Common use of Coding Clause in Contracts

Coding. From the referral notes and clinic records, three main descriptive areas were defined under which patient information was allocated: medical problems, socioeconomic problems and socio-demographic background factors. Every new problem encountered in the referral or clinic notes was transferred to a Microsoft Access (2016, version 16) table and assigned a unique number as a code identifier. Medical problems included diagnoses and physical and mental health complaints as well as compliance-, pharmacology- and treatment-related problems. Socio-economic problems included all current socio-economic and emotional issues. Finally, demographic factors consisted of descriptions of the patient and his or her environment that were not presented directly as a problem but as factors describing the patientʼs situation. The codes were based on previous studies at the clinic and adjusted during the course of the study (before the data were analysed statistically) [13]. Only the corresponding author conducted the data collection. The supervisor made random quality assurance checks. Statistics A minimum sample size of 88 patients was calculated using a 20% error margin and a 50% difference between the overall probability of agreement and the probability of agreement expected by chance alone, as suggested by Gwet [14]. To ensure a higher statistical power, a sample size of 150 patients was chosen before any statistical calculations were conducted as this was deemed possible within the time limit. The chosen statistical software was RStudio Team (2018. RStudio: Integrated Development for R. RStudio, PBC, Boston, MA. V. 1.1.463)), STATA (StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC) and Microsoft Excel (2010). To describe the level of agreement between patient-perceived problems and the referring doctorʼs perception, Xxxxxʼx kappa coefficient (κ) and Chamberlainʼs proportionate positive agreement (pppa) were calculated [15, 16]: First, the number (%) of patient referral pairs with at least one matching problem in the referral and MHC notes was calculated (see Table 1). Next, it was determined how frequently each problem was reported in the MHC notes only, in the referral only and lastly in both the referral and clinic notes (see Table 1). The number was calculated for the “Problem list” and the total MHC notes, respectively. No p-value was calculated since the null hypothesis is generally not applicable to kappa [17]. Instead, Chamberlainʼs pppa was calculated since κ is problematic when the prevalence of an overlapping problem is low compared with the total number of times it is mentioned [15]. The pppa was read as a regular proportion [15]. Although a κ-value of 0.80 is often recommended as the minimum accepted value of agreement, this depends on the type of measurement [16]. In this study, a κ-value of 0.6 or above (corresponding to moderate, strong, or almost perfect overlap) or a pppa of 0.6 or higher was considered sufficient agreement because of the expected complexity in defining the patientsʼ problems. Ethics Only patients who consented to participate in the research and had this stated explicitly in the patient files were eligible for the study. Permission to handle personal data was granted by the Danish Data Protection Agency X.xx. 0000-00-0000 and by The Region of Southern Denmark X.xx. 19/7712. Trialregistration:not relevant. RESULTS Most patients were female, from Syria and had lived in Denmark for about 14 years (see Table 2). Only two patients did not require a translator. Often, both the patient and his or her partner were on social allowance (82% and 40%, respectively). Only 2% were currently working. Less than 50% had completed elementary school training and about 33% had worked in their home country and/or Denmark. Only two codes were above the 0.6 cut-off in the primary outcome: musculoskeletal pain and Type 2 diabetes mellitus, and only musculoskeletal reached the 0.6 cut-off for pain in the secondary outcome (see Table 1). In terms of the tertiary outcome, most patients had an overlap of one or more medical issues when comparing the referral to the “Problem list”, as well as the referral and the entire MHC data relating to the patient (93.33% and 97.33% respectively – see Table 1). When the patients had a wide range of problems, at least one patient- referral match for each patient may be expected. However, most problems were primarily mentioned in either the referral or the migrant notes and were rarely noted in both places for any single patient (see Figure 1). In contrast, only 20% of the patients had an overlap of one or more socioeconomic issues when comparing the referral and the “Problem list”. This figure only rose to 43.33% when including all MHC data. A greater prevalence of socioeconomic issues such as trauma and financial difficulties was found in the clinic notes than in the referrals (see Table 3).

Coding. From the referral notes and clinic records, three main descriptive areas were defined under which patient information was allocated: medical problems, socioeconomic problems and socio-demographic background factors. Every new problem encountered in the referral or clinic notes was transferred to a Microsoft Access (2016, version 16) table and assigned a unique number as a code identifier. Medical problems included diagnoses and physical and mental health complaints as well as compliance-, pharmacology- and treatment-related problems. Socio-economic problems included all current socio-economic and emotional issues. Finally, demographic factors consisted of descriptions of the patient and his or her environment that were not presented directly as a problem but as factors describing the patientʼs situation. The codes were based on previous studies at the clinic and adjusted during the course of the study (before the data were analysed statistically) [13]. Only the corresponding author conducted the data collection. The supervisor made random quality assurance checks. Statistics A minimum sample size of 88 patients was calculated using a 20% error margin and a 50% difference between the overall probability of agreement and the probability of agreement expected by chance alone, as suggested by Gwet Xxxx [14]. To ensure a higher statistical power, a sample size of 150 patients was chosen before any statistical calculations were conducted as this was deemed possible within the time limit. The chosen statistical software was RStudio Team (2018. RStudio: Integrated Development for R. RStudio, PBC, Boston, MA. V. 1.1.463)), STATA (StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC) and Microsoft Excel (2010). To describe the level of agreement between patient-perceived problems and the referring doctorʼs perception, Xxxxxʼx kappa coefficient (κ) and Chamberlainʼs proportionate positive agreement (pppa) were calculated [15, 16]: First, the number (%) of patient referral pairs with at least one matching problem in the referral and MHC notes was calculated (see Table 1). Next, it was determined how frequently each problem was reported in the MHC notes only, in the referral only and lastly in both the referral and clinic notes (see Table 1). The number was calculated for the “Problem list” and the total MHC notes, respectively. No p-value was calculated since the null hypothesis is generally not applicable to kappa [17]. Instead, Chamberlainʼs pppa was calculated since κ is problematic when the prevalence of an overlapping problem is low compared with the total number of times it is mentioned [15]. The pppa was read as a regular proportion [15]. Although a κ-value of 0.80 is often recommended as the minimum accepted value of agreement, this depends on the type of measurement [16]. In this study, a κ-value of 0.6 or above (corresponding to moderate, strong, or almost perfect overlap) or a pppa of 0.6 or higher was considered sufficient agreement because of the expected complexity in defining the patientsʼ problems. Ethics Only patients who consented to participate in the research and had this stated explicitly in the patient files were eligible for the study. Permission to handle personal data was granted by the Danish Data Protection Agency X.xx. 0000-00-0000 and by The Region of Southern Denmark X.xx. 19/7712. Trialregistration:Trial registration: not relevant. RESULTS Most patients were female, from Syria and had lived in Denmark for about 14 years (see Table 2). Only two patients did not require a translator. Often, both the patient and his or her partner were on social allowance (82% and 40%, respectively). Only 2% were currently working. Less than 50% had completed elementary school training and about 33% had worked in their home country and/or Denmark. Only two codes were above the 0.6 cut-off in the primary outcome: musculoskeletal pain and Type 2 diabetes mellitus, and only musculoskeletal reached the 0.6 cut-off for pain in the secondary outcome (see Table 1). In terms of the tertiary outcome, most patients had an overlap of one or more medical issues when comparing the referral to the “Problem list”, as well as the referral and the entire MHC data relating to the patient (93.33% and 97.33% respectively – see Table 1). When the patients had a wide range of problems, at least one patient- referral match for each patient may be expected. However, most problems were primarily mentioned in either the referral or the migrant notes and were rarely noted in both places for any single patient (see Figure 1). In contrast, only 20% of the patients had an overlap of one or more socioeconomic issues when comparing the referral and the “Problem list”. This figure only rose to 43.33% when including all MHC data. A greater prevalence of socioeconomic issues such as trauma and financial difficulties was found in the clinic notes than in the referrals (see Table 3).

Coding. From the referral notes and clinic records, three main descriptive areas were defined under which patient information was allocated: medical problems, socioeconomic problems and socio-demographic background factors. Every new problem encountered in the referral or clinic notes was transferred to a Microsoft Access (2016, version 16) table and assigned a unique number as a code identifier. Medical problems included diagnoses and physical and mental health complaints as well as compliance-, pharmacology- and treatment-related problems. Socio-economic problems included all current socio-economic and emotional issues. Finally, demographic factors consisted of descriptions of the patient and his or her environment that were not presented directly as a problem but as factors describing the patientʼs situation. The codes were based on previous studies at the clinic and adjusted during the course of the study (before the data were analysed statistically) [13]. Only the corresponding author conducted the data collection. The supervisor made random quality assurance checks. Statistics A minimum sample size of 88 patients was calculated using a 20% error margin and a 50% difference between the overall probability of agreement and the probability of agreement expected by chance alone, as suggested by Gwet Xxxx [14]. To ensure a higher statistical power, a sample size of 150 patients was chosen before any statistical calculations were conducted as this was deemed possible within the time limit. The chosen statistical software was RStudio Team (2018. RStudio: Integrated Development for R. RStudio, PBC, Boston, MA. V. 1.1.463)), STATA (StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC) and Microsoft Excel (2010). To describe the level of agreement between patient-perceived problems and the referring doctorʼs perception, Xxxxxʼx kappa coefficient (κ) and Chamberlainʼs Xxxxxxxxxxxʼx proportionate positive agreement (pppa) were calculated [15, 16]: First, the number (%) of patient referral pairs with at least one matching problem in the referral and MHC notes was calculated (see Table 1). Next, it was determined how frequently each problem was reported in the MHC notes only, in the referral only and lastly in both the referral and clinic notes (see Table 1). The number was calculated for the “Problem list” and the total MHC notes, respectively. No p-value was calculated since the null hypothesis is generally not applicable to kappa [17]. Instead, Chamberlainʼs pppa was calculated since κ is problematic when the prevalence of an overlapping problem is low compared with the total number of times it is mentioned [15]. The pppa was read as a regular proportion [15]. Although a κ-value of 0.80 is often recommended as the minimum accepted value of agreement, this depends on the type of measurement [16]. In this study, a κ-value of 0.6 or above (corresponding to moderate, strong, or almost perfect overlap) or a pppa of 0.6 or higher was considered sufficient agreement because of the expected complexity in defining the patientsʼ problems. Ethics Only patients who consented to participate in the research and had this stated explicitly in the patient files were eligible for the study. Permission to handle personal data was granted by the Danish Data Protection Agency X.xx. 0000-00-0000 and by The Region of Southern Denmark X.xx. 19/7712. Trialregistration:Trial registration: not relevant. RESULTS Most patients were female, from Syria and had lived in Denmark for about 14 years (see Table 2). Only two patients did not require a translator. Often, both the patient and his or her partner were on social allowance (82% and 40%, respectively). Only 2% were currently working. Less than 50% had completed elementary school training and about 33% had worked in their home country and/or Denmark. Only two codes were above the 0.6 cut-off in the primary outcome: musculoskeletal pain and Type 2 diabetes mellitus, and only musculoskeletal reached the 0.6 cut-off for pain in the secondary outcome (see Table 1). In terms of the tertiary outcome, most patients had an overlap of one or more medical issues when comparing the referral to the “Problem list”, as well as the referral and the entire MHC data relating to the patient (93.33% and 97.33% respectively – see Table 1). When the patients had a wide range of problems, at least one patient- referral match for each patient may be expected. However, most problems were primarily mentioned in either the referral or the migrant notes and were rarely noted in both places for any single patient (see Figure 1). In contrast, only 20% of the patients had an overlap of one or more socioeconomic issues when comparing the referral and the “Problem list”. This figure only rose to 43.33% when including all MHC data. A greater prevalence of socioeconomic issues such as trauma and financial difficulties was found in the clinic notes than in the referrals (see Table 3).