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Background Mental health problems and chronic health conditions cause significant productivity loss in the workplace. Chronic pain and mental health care needs in Canada that existed before COVID-19 remain unmet and have been exacerbated by the pandemic. The Alberta Societal Health Integration Program (ABSHIP) proposes an innovative community-based model in which Complementary and Alternative Medicine (CAM) services are provided to help achieve social recovery and improve economic participation. Methods Participants will receive CAM treatment in two modes: a minimum of two acupuncture treatment sessions per week for up to a total of 12 treatments, and voluntary Qigong exercise. The study will recruit 150 participants between the ages of 14 and 55 who are suffering from pain or mental health issues that are causing severe productivity loss. Primary outcome indicators are productivity (WPAI), pain (BPI), quality of sleep (PSQI), depression (PHQ-9), anxiety (GAD-7), anger (DAR-5), quality of life (EQ-5D-5L) and substance use (DAST-10 & CAGE). Secondary outcome indicators include general health care utilization, which will be measured by patients’ self-reported inpatient, outpatient, emergency department, and prescription drug utilization. Data will be collected at baseline (before treatment) and after the sixth and twelfth session (post-treatment) to measure short-term outcomes of the study. To understand the long-term impact of ABSHIP, participants will be invited to take the same survey three and six months after completing the program. Discussion ABSHIP is a first-of-its-kind study that provides CAM integrated interventions to enhance pain management, as well as protecting and fostering psychosocial well-being and resilience in children, adolescents, and seniors. The successful completion of the project will result in cost savings as well as significant evidence to aid instrument policy in the short and long run. Trial Registration This interventional study involving human participants, has been approved by the University of Calgary (UofC) Conjoint Health Research Ethics Board (CHREB) (ethics ID: REB 21-2050).

Objective The aim of the study was to compare the health outcomes and resource use of cancer patients who were new persistent opioid users with those who were not, after undergoing curative intent surgery for cancer. Background Little is known about long-term health outcomes (overdose, mortality) and resource utilization of new persistent opioid users among cancer patients undergoing curative-intent surgery. Methods This retrospective cohort study included all adults with a diagnosis of solid cancers who underwent curative-intent surgery during the study period (2011-2015) in Alberta, Canada and were opioid-naive before surgery, with a follow-up period until December 31, 2019. The key exposure, 'new persistent opioid user', was defined as a patient who was opioid-naive before surgery and subsequently filled at least one opioid prescription between 60 and 180 days after surgery. The primary outcome was opioid overdose that occurred within 3 years of surgery. All-cause death, non-cancer caused death, and department visit (yes vs. no), and hospitalization (yes vs. no) in the follow-up periods were also included as outcomes. Results In total, 19,219 patients underwent curative intent surgery with a median follow-up of 47 months, of which 1,530 (8.0%) were identified as postoperative new persistent opioid users. In total, 101 (0.5%) patients experienced opioid overdose within 3 years of surgery. Compared with non-opioid users, new persistent opioid users experienced a higher rate of opioid overdose (OR=2.37, 95% CI: 1.44 - 3.9) within 3 years of surgery. New persistent opioid use was also associated with a greater likelihood of being hospitalized (OR=2.03, 95% CI: 1.76-2.33) and visiting an emergency room (OR=1.83, 95% CI: 1.62-2.06) in the first year after surgery, and a higher overall (HR=1.28, 95% CI: 1.1-1.49) and non-cancer caused mortality (HR=1.33, 95% CI: 1.12-1.58), when compared with non-opioid users. Conclusion Post-operative new persistent opioid use among cancer patients undergoing curative-intent surgery is associated with subsequent opioid overdose, worse survival and more health resource utilization.

Coronavirus disease 2019 (COVID-19) has triggered the most significant public health crisis in recent years. However, studies on the impact of air quality on COVID-19 confirmed cases and death cases are still limited. This study examines the impact of the air quality on daily COVID-19 confirmed cases and deaths in prefecture-level cities in China from January 22, 2020, to December 31, 2020. The zero-inflated Poisson regression is applied in this paper to explore the impact of air pollution levels on daily confirmed cases and daily death cases. We find significant evidence for air quality index (AQI) to impact daily confirmed COVID-19 cases positively. By further decomposing AQI to its components, we find a significant positive impact from SO2, NO2, O3, and CO on daily confirmed COVID-19 cases. Results also show a positively significant impact from PM10, SO2, NO2, O3, and CO on daily dead COVID-19 cases. This study provides new insights into the relationship between air quality on daily COVID-19 confirmed cases and death cases in China and has direct implications for COVID-19 epidemic prevention and control.

Background: It is essential that clinical documentation and clinical coding be of high quality for the production of healthcare data. Objective: This study assessed qualitatively the strengths and barriers regarding clinical coding quality from the perspective of health information managers. Method: Ten health information managers and clinical coding quality coordinators who oversee clinical coders (CCs) were identified and recruited from nine provinces across Canada. Semi-structured interviews were conducted, which included questions on data quality, costs of clinical coding, education for health information management, suggestions for quality improvement and barriers to quality improvement. Interviews were recorded, transcribed and analysed using directed content analysis and informed by institutional ethnography. Results: Common barriers to clinical coding quality included incomplete and unorganised chart documentation, and lack of communication with physicians for clarification. Further, clinical coding quality suffered as a result of limited resources (e.g. staffing and budget) being available to health information management departments. Managers unanimously reported that clinical coding quality improvements can be made by (i) offering interactive training programmes to CCs and (ii) streamlining sources of information from charts. Conclusion: Although clinical coding quality is generally regarded as high across Canada, clinical coding managers perceived quality to be limited by incomplete and inconsistent chart documentation, and increasing expectations for data collection without equal resources allocated to clinical coding professionals. Implications: This study presents novel evidence for clinical coding quality improvement across Canada.

Perceived social support is known to be an important predictor of health outcomes in patients with acute coronary syndrome (ACS). This study investigates patterns of longitudinal trajectories of patient-reported perceived social support in individuals with ACS. Data are from 3013 patients from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease registry who had their first cardiac catheterization between 2004 and 2011. Perceived social support was assessed using the 19-item Medical Outcomes Study Social Support Survey (MOS) 2 weeks, 1 year, and 3 years post catheterization. Group-based trajectory analysis based on longitudinal multiple imputation model was used to identify distinct subgroups of trajectories of perceived social support over a 3-year follow-up period. Three distinct social support trajectory subgroups were identified, namely: “High” social support group (60%), “Intermediate” social support group (30%), and “Low” social support subgroup (10%). Being female (OR = 1.67; 95% CI = [1.18–2.36]), depression (OR = 8.10; 95% CI = [4.27–15.36]) and smoking (OR = 1.70; 95% CI = [1.23–2.35]) were predictors of the differences among these trajectory subgroups. Although the majority of ACS patients showed increased or fairly stable trajectories of social support, about 10% of the cohort reported declining social support. These findings can inform targeted psycho-social interventions to improve their perceived social support and health outcomes.

ObjectiveThe objective of our study was to explore the perspectives of patients and general practitioners (GPs) regarding interventions to increase initiation of cholesterol lowering medication (or statins), including a proposed laboratory-based facilitated relay intervention.DesignQualitative descriptive study using interviews and focus groups for data collection, and thematic analysis for data analysis.SettingPrimary care providers and patients in Calgary, Alberta, Canada.Participants17 GPs with primarily community-based, non-academic practices with at least 1 year of practice experience participated in semistructured interviews. 14 patients at high risk of cardiovascular disease participated in focus groups.Main outcome measuresExploration of strategies that might be used to enhance the prescription of, and adherence to statin therapy for patients with statin-indicated conditions.ResultsGPs proposed a variety of interventions to improve statin prescription, including electronic record audit solutions, GP directed education, and patient-oriented campaigns. Patients expressed that they may benefit from being provided access to their laboratory test results, as well as targeted education. Both parties provided positive feedback on the proposed laboratory-based facilitated relay intervention, while pointing out areas for improvement. Notably, GPs were concerned that the patient-directed component of the intervention might jeopardise therapeutic relationships, and patients were concerned about accidental disclosure of personal health information. Important considerations for the design of facilitated relay messaging should include brevity, simplicity and the provision of contact information for inquiries.ConclusionsGPs and patients described several suggestions for increasing statin initiation and welcomed the proposal of a laboratory-based facilitated relay strategy. These findings support further testing of this intervention which may enhance GPs’ ability to successfully engage patients in cardiovascular risk reduction through statin therapy.

Impact of isolation precautions on psychological wellbeing of patients has yet to be fully quantified. To assess the impact of isolation precautions on patients' health-related quality of life and depression or anxiety scales and estimate per day cost of anxiety and depression. Literature pertaining to impact of isolation precautions was searched on EMBASE and PubMed databases and Google Scholar. A two-step independent screening of the articles was performed. Articles that compared isolated and non-isolated patients using different quality of life and psychological burden scales were included. A meta-analysis was conducted using the Hospital Anxiety and Depression Scales (HADS-A and HADS-D). Psychological burden measures from selected literature were presented in a graph as effect sizes. Per day cost of anxiety and depression was estimated using pooled mean difference from meta-analysis. Out of 106 articles, 94 were excluded due to inclusion criteria, leaving 12 for full text review. After review of full text of the articles, seven articles were shortlisted for empirical analysis and four out of these seven for meta-analysis. The pooled mean difference estimates for HADS-A was -1.4 (P=0.15) and that for HADS-D was -1.85 (P=0.09). In the empirical analysis of psychological burden scales, the effect in all studies except one was negative. Results from meta-analysis and empirical analysis of psychological burden implied that isolated patients are worse off in general. The implied estimated per day cost of anxiety and depression in terms of quality-adjusted life years (QALYs) is approximately US$10.

Objective: Reducing rural-urban disparities in health and health care has been a key policy goal for the Chinese government. With mental health becoming an increasingly significant public health issue in China, empirical evidence of disparities in the use of mental health services can guide steps to reduce them. We conducted this study to inform China’s on-going health-care reform through examining how health insurance might reduce rural-urban disparities in the utilization of mental health inpatient services in China. Methods: This retrospective study used 10 years (2005–2014) of hospital electronic health records (EHRs) from the Shandong Center for Mental Health (SCMH) and the DaiZhuang Psychiatric Hospital (DZPH), two major psychiatric hospitals in Shandong Province. Health insurance was measured using types of health insurance and the actual reimbursement ratio (RR). Utilization of mental health inpatient services was measured by hospitalization cost, length of stay (LOS), and frequency of hospitalization. We examined rural-urban disparities in the use of mental health services, as well as the role of health insurance in reducing such disparities. Results: Hospitalization costs, LOS, and frequency of hospitalization were all found to be lower among rural than among urban inpatients. Having health insurance and benefiting from a relatively high RR were found to be significantly associated with a greater utilization of inpatient services, among both urban and rural residents. In addition, an increase in the RR was found to be significantly associated with an increase in the use of mental health services among rural patients. Conclusion: Consistent with the existing literature, our study suggests that increasing insurance schemes’ reimbursement levels could lead to substantial increases in the use of mental health inpatient services among rural patients, and a reduction in 3 rural-urban disparities in service utilization. In order to promote mental health care and reduce rural-urban disparities in its utilization in China, improving rural health insurance coverage (e.g., reducing the coinsurance rate) would be a powerful policy instrument.

Background Globally, health-care systems and organizations are looking to improve health system performance through the implementation of a person-centred care (PCC) model. While numerous conceptual frameworks for PCC exist, a gap remains in practical guidance on PCC implementation. Methods Based on a narrative review of the PCC literature, a generic conceptual framework was developed in collaboration with a patient partner, which synthesizes evidence, recommendations and best practice from existing frameworks and implementation case studies. The Donabedian model for health-care improvement was used to classify PCC domains into the categories of “Structure,” “Process” and “Outcome” for health-care quality improvement. Discussion The framework emphasizes the structural domain, which relates to the health-care system or context in which care is delivered, providing the foundation for PCC, and influencing the processes and outcomes of care. Structural domains identified include: the creation of a PCC culture across the continuum of care; co-designing educational programs, as well as health promotion and prevention programs with patients; providing a supportive and accommodating environment; and developing and integrating structures to support health information technology and to measure and monitor PCC performance. Process domains describe the importance of cultivating communication and respectful and compassionate care; engaging patients in managing their care; and integration of care. Outcome domains identified include: access to care and Patient-Reported Outcomes. Conclusion This conceptual framework provides a step-wise roadmap to guide health-care systems and organizations in the provision PCC across various health-care sectors.

Background Surveillance using coded administrative health data has shown that the prevalence of hypertension and diabetes in Canada increased substantially between 1998 to 2008. These findings require an assumption that the validity of hypertension and diabetes coding is stable over time. We tested this assumption by examining temporal trends in the validity of coding for hypertension and diabetes in the Canadian hospital Discharge Abstract Database. Methods We used the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) database, a clinical registry, as the reference standard to evaluate the validity of the Discharge Abstract Database in recording hypertension and diabetes in Alberta. The APPROACH database contains data for all Alberta residents who have undergone cardiac catheterization and includes prospective ascertainment of comorbid conditions before each procedure. We linked patient data between the 2 databases for 2002 to 2013 using patient provincial health number. Temporal trends in sensitivity, specificity, positive predictive value, negative predictive value and Cohen κ were calculated for both hypertension and diabetes in the Discharge Abstract Database. Results We matched 63 483 patients between the APPROACH database and the Discharge Abstract Database. The validity of the Discharge Abstract Database for hypertension and diabetes remained mostly consistent over time. Between 2002 and 2013, sensitivity, specificity, positive predictive value and negative predictive value ranged from 66% to 87% for hypertension and from 81% to 98% for diabetes; the corresponding κ scores ranged from 0.50 to 0.62 and from 0.80 to 0.89. No significant differences in the validity of coding were found across age, sex or hospital location subgroups. Interpretation The validity of coding for hypertension and diabetes in the Discharge Abstract Database remained fairly consistent between 2002 and 2013. Our findings support the use of the Discharge Abstract Database for hypertension and diabetes surveillance in hospital settings.

Perceived social support is known to be an important predictor of health outcomes in patients with acute coronary syndrome (ACS). This study investigates patterns of longitudinal trajectories of patient-reported perceived social support in individuals with ACS.Data are from 3013 patients from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease registry who had their first cardiac catheterization between 2004 and 2011. Perceived social support was assessed using the 19-item Medical Outcomes Study Social Support Survey (MOS) 2 weeks, 1 year, and 3 years post catheterization. Group-based trajectory analysis based on longitudinal multiple imputation model was used to identify distinct subgroups of trajectories of perceived social support over a 3-year follow-up period.Three distinct social support trajectory subgroups were identified, namely: "High" social support group (60%), "Intermediate" social support group (30%), and "Low" social support subgroup (10%). Being female (OR = 1.67; 95% CI = [1.18-2.36]), depression (OR = 8.10; 95% CI = [4.27-15.36]) and smoking (OR = 1.70; 95% CI = [1.23-2.35]) were predictors of the differences among these trajectory subgroups.Although the majority of ACS patients showed increased or fairly stable trajectories of social support, about 10% of the cohort reported declining social support. These findings can inform targeted psycho-social interventions to improve their perceived social support and health outcomes.

BACKGROUND Patient-centred quality indicators allow health care systems to monitor and evaluate patient-centred care practices and identify gaps in health care quality. Our objective was to determine whether Canadian provinces and territories measure patient-centred care, identify patient-centred quality indicators currently being used and compare patient-centred care practices and measurement in Canada to those of health care systems in other countries. METHODS An online survey was developed to collect data on demographic characteristics, patient-centred care practices, and indicators used at quality improvement organizations and health care authorities. The survey was conducted with quality improvement leads in Canada and 4 other countries. Content analysis methods were used to analyze and report the data. Patient-centred quality indicators were identified and categorized according to the Donabedian framework (structure, process, outcome). RESULTS The survey had a response rate of 47/67 (70%) and a completion rate of 58/60 (97%). We obtained completed surveys from 12 of the 13 provinces and territories in Canada. Respondents from most provinces indicated their organization used patient-centred care measures to inform practices. Respondents in only 4 provinces/territories reported using patient-centred quality indicators, for a total of 61 unique indicators. Most indicators used across Canada assessed aspects of care related to the Donabedian components of process and outcome. Findings for Canada were comparable to those for Sweden, England, Australia and New Zealand, where many measures are still in development. INTERPRETATION This study provided greater insight into patient-centred care measurement across Canada, Sweden, England, Australia and New Zealand and helped us to identify patient-centred quality indicators currently in use. These results will inform the development of a standard set of patient-centred quality indicators for implementation by health care organizations to improve the quality of health care.

IntroductionIt is essential that clinical documentation and data coding be of high quality for the production of healthcare data for research or administrative purposes. However, there is a limited understanding of the facilitators and barriers of coded data quality and strategies to improve it. Objectives and ApproachOur objective was to qualitatively assess what influences coded data quality from the perspective of health information managers who are responsible for the work of coding specialists. Nine health information managers and/or coding quality coordinators who oversee coding specialists were identified and recruited from nine provinces across Canada to participate in this study. Semi-structured interviews were conducted which asked questions on participant demographics, responsibilities, data quality, costs and budget of coding, continuing education for Health Information Management (HIM), suggestions for quality improvement, and barriers to quality improvement. Interviews were recorded and transcribed, and analyzed using Directed Content Analysis methodology. ResultsInterviewees were primarily responsible for managing staff, quality assurance, audits, reporting, budget, data collection, and transcription. Managers reported that the experienced coders under their employ strengthened coding quality. Common barriers to coding quality included incomplete and unorganized chart documentation, which led to undercoding, and lack of communication and access to physicians for clarification when needed. Further, coding quality suffered as a result of limited resources (e.g. staffing and budget) being available to HIM departments for an ever-expanding workload, that was commonly due to increasingly complex charts and additional project data. Managers unanimously reported that coding quality improvements can be made by 1) making interactive training programs available to coding specialists, and 2) streamlining sources of information from charts (i.e., transitioning to standardized electronic charting). Conclusion/ImplicationsAlthough coding quality is generally regarded as high across Canada, quality can be hampered by incomplete and inconsistent chart documentation, lack of resources (e.g. financial support, staff, education), and inconsistent coding standards across hospitals and provinces. This study presents novel evidence for coding quality improvement across Canada.

IntroductionCoded data serves a critical part in the process of identifying the resource allocation required for each department in a hospital and for research purposes. This paper attempts a cost-benefit analysis of the transition from ICD-9 health indicator coding system to ICD-10 coding system and quantify the economic impacts. Objectives and ApproachThe hypothesis adopted by this paper is that the transition from ICD-9 to ICD-10 has been beneficial for the health system due better disease management, resulting in cost savings and facilitation of high quality health research. Analyzing the inflation-adjusted costs compared with the benefits accrued from implementing the new coding system would enable informed decision making for the stakeholders at government and other levels of health provision. The methodology involves constructing ‘benefit scenarios’ via analysis of existing literature and interviewing coding managers; costs are evaluated using data collected on re-training coders and productivity losses during the transition phase. ResultsAn example of a benefit scenario would take the form of cost savings associated with correctly identifying people with diabetes (due to coded charts), hence resulting in a decline in blood sugar (HbA1c) levels via better disease management. This in turn may cause reductions in other high blood-sugar related diseases and thus increase efficiency for government funding in the health care sector. Improved data quality in ICD-10 is expected to have resulted in gains from specificity due to increased sensitivity of data classification and grouping. Actual cost of re-training of coders and ICD-10 software provider fees are expected to be higher than the costs anticipated before ICD-10 implementation. Productivity losses in the transition phase are expected to have declined as coders became more adept at coding. Conclusion/ImplicationsAn economic evaluation proves to be a vital part of eliciting whether the transition to the newer method of coding, ICD-10, has been beneficial to the end users of the data. It is important to understand the efficiency of resource allocation to healthcare and the financial implications such investments entail.

ObjectivesThe purpose of this study was to identify patient-centred quality indicators (PC-QI) and measures for measuring cultural competence in healthcare.DesignScoping review.SettingAll care settings.Search strategyA search of CINAHL, EMBASE, MEDLINE, PsycINFO, Social Work Abstracts and SocINDEX, and the grey literature was conducted to identify relevant studies. Studies were included if they reported indicators or measures for cultural competence. We differentiated PC-QIs from measures: PC-QIs were identified asa unit of measurement of the performance of the healthcare system, which reflects what matters to patients and families, and to any individual that is in contact with healthcare services.In contrast, measures evaluate delivery of patient-centred care, in the form of a survey and/or checklist. Data collected included publication year and type, country, ethnocultural groups and mention of quality indicator and/or measures for cultural competence.ResultsThe search yielded a total of 786 abstracts and sources, of which 16 were included in the review. Twelve out of 16 sources reported measures for cultural competence, for a total of 10 measures. Identified domains from the measures included: physical environment, staff awareness of attitudes and values, diversity training and communication. Two out of 16 sources reported PC-QIs for cultural competence (92 structure and process indicators, and 48 outcome indicators). There was greater representation of structure and process indicators and measures for cultural competence, compared with outcome indicators.ConclusionMonitoring and evaluating patient-centred care for ethnocultural communities allows for improvements to be made in the delivery of culturally competent healthcare. Future research should include development of PC-QIs for measuring cultural competence that also reflect cultural humility, and the involvement of ethnocultural communities in the development and implementation of these indicators.

Background Health insurance and provider payment reforms all over the world beg a key empirical question: what are the potential impacts of patient cost-sharing on health care utilization, cost and outcomes? The unique health insurance system and rich electronic medical record (EMR) data in China provides us a unique opportunity to study this topic. Methods Four years (2010 to 2014) of EMR data from one medical center in China were utilized, including 10,858 adult patients with liver diseases. We measured patient cost-sharing using actual reimbursement ratio (RR) which is allowed us to better capture financial incentive than using type of health insurance. A rigorous risk adjustment method was employed with both comorbidities and disease severity measures acting as risk adjustors. Associations between RR and health use, costs and outcome were analyzed by multivariate analyses. Results After risk adjustment, patients with more generous health insurance coverage (higher RR) were found to have longer hospital stay, higher total cost, higher medication cost, and higher ratio of medication to total cost, as well as higher number and likelihood that specific procedures were performed. Conclusion Our study implied that patient cost-sharing affects health care services use and cost. This reflects how patients and physicians respond to financial incentives in the current healthcare system in China, and the responses could be a joint effect of both demand and supply side moral hazard. In order to contain cost and improve efficiency in the system, reforming provide payment and insurance scheme is urgently needed.

BackgroundHospital length of stay (LOS) is a widely used measure for assessing cross-jurisdiction health system performance and informs resource allocation decisions. However, the accuracy of existing LOS risk adjustment models are limited, because they are mostly derived from administrative data, which mostly contain clinical/diagnostic information but lack detailed information on relevant demographic, socio-economic (SES), and self-reported health-related quality of life (HRQOL) risk factors, which have been shown to improve the accuracy of LOS risk adjustment models. The study investigates the relative contribution of demographic, socio-economic, and health status risk factors derived through data linkage in improving the accuracy of LOS risk adjustment models. MethodsPopulation-based data on 8000 individuals hospitalized for coronary heart disease were obtained from Alberta Provincial Project on Outcomes Assessment in Coronary Heart Disease (APPROACH) registry and linked to Alberta Discharge Abstract Database (DAD). SES was measured using multi-domain measure of SES derived from area-level census information, while the health-related quality of life outcome was measured using the Seattle Angina Questionnaire. LOS risk adjustment model based on hierarchical logistic regression models was developed to assess relative impact of each SES measure and HRQOL measure improving the predictive accuracy of LOS adjustment models. The relative impact of each predictor was assessed by its adjusted odds ratio (OR) and improvement over the predictive accuracy of a reference model that included patients’ clinical risk factors only. ResultMore than 80% of the hospitalized individuals had prolonged LOS more than 10 days. The HRQOL and single-domain measures of SES had significant impact in accurately predicting LOS. But the inclusion of the multi-domain measure SES did not significantly improve the accuracy of LOS risk adjustment models ConclusionUsing large population-based Canadian data, our study suggests that the inclusion of patients’ SES and health status information through data linkage can improve the accuracy of LOS risk adjustment models. The development of more accurate risk adjustment models can aid the identification of individuals at risk of prolonged LOS and comparison of health system performance across several cross-jurisdictions.

ObjectiveChina has collected high volume of electronic health record (EMR) data. The rich information in EMR data could be used for health services research. The first challenge is developing methods for extracting study variables. Our study aimed to develop and validate data extraction methods for defining clinical conditions. ApproachThe EMRs were from Beijing You-An Hospital, a leading liver diseases specialized teaching hospital affiliated with Capital Medical University in China. We developed EMR based case definitions for extracting common liver diseases and comorbidities in Charlson and Elixhauser comorbidity algorithms. We developed the EMR case definitions based on the fundamental EMR structure and clinical expertise. To determine validity of the EMR case definitions, we calculated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for each case definition based on the “gold standard”: randomly selected 450 charts review conducted by two hepatologists. The agreement between the two reviewers was assessed by kappa value. ResultsIn total, 69,864 EMRs for adult patients with liver disease admitted between 2010 and 2015 were included in this study. Among these patients, we identified 13,763 (19.7%) PLC, 18,654 (26.7%) Hepatitis B Virus (HBV), 4681 (6.7%) Hepatitis C Virus (HCV), 18,933 (27.1%) cirrhosis, 7,964 (11.4%) diabetes, 8,523 (12.2%) hypertension, 3,283 (4.7%) Fluid and Electrolyte Disorder (FED), 3,563 (5.1%) renal disease and 1,258 (1.8%) Cerebrovascular Disease (CEVD). Between the two reviewers, the Kappa values fell between 0.65 and 1.00. Of the 450 EMRs reviewed, the two reviewers identified 95 (21.1%) PLC, 197 (43.8%) HBV, 40 (8.9%) HCV, 146 (32.4%) cirrhosis, 39 (8.7%) diabetes, 45 (10.0%) hypertension, 25 (5.6%) FED, 26 (5.8%) renal disease and 8 (1.8%) CEVD. Compared to chart review, the sensitivity, specificity, PPV and NPV of the algorithms for above conditions are respectively: (PLC) 100%, 99.44%, 97.94%, 100%; (HBV) 61.93%, 99.21%, 98.39%, 76.99%; (HCV) 72.50%, 99.51%, 93.55%, 97.37%; (cirrhosis) 78.77%, 98.36%, 95.83%, 90.61%; (diabetes) 97.44%, 98.05%, 82.61%, 99.75%; (hypertension) 95.00%, 99.72%, 97.44%, 99.45%; (FED) 71.43%, 100.00%, 100.00%, 99.09%; (renal disease) 96.15% 100.00% 100.00% 99.76%; (CEVD) 100.00%, 99.77%, 88.89%, 100.00%. ConclusionOur EMR case definitions of above conditions had high validity and could be applied to extract clinical variables including major liver diseases and comorbidities from Chinese EMR. This extracting method could be modified slightly for extracting other medical conditions from Chinese EMRs.

ObjectiveRisk adjustment methods are widely used to compare quality of care or predict health outcome, but the optimal approach is unclear for liver disease. This study is to compare the performance of common risk adjustment methods for predicting in-hospital mortality in patients with liver disease using Electronic Medical Record (EMR). ApproachThe EMR data was derived from Beijing YouAn Hospital between 2010 and 2015. 85,526 EMRs were included. Previously developed and validated automated EMR case definitions were applied to define the conditions including primary liver cancer, cirrhosis and other conditions included in Charlson, Elixhauser comorbidity algorithms, Child-Turcotte-Pugh (CTP) score and Model for End-Stage Liver Disease (MELD). Logistic regression was conducted and C-statistic was obtained to compare the performance of the different methods for predicting in-hospital mortality. To eliminate the effect of the model complexity on model performance, we compared Akaike Information Criterion (AIC) of different methods (smaller AIC is better). ResultIn total, we included three liver diseases cohort: 7,178 Primary Liver Cancer (PLC) patients, 11,121 cirrhosis patients and 7,298 cirrhosis without PLC patient. For PLC cohort, C-statistics of these compared indexes ranged from 0.72 to 0.84; AIC was between 4312.3 and 5048.4. For cirrhosis cohort, C-statistics of these compared indexes ranged from 0.73 to 0.83; AIC was between 4952.1 and 5788.2. For cirrhosis without PLC cohort, C-statistics ranged from 0.73 to 0.84; AIC was between 2608.3 and 3240.5. It was consistent across the three cohorts that MELD + sodium (MELD_Na) score (a variant of MELD score) had the highest C-statistic and lowest AIC; CTP had the lowest C-statistic and highest AIC. Integrating Charlson Comorbidity to MELD_Na, C-statistic improved to 0.86 and AIC reduced. ConclusionAmong the compared risk adjustment methods, MELD_Na performed best for predicting in-hospital mortality among patients with PLC or cirrhosis using Chinese EMRs. Adding clinical information to comorbidity algorithms improved the performance of the model.

ObjectiveWith tremendous potential for research and policy use, the development of Electronic Medical Record (EMR) is unprecedentedly growing in China. The rich clinical and financial data in the Chinese EMR provides us a unique chance to examine the impacts of health insurance on health care utilization and outcomes, controlling for patient’s disease severity. ApproachesOur study population included patients with cirrhosis or primary liver cancer (PLC), from a large teaching hospital in Beijing. The comorbidity and disease severity variables were defined using EMR automated extraction methods that were validated in previous study. Health insurance was measured by actual reimbursement ratio (RR), which better captures patients’ actual financial burden than type of health insurance. Generalized linear regression model was used to analyze the impacts of health insurance coverage on total hospital expense, ratio of medication cost to total expense, and number of major procedures (i.e., transcatheter arterial chemoembolization, TACE) for cirrhosis. Logistic regression was used to assess the impact of health insurance on hospital mortality and the rate of TACE. We employed a wide range of risk factors in our models to adjust for disease severity and comorbidities, including Charlson comorbidities, MELD-Na score, and etiological factors of liver diseases.ResultsIn total, 5,465 cirrhosis patients and 3,357 PLC patients were included in the study. Among the PLC patients we identified 534 patients underwent TACE. After adjusted for comorbidities, disease severity and other confounders, RR was found to be associated with hospital mortality with odds ratio 3.2 in cirrhosis patients and 6.0 in PLC patients. Higher RR was correlated to lower total hospital cost (logarithm transferred coefficient -0.08 in cirrhosis patients and -0.15 in PLC patients) but related to higher ratio of medication cost (logarithm transferred coefficient 0.09 in both cirrhosis and PLC patients). Additionally, higher RR was associated with higher rate (odds ratio 1.6 in PLC patients) and also more times of TACE (logarithm transferred coefficient 0.31 in TACE patients). The results were consistent between cirrhosis patients and PLC patients.ConclusionThis study provided evidences that physicians’ behavior was influenced by health insurance. Patients with more generous health insurance coverage (higher RR) were found to have relatively lower total hospital cost but higher ratio of medication cost, higher rate and more times of TACE, and were more prone to die in hospital. These are evidences for physician’s gaming reacting to the economic incentives of the payment systems in China.

Background Risk adjustment is essential for valid comparison of patients’ health outcomes or performances of health care providers. Several risk adjustment methods for liver diseases are commonly used but the optimal approach is unknown. This study aimed to compare the common risk adjustment methods for predicting in-hospital mortality in cirrhosis patients using electronic medical record (EMR) data. Methods The sample was derived from Beijing YouAn hospital between 2010 and 2014. Previously validated EMR extraction methods were applied to define liver disease conditions, Charlson comorbidity index (CCI), Elixhauser comorbidity index (ECI), Child-Turcotte-Pugh (CTP), model for end-stage liver disease (MELD), MELD sodium (MELDNa), and five-variable MELD (5vMELD). The performance of the common risk adjustment models as well as models combining disease severity and comorbidity indexes for predicting in-hospital mortality was compared using c-statistic. Results Of 11,121 cirrhotic patients, 69.9% were males and 15.8% age 65 or older. The c-statistics across compared models ranged from 0.785 to 0.887. All models significantly outperformed the baseline model with age, sex, and admission status (c-statistic: 0.628). The c-statistics for the CCI, ECI, MELDNa, and CTP were 0.808, 0.825, 0.849, and 0.851, respectively. The c-statistic was 0.887 for combination of CTP and ECI, and 0.882 for combination of MELDNa score and ECI. Conclusions The liver disease severity indexes (i.e., CTP and MELDNa score) outperformed the CCI and ECI for predicting in-hospital mortality among cirrhosis patients using Chinese EMRs. Combining liver disease severity and comorbidities indexes could improve the discrimination power of predicting in-hospital mortality.

Background Health insurance and provider payment reforms all over the world beg a key empirical question: what are the potential impacts of patient cost-sharing on health care utilization, cost and outcomes? The unique health insurance system and rich electronic medical record (EMR) data in China provides us a unique opportunity to study this topic. Methods Four years (2010 to 2014) of EMR data from one medical center in China were utilized, including 10,858 adult patients with liver diseases. We measured patient cost-sharing using actual reimbursement ratio (RR) which is allowed us to better capture financial incentive than using type of health insurance. A rigorous risk adjustment method was employed with both comorbidities and disease severity measures acting as risk adjustors. Associations between RR and health use, costs and outcome were analyzed by multivariate analyses. Results After risk adjustment, patients with more generous health insurance coverage (higher RR) were found to have longer hospital stay, higher total cost, higher medication cost, and higher ratio of medication to total cost, as well as higher number and likelihood that specific procedures were performed. Conclusion Our study implied that patient cost-sharing affects health care services use and cost. This reflects how patients and physicians respond to financial incentives in the current healthcare system in China, and the responses could be a joint effect of both demand and supply side moral hazard. In order to contain cost and improve efficiency in the system, reforming provide payment and insurance scheme is urgently needed.

ObjectivesThe shift to the patient-centred care (PCC) model as a healthcare delivery paradigm calls for systematic measurement and evaluation. In an attempt to develop patient-centred quality indicators (PC-QIs), this study aimed to identify quality indicators that can be used to measure PCC.MethodsDesign: scoping review. Data Sources: studies were identified through searching seven electronic databases and the grey literature. Search terms included quality improvement, quality indicators, healthcare quality and PCC. Eligibility Criteria: articles were included if they mentioned development and/or implementation of PC-QIs. Data Extraction and Synthesis: extracted data included study characteristics (country, year of publication and type of study/article), patients’ inclusion in the development of indicators and type of patient populations and point of care if applicable (eg, in-patient, out-patient and primary care).ResultsA total 184 full-text peer-reviewed articles were assessed for eligibility for inclusion; of these, 9 articles were included in this review. From the non–peer-reviewed literature, eight documents met the criteria for inclusion in this study. This review revealed the heterogeneity describing and defining the nature of PC-QIs. Most PC-QIs were presented as PCC measures and identified as guidelines, surveys or recommendations, and therefore cannot be classified as actual PC-QIs. Out of 502 ways to measure PCC, only 25 were considered to be actual PC-QIs. None of the identified articles implemented the quality indicators in care settings.ConclusionThe identification of PC-QIs is a key first step in laying the groundwork to develop evidence-based PC-QIs. Research is needed to continue the development and implementation of PC-QIs for healthcare quality improvement.

Currently, the most popular hospital payment method in China is fee-for-service (FFS) with a global budget cap. As of December 2009, a policy change means that heart stents are covered by public health insurance, whereas previously they were not. This policy change provides us an opportunity to study how a change in insurance benefit affected the quantity and quality of hospital services. The new policy introduced incentives for both patients and providers: it encourages patient demand for percutaneous coronary intervention (PCI) services and stent use (moral hazard effect), and discourages hospital supply due to the financial pressures of the global cap (provider gaming effect). If the provider’s gaming effect dominates the moral hazard effect, actual utilisation and costs might go down, and vice versa. Our hypothesis is that patients in the higher reimbursement groups will have fewer PCIs and lower inpatient costs. We aimed to examine the impact of health insurance benefit design on PCI and stent use, and on inpatient costs and out-of-pocket expenses for patients with acute myocardial infarction (AMI) in Shanghai. We included 720 patients with AMI (467 before the benefit change and 253 after) from a large teaching tertiary hospital in Shanghai. Data were collected via review of hospital medical charts, and from the hospital billing database. Patient information collected included demographic characteristics, medical history and procedure information. All patients were categorised into four groups according to their actual reimbursement ratio: high (90–100 %), moderate (80–90 %), low (0–80 %) and none (self-paid patients). Multiple regression and difference-in-difference (DID) models were used to investigate the impacts of the health insurance benefit design on PCI and stent use, and on total hospital costs and patients’ out-of-pocket expenses. After the change in insurance benefit policy, compared with the self-paid group, PCI rates for the moderate and low reimbursement groups increased by 22.2 and 20.3 %, respectively, and decreased by 48.7 % for the high reimbursement group. The change in insurance benefit policy had no impact on the number of stents used. The high reimbursement group had the lowest hospital costs, and the low reimbursement group had the highest hospital costs, regardless of benefit policy change. The general linear regression results showed that the high reimbursement group had higher total hospital costs than the self-paid group, but were the lowest among all reimbursement groups after the benefit policy change (DIDh = 1,202.21, P = 0.0096). There were no significant changes in the other two groups, and there were no differences in the out-of-pocket costs across any of the insured groups. Our results suggest that the benefit policy change did not impact life-saving procedures or reduce patients’ burden of disease among AMI patients. The effect of ‘provider gaming’ was the strongest for the high reimbursement group as a result of the global budget cap pressure. The current FFS with a global budget cap is of low efficiency for cost containment and equity improvement. Payment method reforms with alignment of financial incentives to improve provider behaviour in practicing evidence-based medicine are needed in China.

Background In 2003, the New Cooperative Medical Scheme (NCMS) was introduced in China to re-establish health insurance for the country’s vast rural population. In addition, the coverage of NCMS has been expanding after the new health care reform launched in 2009. This study aims to examine whether the NCMS and its recent expansion have reached the goal of reducing the risk and inequality of catastrophic health spending for rural residents in China. Methods We conducted a face-to-face household survey in three counties of the Shandong province in 2009 and 2012. Using this unique panel data, we examined the changes in the incidence and intensity of catastrophic health expenditures (CHEs) before and after NCMS reimbursement. We used concentration index (CI) and decomposition method to study the changes in inequality in CHEs. Results We found that NCMS reimbursement played a role of reducing both the incidence and intensity of CHEs, and that this impact was stronger after the new health care reform was launched. After reimbursement, the concentration indices for CHEs were 0.073 and 0.021 in 2009 and 2012, indicating that the rich had a greater tendency to incur CHEs and there existed less inequality in the incidence of CHEs after reimbursement in 2012 compared with 2009. The decomposition analysis results suggested that changes in CHE inequality between 2009 and 2012 were attributed to changes in economic status and household size rather than reimbursement levels. Conclusions Our results indicated that inequality was shrinking from 2009 to 2012, which could be a result of fewer rich people having CHEs in 2012 compared with 2009. The impact of NCMS in alleviating the financial burden of rural residents was still limited, especially among the poor. Health care reform policies in China that aim to reduce CHEs must continue to place an emphasis on improving reimbursement, cost containment, and reducing income inequalities.

Background The adoption of the electronic medical record (EMR) is rapidly growing in China. Constantly evolving, Chinese EMRs contain vast amounts of clinical and financial data, providing tremendous potential for research and policy use; however, they are only partially standardized and contain free text or unstructured data. To utilize the information contained in Chinese EMRs, the development of data extraction methodology is urgently needed. The purpose of this study is to develop and validate methods to extract clinical information from the Chinese EMR for research use. Methods Using 2010 to 2014 EMR data from YouAn Hospital, a large teaching hospital affiliated with Capital Medical University in Beijing, China, we developed extraction methods including 40 EMR definitions for defining 6 liver disease, 5 disease severity conditions, and 29 comorbidities and treatments. We conducted a chart review of 450 randomly selected EMRs. Using physician chart review results as a reference, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to validate each EMR definition. Results The sensitivity of the 6 EMR definitions for liver diseases ranged from 78.9 to 100.0 %, and PPV ranged from 82.1 to 100.0 %. The sensitivity of the 5 definitions on disease severity conditions ranged from 91.0 to 100.0 %, and PPV ranged from 79.2 to 100.0 %. Among the 29 EMR definitions for comorbidities and treatments, 23 had sensitivity over 90.0 % and 25 had PPV over 80.0 %. The specificity and NPV for all 40 EMR definitions were over 90.0 %. Conclusion The extraction method developed is a valid way of extracting information on liver diseases, comorbidities and related treatments from YouAn hospital EMRs. Our method should be modified for application to other Chinese EMR systems, following our framework for extracting conditions. Electronic supplementary material The online version of this article (doi:10.1186/s12911-016-0348-6) contains supplementary material, which is available to authorized users.