosis of the jaw. Patients receiving denosumab should also be observed for symptoms of hypocalcemia, especially those with impaired kidney function. Figure 2 shows the number of patients who received education from nurse case managers between 2018 and 2021. During this period, a total of 3,057 patients participated in the education program. Of these, 2,628 patients were undergoing NHI-covered anti-osteoporotic pharmaceutical therapy. Additionally, 429 patients with a T-score of ≤ − 2.5 who were not eligible for NHI-covered therapy also received education from nurse case managers. Among these 429 patients, 205 later opted for out-of-pocket pharmaceutical therapy.

We used a patient decision aid (PDA) to facilitate SDM [30, 31] (the English translation of the PDA is presented in Supplementary Material, Additional File 1). The PDA was developed according to the International Patient Decision Aid Standards [32‐34]. The development process involved several key steps, including a user needs assessment, structural planning, retrieving, integrating, and translating data based on evidence-based medicine, drafting the initial PDA, conducting preliminary testing (alpha testing), and engaging in iterative discussions and revisions with patients and experts. This was followed by real-world testing (beta testing), which ultimately led to the creation of the final version of the PDA. The nurse case managers also provided information on out-of-pocket costs. We also established an osteoporosis case management information system that nurse case managers can use to track patients’ follow-up appointments. The system identifies patients who have missed scheduled appointments to help ensure adherence to prescribed medications. Additionally, case managers provide telephone counseling to improve patient compliance. When a patient misses a follow-up, our nurse case managers will contact them to understand the reason for their absence, assist with rescheduling the appointment, and educate them on the importance of medication adherence for treatment. Nurse case managers also handle incoming phone inquiries from patients or their families, but they only proactively reach out to patients when the system flags a missed appointment for clinic follow-up and medication collection. In 2018, the Osteoporosis Prevention and Treatment Center officially implemented this integrated care program.

Outcome analysis and variable definition

Primary objective of this study was to evaluate whether patient education by nurse case managers influences patients with a T-score ≤ − 2.5, who are ineligible for NHI coverage, to choose out-of-pocket anti-osteoporotic pharmaceutical therapy. Bone mineral density measurements of the lumbar spine (L1–L4) and bilateral femoral necks were obtained through DXA using the Lunar Prodigy (General Electric, Fairfield, CT, USA), with results reported in g/cm². The least significant change was ± 0.010 g/cm² for the lumbar spine (L1–L4) and ± 0.012 g/cm² for the femoral neck. T-scores were computed using the manufacturer’s reference data. Estimated glomerular filtration rate (eGFR) was calculated using the 2021 Chronic Kidney Disease Epidemiology Collaboration creatinine Eqs. [35, 36]. The following formula was used to estimate corrected calcium levels in the serum [37]: corrected calcium (mg/dL) = serum calcium (mg/dL) + 0.8 × (4 − serum albumin [g/dL]). Fracture Risk Assessment Tool (FRAX^®) is promoted by the International Osteoporosis Foundation and World Health Organization to help patients and medical staff estimate the 10-year risk of major osteoporotic fractures and hip fractures. This estimation is based on clinical data and medical history with or without BMD measurements. The 10-year fracture risk is calculated using the Taiwan-specific FRAX^® calculator in our study [28, 38, 39]. Patients were considered at moderate risk if the probability of a major osteoporotic fracture was > 10%, or a hip fracture was > 1.5%. High risk is defined as a major osteoporotic fracture probability of > 20% or a hip fracture probability of > 3% [25, 40].

Statistical analysis

Continuous variables with normal distribution are shown as mean ± standard deviation, whereas continuous variables with non-normal distribution are presented as median (interquartile range). Categorical variables are reported as numbers (percentages). Normality was assessed using the Kolmogorov–Smirnov test. Tests for statistical significance were conducted using the Mann–Whitney U test for continuous variables and the chi-square test or Fisher’s exact test for categorical variables. We used a logistic regression model to analyze factors associated with a patient’s decision regarding out-of-pocket pharmaceutical therapy. Variables in the univariate analysis a P < 0.05 were considered potential confounders in the multivariate models. Multivariate analysis was performed to adjust for the confounders selected in the univariate analysis. The level of significance was set at a two-tailed value of P < 0.05. Statistical analyses were performed using MedCalc for Windows, version 22.303 (MedCalc Software; Ostend, Belgium). GraphPad Prism 9.5.0 software (GraphPad Software, La Jolla, CA, USA) was used to create bar and line graphs.

Results

Trend of out-of-pocket anti-osteoporotic pharmaceutical therapy

A total of 27,445 bone mineral density scans were performed at our hospital between January 1, 2014 and December 31, 2021. Of these, 9,503 patients were diagnosed with osteoporosis based on a T-score of ≤ − 2.5. Among patients with a T-score of ≤ − 2.5, 5,041 also had fragility fractures and subsequently received NHI-covered anti-osteoporotic pharmaceutical therapy. However, there were 4,462 patients with a T-score of ≤ − 2.5 who were not eligible for NHI-covered anti-osteoporotic pharmaceutical therapy. Of these 4,462 patients, 888 chose to pay out-of-pocket for anti-osteoporotic pharmaceutical therapy, whereas 3,574 opted for conservative treatment.

Figure 3 shows the number and percentage of patients who chose to pay out-of-pocket for anti-osteoporotic pharmaceutical therapy among the patients with a T-score of ≤ − 2.5 who were not eligible for NHI-covered anti-osteoporotic pharmaceutical therapy. Before the implementation of the integrated care program and patient education by nurse case managers (2014–2017), 16% of the patients chose to pay out-of-pocket for anti-osteoporotic pharmaceutical therapy. After the implementation of the integrated care program and patient education by nurse case managers (2018–2021), this rate increased significantly to 22% (from 16 to 22%; 248 of 1,552 vs. 640 of 2,910; P < 0.001). The number and rate of patients who chose to pay out-of-pocket for anti-osteoporotic pharmaceutical therapy among the patients with a T-score of ≤ − 2.5 who were not eligible for NHI-covered anti-osteoporotic pharmaceutical therapy was lower in 2021 than that before it, likely because of the impact of the coronavirus disease outbreak in Taiwan.

Figure 4 shows the number of patients who chose different types of out-of-pocket anti-osteoporotic medication over the years. Denosumab is the most commonly used out-of-pocket anti-osteoporotic medication. Selective estrogen receptor modulators are the second most popular choice, followed by bisphosphonates.

Factors influencing a patient’s decision on out-of-pocket pharmaceutical therapy

Table 1 compares the clinical characteristics of the out-of-pocket pharmaceutical therapy group (n = 640) and the conservative treatment group (n = 640) after a 1:1 propensity score match based on age and sex. There were no significant differences between the two groups in terms of age, sex, weight, body mass index, history of rheumatoid arthritis, history of hypertension, history of cerebrovascular accident, eGFR, serum creatinine, serum albumin, white blood cell count, hemoglobin, corrected calcium, phosphorus, intact parathyroid hormone, left femoral neck BMD, right femoral neck BMD, left femoral neck T-score, or right femoral neck T-score. However, the out-of-pocket pharmaceutical therapy group had a significantly greater height (155.2 vs. 154.5 cm; P = 0.020), higher proportion of patients with a history of osteoarthritis (59.8% vs. 51.6%; P = 0.003), lower proportion of patients with a history of diabetes mellitus (17.8% vs. 22.5%; P = 0.037), lower alkaline phosphatase levels (91.0 vs. 93.0 mg/dL; P < 0.001), lower lumbar spine BMD (0.845 vs. 0.867 g/cm²; P < 0.001), lower lumbar spine T-scores (− 2.7 vs. −2.5; P < 0.001), higher ten-year major osteoporotic fracture risk (16.2% vs. 15.3%; P = 0.035), and higher ten-year hip fracture risk (6.7% vs. 6.3%; P = 0.028). Additionally, a significantly higher proportion of patients in the out-of-pocket pharmaceutical therapy group received patient education from nurse case managers than those in the conservative treatment group (32.0% vs. 8.1%; P < 0.001).

Table 1

Comparison of clinical characteristics between out-of-pocket pharmaceutical therapy and conservative treatment

Clinical characteristics	Conservative treatment (n = 640)		Out-of-pocket treatment (n = 640)		P value
Age (years)	66.0	(59.0–74.0)	65.0	(59.0–71.0)	0.183
Male sex	50	(7.8%)	50	(7.8%)	1.000
Height (cm)	154.5	(150.4–158.3)	155.2	(151.7–159.9)	0.020*
Weight (kg)	54.1	(48.3–61.1)	55.0	(49.9–60.9)	0.179
Body mass index(kg/m²)	22.8	(20.5–25.4)	22.8	(20.5–25.2)	0.925
History of OA	330	(51.6%)	383	(59.8%)	0.003**
History of RA	75	(11.7%)	72	(11.3%)	0.793
History of DM	144	(22.5%)	114	(17.8%)	0.037*
History of HTN	173	(27.0%)	172	(26.9%)	0.950
History of CVA	63	(9.8%)	49	(7.7%)	0.166
eGFR (mL/min/1.73 m²)	85.8	(66.5–97.5)	88.5	(72.1–97.5)	0.103
Serum creatinine (mg/dL)	0.77	(0.67–0.93)	0.75	(0.66–0.89)	0.086
Serum albumin (g/dL)	4.3	(4.0–4.5)	4.3	(4.1–4.5)	0.329
ALK-p (mg/dL)	93.0	(76.0–114.0)	91.0	(71.0–118.0)	< 0.001**
Corrected calcium (mg/dL)	9.10	(8.90–9.48)	9.20	(8.90–9.50)	0.579
Phosphorus (mg/dL)	3.6	(3.3–4.1)	3.6	(3.3–3.9)	0.356
i-PTH (pg/mL)	60.4	(42.0–99.8)	59.4	(40.5–87.9)	0.334
WBC count (10⁹/L)	6210	(4810–7870)	5980	(4910–7700)	0.481
Hemoglobin (g/dL)	12.7	(11.3–13.6)	12.7	(11.4–13.7)	0.534
Lumbar spine BMD (g/cm²)	0.867	(0.803–0.958)	0.845	(0.778–917)	< 0.001**
Left femoral neck BMD (g/cm²)	0.679	(0.632–0.716)	0.671	(0.619–0.712)	0.089
Right femoral neck BMD (g/cm²)	0.670	(0.630–0.709)	0.671	(0.619–0.713)	0.721
Lumbar spine T-score	−2.5	(− 3.1 – −1.8)	−2.7	(− 3.3 – −2.1)	< 0.001**
Left femoral neck T-score	−2.6	(− 2.9 – −2.3)	−2.6	(− 3.0 – −2.3)	0.081
Right femoral neck T-score	−2.6	(− 2.9 – −2.4)	−2.6	(− 3.0 – −2.3)	0.616
FRAX^® (MOF)	15.3	(11.0–20.6)	16.2	(11.4–22.4)	0.035*
FRAX^® (hip)	6.3	(3.7–9.4)	6.7	(4.1–10.6)	0.028*
PE by Nurse Case Manager	52	(8.1%)	205	(32.0%)	< 0.001**

For continuous variables, the independent-samples t-test was used if the data were normally distributed, and if not, the Mann–Whitney U test was used. Categorical data were compared using the chi-square test. Values are expressed as number (percentage) or median (interquartile range). *p < 0.05, **p < 0.01. OA, osteoarthritis; RA, rheumatoid arthritis; DM, diabetes mellitus; HTN, hypertension; CVA, cerebrovascular accident; eGFR, estimated glomerular filtration rate calculated using the 2021 Chronic Kidney Disease Epidemiology Collaboration creatinine equation; ALK-p, alkaline phosphatase; i-PTH, intact parathyroid hormone; WBC, white blood cell; BMD, bone mineral density; MOF, major osteoporotic fracture; PE, patient education

Table 2 summarizes the results of the logistic regression analysis of the patients’ decision to opt for out-of-pocket pharmaceutical therapy. In the univariate regression, a high height (crude odds ratio [cOR] = 1.02; P = 0.014), history of osteoarthritis (cOR = 1.40; P = 0.003), high eGFR (cOR = 1.01; P = 0.049), higher serum albumin levels (cOR = 1.87; P < 0.001), lumbar spine T-score ≤ − 2.5 (cOR = 1.08; P < 0.001), high risk of major osteoporotic fracture (cOR = 1.28; P = 0.049), and patient education by nurse case managers (cOR = 5.33; P < 0.001) were associated with patient’s decision to choose out-of-pocket pharmaceutical therapy. Conversely, patients with a history of diabetes mellitus (cOR = 0.75; P = 0.037), serum albumin levels < 3.5 (cOR = 0.38; P = 0.002), or higher lumbar spine BMD (cOR = 0.19; P < 0.001) were less likely to choose out-of-pocket anti-osteoporotic pharmaceutical therapy. The multivariate regression model indicated that a history of osteoarthritis (adjusted odds ratio [aOR] = 1.53; P = 0.013) and patient education by nurse case managers (aOR = 5.13; P < 0.001) were significantly associated with patients’ decision to opt for out-of-pocket pharmaceutical therapy.

Table 2

Factors influencing a patient’s decision on out-of-pocket pharmaceutical therapy

	Univariate			Multivariate
	cOR	95% CI	P value	aOR	95% CI	P value
Age > 65	0.93	(0.75 − 1.16)	0.536
Male sex	1.00	(0.67–1.50)	1.000
Height (cm)	1.02	(1.01–1.04)	0.014*	1.02	(0.99–1.04)	0.159
Weight (kg)	1.01	(0.99–1.02)	0.329
Body mass index < 18.5 kg/m²	0.72	(0.47–1.11)	0.135
History of OA	1.40	(1.22–1.75)	0.003**	1.53	(1.09–2.15)	0.013*
History of RA	0.96	(0.68–1.35)	0.793
History of DM	0.75	(0.57–0.98)	0.037*	0.72	(0.48–1.07)	0.106
History of HTN	0.99	(0.78–1.27)	0.950
History of CVA	0.76	(0.51–1.12)	0.167
eGFR (mL/min/1.73 m²)	1.01	(1.00–1.01)	0.049*	1.00	(0.99–1.01)	0.838
Serum creatinine (mg/dL)	0.98	(0.92–1.05)	0.532
Serum albumin (g/dL)	1.87	(1.35–2.61)	< 0.001**	1.27	(0.69–2.35)	0.445
Serum albumin < 3.5	0.38	(0.21–0.69)	0.002**	0.59	(0.18–1.89)	0.373
ALK-p (mg/dL)	1.00	(0.99–1.00)	0.076
Corrected calcium (mg/dL)	1.07	(0.85–1.35)	0.578
Phosphorus (mg/dL)	0.88	(0.72–1.08)	0.220
i-PTH (pg/mL)	1.00	(0.99–1.00)	0.190
WBC count(10⁹/L)	1.00	(0.99–1.00)	0.188
Hemoglobin (g/dL)	1.05	(0.96–1.14)	0.301
Lumbar spine BMD (g/cm²)	0.19	(0.08–0.44)	< 0.001**	0.25	(0.04–1.67)	0.151
Left femoral neck BMD (g/cm²)	0.34	(0.08–1.39)	0.132
Right femoral neck BMD (g/cm²)	0.70	(0.17–2.87)	0.619
Lumbar spine T-score ≤ − 2.5	1.62	(1.28–2.05)	< 0.001**	1.13	(0.69–1.85)	0.620
Left femoral neck T-score ≤ − 2.5	1.08	(0.85–1.37)	0.532
Right femoral neck T-score ≤ − 2.5	0.89	(0.70–1.13)	0.331
FRAX^® (MOF)	1.01	(0.99–1.02)	0.126
Low risk	0.92	(0.68–1.23)	0.551
Moderate risk	0.86	(0.68–1.07)	0.178
High risk	1.28	(1.00–1.65)	0.049*	1.40	(0.96–2.05)	0.083
FRAX^® (hip)	1.01	(0.99–1.03)	0.123
Low risk	1.24	(0.67–2.28)	0.497
Moderate risk	0.73	(0.52–1.01)	0.061
High risk	1.24	(0.91–1.67)	0.169
PE by Nurse Case Manager	5.33	(3.84–7.40)	< 0.001**	5.13	(3.38–7.78)	< 0.001**

*p < 0.05; **p < 0.01. Logistic regression.

cOR, Crude odds ratio; aOR, Adjusted odds ratio; CI, confidence interval; OA, osteoarthritis; RA, rheumatoid arthritis; DM, diabetes mellitus; HTN, hypertension; CVA, cerebrovascular accident; eGFR, estimated glomerular filtration rate; ALK-p, alkaline phosphatase; i-PTH, intact parathyroid hormone; WBC, white blood cell; BMD, bone mineral density; MOF, major osteoporotic fracture; PE, patient education

Discussion

The results of our study showed that after the implementation of an integrated care program in 2018, there was a significant increase in the number of patients with a T-score of ≤ − 2.5 who are ineligible for the NHI coverage, opting for out-of-pocket anti-osteoporotic pharmaceutical therapy, with the percentage increasing from 16 to 22%. Denosumab is the most commonly used out-of-pocket anti-osteoporotic medication. This might be because denosumab is administered as a subcutaneous injection every six months, which eliminates the need for daily oral medication or intravenous infusions. Furthermore, pharmacokinetic studies have shown that denosumab does not require dose adjustments in patients with chronic kidney disease, including those undergoing hemodialysis, making it an appropriate option for individuals with impaired kidney function. A history of osteoarthritis and patient education provided by nurse case managers were the main factors influencing the decision to choose out-of-pocket treatment. In our cohort, osteoarthritis was the most common comorbidity in patients with osteoporosis [41]. As a prevalent musculoskeletal disorder, patients are more likely to be concerned about their osteoporotic status. Table 1 shows that the majority of our study participants have a ten-year hip fracture probability more than 3%, with most FRAX^® (hip) scores exceeding this threshold. This suggests that our participants were at high risk for fragility fractures. Patients who received education from nurse case managers were more than five times more likely to choose out-of-pocket anti-osteoporotic pharmaceutical therapy than those who did not receive such education. This suggests that targeted patient education and SDM facilitated by nurse case managers play a crucial role in encouraging high-risk patients with osteoporosis to pursue effective treatment options despite the lack of insurance coverage.

Fracture liaison services is a multidisciplinary approach designed to prevent secondary fractures. This program typically involves a coordinator that helps manage patient care through a team of specialists. Meta-analyses have shown that FLS improves patient compliance, increases the rates of BMD examinations and treatment, and generally improves patient outcomes. Notably, FLS is associated with reduced refracture rates and mortality [23, 24]. The FLS programs have also been shown to improve osteoporosis management, decrease fall incidents, and decrease mortality and refracture rates in Taiwan [42‐44]. Chang et al. [43] evaluated the effectiveness of an osteoporosis liaison service (OLS) program within a healthcare system over a one-year period. The OLS program consisted of two components: Medication Management Services (MMS) to enhance medication adherence and FLS for the prevention of secondary fractures. The MMS program included patients who had issues with osteoporosis medication, although they did not necessarily have fragility fractures. The study found that the OLS program improved osteoporosis care by increasing medication adherence and reducing falls. Many studies have reported that the coordinators or case managers of these programs are often nurses.

Several studies have highlighted the importance of nurse case managers and practitioners in improving osteoporosis care and enhancing the patients’ experiences [15‐22]. For example, Wozniak et al. [15] conducted a randomized controlled trial to explore patients’ experiences with nurse case-managed osteoporosis care. The study found that patients perceived this care as acceptable, accessible, and appropriate. It included both patients with previous fractures and those with low bone density but no history of fractures. Majumdar et al. [16] conducted a randomized controlled pilot trial to compare the effectiveness of a nurse case manager approach to a multifaceted intervention in enhancing osteoporosis care for patients with wrist fractures. They found that a nurse case manager was more effective than a multifaceted intervention in increasing the rate of appropriate osteoporosis care among high-risk patients following a wrist fracture. Similarly, Seuffert et al. [17] evaluated whether an educational intervention led by a nurse practitioner could improve treatment adherence in patients with low BMD in orthopedic practice. Their findings showed that nurse practitioner-led educational interventions improved the initiation of calcium, vitamin D, and other active treatments in these patients. Similar to these studies, our study shows that nurse case managers play a key role in influencing patients’ decisions. Medical practitioners should consider incorporating nurse case managers into their teams to provide targeted education and facilitate SDM, particularly in high-risk populations. However, Seuffert et al. also noted that even with education, a significant proportion of patients did not adhere to osteoporosis treatment recommendations, which is consistent with our findings. These findings suggest that, although educational interventions by healthcare providers can improve treatment adherence to some extent, ongoing efforts are required to address barriers to optimal osteoporosis care, such as patient concerns about treatment side effects and costs. Developing more comprehensive strategies, including patient education, follow-up, and support, may be necessary to enhance adherence and improve patient outcomes.

In addition to using a PDA to facilitate SDM, we also implemented an osteoporosis case management information system that allows nurse case managers to track patients’ follow-up appointments. When a patient misses a follow-up, our nurse case managers will contact them to understand the reason for their absence, assist with rescheduling the appointment, and educate them on the importance of medication adherence for treatment. Cornelissen et al. [20] described a study protocol evaluating the cost-effectiveness of combining a decision aid and motivational interviewing to improve medication persistence in osteoporosis patients treated at the FLS. Their study will compare a multi-component adherence intervention with standard care in a quasi-experimental design. The multi-component adherence intervention involves two nurse-led consultations: the first facilitates shared decision-making using the decision aid, and the follow-up employs motivational interviewing to support adherence. This study protocol further highlights the crucial role of nursing staff in enhancing patient compliance.

Discussions about costs during patient-clinician encounters, especially regarding out-of-pocket options, are often brief and rarely address affordability or cost-value considerations [45]. Shared decision-making provides a natural framework for these discussions, allowing patients to share their values and preferences while treatment options are explained by clinicians. Together, they can select the optimal course of action. Additionally, SDM has proven to be effective in enhancing decision-making and promoting health equity. Incorporating cost information into SDM helps patients understand trade-offs and align treatment choices with their preferences [46]. However, challenges, such as time constraints, can hinder these discussions [47]. Studies suggest that clinical staff can effectively communicate cost information; however, this may require additional training [48, 49]. Because osteoporosis-qualified nurse case managers have received specialized training in osteoporosis cases, we believe that they are well suited to assist with SDM and provide cost information at our hospital. Healthcare providers using PDAs should consider the financial costs to ensure that they align with the goals of SDM. Health systems should prioritize the dissemination of SDM materials that incorporate cost data. Maas et al. [50] reported on a study that focused on developing a decision aid to assist patients visiting FLS after a recent fracture, helping them decide whether to start anti-osteoporosis medication. In contrast, our study focuses on patients with osteoporosis who are at high risk, but have not yet fractured any bone and are not eligible for NHI coverage. Therefore, our PDA also provides cost information to patients. The use of PDA as part of an integrated care program was effective in our study. Healthcare providers should incorporate PDAs widely to help patients understand their options and make informed decisions, particularly regarding out-of-pocket treatments.

This study has a few limitations that must be acknowledged. First, the retrospective nature of the study may have introduced some unrecognized confounding factors, potentially biasing the results. We did not have data on the patients’ socioeconomic status, which was likely related to their decision to opt for out-of-pocket pharmaceutical therapy. Our study revealed that a substantial number of patients chose conservative treatment. Future studies can focus on understanding the barriers to treatment uptake, such as financial concerns, lack of awareness, or cultural factors, to develop more targeted interventions. Second, although we followed Taiwan’s existing guidelines to provide patient recommendations, we did not assess the long-term outcomes of these patients. Although our study showed increased uptake of out-of-pocket therapy, it would be valuable to conduct longitudinal studies to evaluate the long-term health outcomes and cost-effectiveness of these treatments in populations not covered by the NHI in Taiwan. This would provide more comprehensive data on the benefits of expanding coverage and integrating care programs. Third, the study population was limited to a single location, which may have introduced a selection bias. Further multicenter studies are needed to confirm the conclusion that patients who receive education from nurse case managers are significantly more likely to choose out-of-pocket anti-osteoporotic pharmaceutical therapy.

Conclusions

Patients who received education from nurse case managers at our hospital were significantly more likely to choose out-of-pocket anti-osteoporotic pharmaceutical therapy. Our study highlights the impact of nurse case managers on decision-making regarding out-of-pocket therapy among patients who are not eligible for NHI reimbursement. We propose that increase in out-of-pocket expenses following education indicates that informed personalized patient education by case managers can help bridge the gap between insurance coverage and medical guidelines.

Acknowledgements

The authors thank the Biostatistics Task Force of Taichung Veterans General Hospital and Chien-Yi Hsu for their consultations on the statistical analysis. The authors thank the Osteoporosis Prevention and Treatment Center of Taichung Veterans General Hospital for providing the osteoporosis database.

Declarations

This study was approved by the Institutional Review Board of the Taichung Veterans General Hospital (TCVGH-IRB No. CE22167A; Date of Approval: April 18, 2022), following the relevant institutional guidelines and regulations. Patient informed consent was waived because of the retrospective nature of the data analysis.

Not applicable.

Competing interests

The authors declare no competing interests.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Vorheriger Artikel The effect of nurses’ Machiavellian and deontic justice personality on the tendency to make medical errors and other factors: a cross-sectional study

Nächster Artikel ‘Technology in end-of-life care is very important’: the view of nurses regarding technology and end-of-life care

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material, Additional File 1: File format: PDF. Title of data: English translation of the patient decision aids. Description of data: English translation of the patient decision aids

Harvey N, Dennison E, Cooper C, Osteoporosis. Impact on health and economics. Nat Rev Rheumatol. 2010;6:99–105. https://doi.org/10.1038/nrrheum.2009.260.CrossRefPubMed

Chen FP, Huang TS, Fu TS, Sun CC, Chao AS, Tsai TL. Secular trends in incidence of osteoporosis in Taiwan: a nationwide population-based study. Biomed J. 2018;41:314–20. https://doi.org/10.1016/j.bj.2018.08.001.CrossRefPubMedPubMedCentral

Kanis JA, Odén A, McCloskey EV, Johansson H, Wahl DA, Cooper C, et al. A systematic review of hip fracture incidence and probability of fracture worldwide. Osteoporos Int. 2012;23:2239–56. https://doi.org/10.1007/s00198-012-1964-3.CrossRefPubMedPubMedCentral

Wang CB, Lin CFJ, Liang WM, Cheng CF, Chang YJ, Wu HC, et al. Excess mortality after hip fracture among the elderly in Taiwan: a nationwide population-based cohort study. Bone. 2013;56:147–53. https://doi.org/10.1016/j.bone.2013.05.015.CrossRefPubMed

Tai TW, Li CC, Huang CF, Chan WP, Wu CH. Treatment of osteoporosis after hip fracture is associated with lower all-cause mortality: a nationwide population study. Bone. 2022;154:116216. https://doi.org/10.1016/j.bone.2021.116216.CrossRefPubMed

Li CC, Hsu JC, Liang FW, Chang YF, Chiu CJ, Wu CH. The association between osteoporosis medications and lowered all-cause mortality after hip or vertebral fracture in older and oldest-old adults: a nationwide population-based study. Aging. 2022;14:2239–51. https://doi.org/10.18632/aging.203927.CrossRefPubMedPubMedCentral

Chen JL, Tai TW, Chou CY, Ku CK, Chien LN, Huang TW, et al. Incidence of different types of subsequent fractures and related mortality in Taiwan. Arch Osteoporos. 2022;17:55. https://doi.org/10.1007/s11657-022-01098-6.CrossRefPubMed

Tai TW, Hwang JS, Li CC, Hsu JC, Chang CW, Wu CH. The impact of various anti-osteoporosis drugs on all-cause mortality after hip fractures: a nationwide population study. J Bone Min Res. 2022;37:1520–26. https://doi.org/10.1002/jbmr.4627.CrossRef

Hsu YH, Li CC, Liang FW, Peng ZY, Chang YF, Hsu JC, et al. Reduced all-cause mortality with bisphosphonates among post-fracture osteoporosis patients: a nationwide study and systematic review. Clin Pharmacol Ther. 2022;112:711–9. https://doi.org/10.1002/cpt.2645.CrossRefPubMed

10.

Wu CH, Li CC, Hsu YH, Liang FW, Chang YF, Hwang JS. Comparisons between different anti-osteoporosis medications on postfracture mortality: a population-based study. J Clin Endocrinol Metab. 2023;108:827–33. https://doi.org/10.1210/clinem/dgac636.CrossRefPubMed

11.

Shepherd JA, Schousboe JT, Broy SB, Engelke K, Leslie WD. Executive Summary of the 2015 ISCD Position Development Conference on Advanced Measures From DXA and QCT: Fracture Prediction Beyond BMD. J Clin Densitom. 2015;18:274–86. https://doi.org/10.1016/j.jocd.2015.06.013

12.

Tai TW, Huang CF, Huang HK, Yang RS, Chen JF, Cheng TT, et al. Clinical practice guidelines for the prevention and treatment of osteoporosis in Taiwan: 2022 update. J Formos Med Assoc. 2023;122(Suppl 1):S4–13. https://doi.org/10.1016/j.jfma.2023.01.007.CrossRefPubMed

13.

LeBoff MS, Greenspan SL, Insogna KL, Lewiecki EM, Saag KG, Singer AJ, et al. The clinician’s guide to prevention and treatment of osteoporosis. Osteoporos Int. 2022;33:2049–102. https://doi.org/10.1007/s00198-021-05900-y.CrossRefPubMedPubMedCentral

14.

Ip TP, Lee CA, Lui TD, Wong RMY, Cheung CL, Chiu KCP et al. OSHK Task Group for the Formulation of the 2024 OSHK guideline for clinical management of postmenopausal osteoporosis in Hong Kong. HK Med J. 2024 OSHK Guideline for Clinical Management of Postmenopausal osteoporosis in Hong Kong. Hong Kong Med J. 2024;30 Suppl 2:1–44.

15.

Wozniak LA, Rowe BH, Ingstrup M, Johnson JA, McAlister FA, Bellerose D, et al. Patients’ experiences of nurse case-managed osteoporosis care: a qualitative study. J Patient Exp. 2020;7(2):251–7. https://doi.org/10.1177/2374373519827340.CrossRefPubMed

16.

Majumdar SR, Johnson JA, Bellerose D, McAlister FA, Russell AS, Hanley DA, et al. Nurse case-manager vs multifaceted intervention to improve quality of osteoporosis care after wrist fracture: Randomized controlled pilot study. Osteoporos Int. 2011;22:223–30. https://doi.org/10.1007/s00198-010-1212-7.CrossRefPubMed

17.

Seuffert P, Sagebien CA, McDonnell M, O’Hara DA. Evaluation of osteoporosis risk and initiation of a nurse practitioner intervention program in an orthopedic practice. Arch Osteoporos. 2016;11:10. https://doi.org/10.1007/s11657-016-0262-7.CrossRefPubMed

18.

Seah YC, Ho SF, Ang ASH, Bacud JP, Rosario BH. Nurse-initiated fall and osteoporosis screening for older adults in the emergency department. Cureus. 2023;15:e36001. https://doi.org/10.7759/cureus.36001.CrossRefPubMedPubMedCentral

19.

O’Rourke N. The orthopaedic nurse practitioner: breaking tradition to fill gaps in care delivery through varied scopes of practice. Int J Orthop Trauma Nurs. 2022;44:100843. https://doi.org/10.1016/j.ijotn.2020.100843.CrossRefPubMed

20.

Cornelissen D, Boonen A, Evers S, van den Bergh JP, Bours S, Wyers CE, et al. Improvement of osteoporosis Care Organized by nurses: ICON study - protocol of a quasi-experimental study to assess the (cost)-effectiveness of combining a decision aid with motivational interviewing for improving medication persistence in patients with a recent fracture being treated at the fracture liaison service. BMC Musculoskelet Disord. 2021;22(1):913. https://doi.org/10.1186/s12891-021-04743-2.CrossRefPubMedPubMedCentral

21.

Hawarden A, Bullock L, Chew-Graham CA, Herron D, Hider S, Jinks C, et al. Incorporating FRAX into a nurse-delivered integrated care review: a multi-method qualitative study. BJGP Open. 2023;7(2). https://doi.org/10.3399/BJGPO.2022.0146. BJGPO.2022.0146.

22.

Colón-Emeric CS, Hecker EJ, McConnell E, Herndon L, Little M, Xue T, et al. Improving shared decision-making for osteoporosis pharmacologic therapy in nursing homes: a qualitative analysis. Arch Osteoporos. 2022;17(1):11. https://doi.org/10.1007/s11657-021-01050-0.CrossRefPubMedPubMedCentral

23.

Li N, Hiligsmann M, Boonen A, van Oostwaard MM, de Bot RTAL, Wyers CE, et al. The impact of fracture liaison services on subsequent fractures and mortality: a systematic literature review and meta-analysis. Osteoporos Int. 2021;32:1517–30. https://doi.org/10.1007/s00198-021-05911-9.CrossRefPubMedPubMedCentral

24.

Wu CH, Tu ST, Chang YF, Chan DC, Chien JT, Lin CH, et al. Fracture liaison services improve outcomes of patients with osteoporosis-related fractures: a systematic literature review and meta-analysis. Bone. 2018;111:92–100. https://doi.org/10.1016/j.bone.2018.03.018.CrossRefPubMed

25.

Kanis JA, Oden A, Johansson H, Borgström F, Ström O, McCloskey E. FRAX and its applications to clinical practice. Bone. 2009;44:734–43. https://doi.org/10.1016/j.bone.2009.01.373.CrossRefPubMed

26.

Wu CH, McCloskey EV, Lee JK, Itabashi A, Prince R, Yu W, et al. Consensus of official position of IOF/ISCD FRAX initiatives in Asia-Pacific region. J Clin Densitom. 2014;17:150–5. https://doi.org/10.1016/j.jocd.2013.06.002.CrossRefPubMed

27.

Kanis JA, Harvey NC, Cooper C, Johansson H, Odén A, McCloskey EV, et al. Systematic review of intervention thresholds based on FRAX: a report prepared for the National Osteoporosis Guideline Group and the International Osteoporosis Foundation. Arch Osteoporos. 2016;11:25. https://doi.org/10.1007/s11657-016-0278-z.CrossRefPubMedPubMedCentral

28.

Chen JF, Yu SF, Hsu CY, Chiu WC, Wu CH, Lai HM, et al. The role of bone mineral density in therapeutic decision-making using the fracture risk Assessment Tool (FRAX): a sub-study of the Taiwan OsteoPorosis Survey (TOPS). Arch Osteoporos. 2019;14:101. https://doi.org/10.1007/s11657-019-0653-7.CrossRefPubMed

29.

Koppel R, Metlay JP, Cohen A, Abaluck B, Localio AR, Kimmel SE, et al. Role of computerized physician order entry systems in facilitating medication errors. JAMA. 2005;293:1197–203. https://doi.org/10.1001/jama.293.10.1197.CrossRefPubMed

30.

Pencille LJ, Campbell ME, Van Houten HK, Shah ND, Mullan RJ, Swiglo BA, et al. Protocol for the osteoporosis choice trial. A pilot randomized trial of a decision aid in primary care practice. Trials. 2009;10:113. https://doi.org/10.1186/1745-6215-10-113.CrossRefPubMedPubMedCentral

31.

Ralston KAP, Hauser B, Paskins Z, Ralston SH. Effective communication and the osteoporosis care gap. J Bone Min Res. 2022;37:2049–54. https://doi.org/10.1002/jbmr.4701.CrossRef

32.

Coulter A, Stilwell D, Kryworuchko J, Mullen PD, Ng CJ, van der Weijden T. A systematic development process for patient decision aids. BMC Med Inf Decis Mak. 2013;13;Suppl 2. https://doi.org/10.1186/1472-6947-13-S2-S2CrossRef

33.

Stacey D, Volk RJ, IPDAS Evidence Update Leads (Hilary Bekker, Karina Dahl Steffensen, Hoffmann TC, McCaffery K, Thompson R, Thomson R. Lyndal Trevena, Trudy van der Weijden, and Holly Witteman). The international patient decision aid standards (IPDAS) collaboration: Evidence Update 2.0. Med Decis Making. 2021;41:729–33. https://doi.org/10.1177/0272989X211035681

34.

Witteman HO, Maki KG, Vaisson G, Finderup J, Lewis KB, Dahl Steffensen K, et al. Systematic development of patient decision aids: an update from the IPDAS collaboration. Med Decis Mak. 2021;41:736–54. https://doi.org/10.1177/0272989X211014163.CrossRef

35.

Inker LA, Eneanya ND, Coresh J, Tighiouart H, Wang D, Sang Y, et al. New creatinine- and cystatin C-based equations to estimate GFR without race. N Engl J Med. 2021;385:1737–49. https://doi.org/10.1056/NEJMoa2102953.CrossRefPubMedPubMedCentral

36.

Kramer HJ, Jaar BG, Choi MJ, Palevsky PM, Vassalotti JA, Rocco MV, et al. An endorsement of the removal of race from GFR estimation equations: a position statement from the National Kidney Foundation kidney disease outcomes quality initiative. Am J Kidney Dis. 2022;80:691–6. https://doi.org/10.1053/j.ajkd.2022.08.004.CrossRefPubMed

37.

Payne RB, Little AJ, Williams RB, Milner JR. Interpretation of serum calcium in patients with abnormal serum proteins. Br Med J. 1973;4:643–6. https://doi.org/10.1136/bmj.4.5893.643.CrossRefPubMedPubMedCentral

38.

Chan DC, McCloskey EV, Chang CB, Lin KP, Lim LC, Tsai KS, et al. Establishing and evaluating FRAX^® probability thresholds in Taiwan. J Formos Med Assoc. 2017;116:161–8. https://doi.org/10.1016/j.jfma.2016.03.006.CrossRefPubMed

39.

Liu IT, Liang FW, Li CC, Chang YF, Sun ZJ, Lu TH, et al. Validation of the Taiwan FRAX^® calculator for the prediction of fracture risk. Arch Osteoporos. 2022;17:27. https://doi.org/10.1007/s11657-022-01068-y.CrossRefPubMed

40.

Kanis JA, McCloskey EV, Johansson H, Oden A, Ström O, Borgström F. Development and use of FRAX in osteoporosis. Osteoporos Int. 2010;21(Suppl 2):S407–13. https://doi.org/10.1007/s00198-010-1253-y.CrossRefPubMed

41.

Puth MT, Klaschik M, Schmid M, Weckbecker K, Münster E. Prevalence and comorbidity of osteoporosis- a cross-sectional analysis on 10,660 adults aged 50 years and older in Germany. BMC Musculoskelet Disord. 2018;19:144. https://doi.org/10.1186/s12891-018-2060-4.CrossRefPubMedPubMedCentral

42.

Chang CB, Yang RS, Chang LY, Peng JK, Tsai KS, Huang WJ, et al. Fracture types affect clinical outcomes of patients managed within the fracture liaison and osteoporosis medication management services. Sci Rep. 2019;9:10089. https://doi.org/10.1038/s41598-019-46315-4.CrossRefPubMedPubMedCentral

43.

Chang CB, Yang RS, Chang LY, Peng JK, Tsai KS, Huang WJ, et al. One-year outcomes of an osteoporosis liaison services program initiated within a healthcare system. Osteoporos Int. 2021;32:2163–72. https://doi.org/10.1007/s00198-021-05859-w.CrossRefPubMed

44.

Chien LN, Li YF, Yang RS, Yang TH, Chen YH, Huang WJ, et al. Real-world cost-effectiveness analysis of the fracture liaison services model of care for hip fracture in Taiwan. J Formos Med Assoc. 2022;121:425–33. https://doi.org/10.1016/j.jfma.2021.05.028.CrossRefPubMed

45.

Szumigalski KD, Tan ASL, Sinaiko AD. Let’s talk costs: out-of-pocket cost discussions and shared decision making. Patient Educ Couns. 2020;103:2388–90. https://doi.org/10.1016/j.pec.2020.04.022.CrossRefPubMed

46.

Durand MA, Carpenter L, Dolan H, Bravo P, Mann M, Bunn F, et al. Do interventions designed to support shared decision-making reduce health inequalities? A systematic review and meta-analysis. PLoS ONE. 2014;9:e94670. https://doi.org/10.1371/journal.pone.0094670.CrossRefPubMedPubMedCentral

47.

Hunter WG, Zhang CZ, Hesson A, Davis JK, Kirby C, Williamson LD, et al. What strategies do physicians and patients discuss to reduce out-of-pocket costs? Analysis of cost-saving strategies in 1,755 outpatient clinic visits. Med Decis Mak. 2016;36:900–10. https://doi.org/10.1177/0272989X15626384.CrossRef

48.

Sloan CE, Ubel PA. The 7 habits of highly effective cost-of-care conversations. Ann Intern Med. 2019;170(9Suppl):S33–5. https://doi.org/10.7326/M19-0537.CrossRefPubMed

49.

Perez Jolles M, Richmond J, Thomas KC. Minority patient preferences, barriers, and facilitators for shared decision-making with health care providers in the USA: a systematic review. Patient Educ Couns. 2019;102:1251–62. https://doi.org/10.1016/j.pec.2019.02.003.CrossRefPubMed

50.

Maas L, Raskin N, van Onna M, Cornelissen D, Bours S, van der Weijden T, et al. Development and usability of a decision aid to initiate anti-osteoporosis medication treatment in patients visiting the fracture liaison service with a recent fracture. Osteoporos Int. 2024;35:69–79. https://doi.org/10.1007/s00198-023-06906-4.CrossRefPubMed

Titel: Impact of patient education by nurse case managers on decision making for out-of-pocket anti-osteoporotic pharmaceutical therapy: a single-center retrospective study
verfasst von: Ya-Lian Deng
Hsu-Tung Lee
Shih-Yi Lin
Tan-Hsiu Liao
Chia-Tien Hsu
Publikationsdatum: 01.12.2024
Verlag: BioMed Central
Erschienen in: BMC Nursing / Ausgabe 1/2024
Elektronische ISSN: 1472-6955
DOI: https://doi.org/10.1186/s12912-024-02467-x

The effects of simulation-based education on undergraduate nursing students' competences: a multicenter randomized controlled trial

Open Access
Research

The general impact of self-stigma of mental illness on adult patients with depressive disorders: a systematic review

Open Access
Research

Perceived academic anxiety and procrastination among emergency nursing students: the mediating role of cognitive emotion regulation

Open Access
Research

Does internet addiction affect the level of emotional intelligence among nursing students? A cross-sectional study

Open Access
Research

The effect of nurses’ Machiavellian and deontic justice personality on the tendency to make medical errors and other factors: a cross-sectional study

Open Access
Research

The heterogeneous depression trajectory and its predictors in coronary heart disease patients undergoing home-based cardiac rehabilitation: a cohort study

Open Access
Research

Springerpflege

Impact of patient education by nurse case managers on decision making for out-of-pocket anti-osteoporotic pharmaceutical therapy: a single-center retrospective study

Abstract

Background

Methods

Results

Conclusions

Supplementary Information

Publisher’s note

Background

Methods

Study design and enrolled participants

Integrated care program and patient education by nurse case managers

Outcome analysis and variable definition

Statistical analysis

Results

Trend of out-of-pocket anti-osteoporotic pharmaceutical therapy

Factors influencing a patient’s decision on out-of-pocket pharmaceutical therapy

Discussion

Conclusions

Acknowledgements

Declarations

Competing interests

Publisher’s note

Electronic supplementary material

Weitere Artikel der Ausgabe 1/2024

The effects of simulation-based education on undergraduate nursing students' competences: a multicenter randomized controlled trial

The general impact of self-stigma of mental illness on adult patients with depressive disorders: a systematic review

Perceived academic anxiety and procrastination among emergency nursing students: the mediating role of cognitive emotion regulation

Does internet addiction affect the level of emotional intelligence among nursing students? A cross-sectional study

The effect of nurses’ Machiavellian and deontic justice personality on the tendency to make medical errors and other factors: a cross-sectional study

The heterogeneous depression trajectory and its predictors in coronary heart disease patients undergoing home-based cardiac rehabilitation: a cohort study

Springerpflege

Abstract

Background

Methods

Results

Conclusions

Supplementary Information

Publisher’s note

Background

Methods

Study design and enrolled participants

Integrated care program and patient education by nurse case managers

Outcome analysis and variable definition

Statistical analysis

Results

Trend of out-of-pocket anti-osteoporotic pharmaceutical therapy

Factors influencing a patient’s decision on out-of-pocket pharmaceutical therapy

Discussion

Conclusions

Acknowledgements

Declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Publisher’s note

Electronic supplementary material

The effects of simulation-based education on undergraduate nursing students' competences: a multicenter randomized controlled trial

The general impact of self-stigma of mental illness on adult patients with depressive disorders: a systematic review

Perceived academic anxiety and procrastination among emergency nursing students: the mediating role of cognitive emotion regulation

Does internet addiction affect the level of emotional intelligence among nursing students? A cross-sectional study

The effect of nurses’ Machiavellian and deontic justice personality on the tendency to make medical errors and other factors: a cross-sectional study

The heterogeneous depression trajectory and its predictors in coronary heart disease patients undergoing home-based cardiac rehabilitation: a cohort study