scholarly journals How is patient activation related to healthcare service utilisation? Evidence from electronic patient records in England

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Feifei Bu ◽  
Daisy Fancourt
Keyword(s):  
Health Care ◽  
Healthcare Service ◽  
Patient Activation ◽  
Outcome Variable ◽  
Service Utilisation ◽  
Patient Records ◽  
Electronic Patient Records ◽  
Emergency Admissions ◽  
Gp Visits

Abstract Background There is increasing awareness of the importance of patient activation (knowledge, skills, and confidence for managing one’s health and health care) among clinicians and policy makers, with emerging evidence showing higher levels of patient activation are associated with better health outcomes and experiences of health care. This study aimed to examine the association between patient activation and a wide range of specific types of healthcare service utilisation in England, including GP and non-GP primary care, elective and emergency hospital admissions, outpatient visits, and attendances at the Accident and Emergency department. Methods Data were derived from linked electronic patient records collected by primary and secondary healthcare providers in North West London between January 2016 and November 2019. Our analyses focused on adults (18+) with a valid Patient Activation Measure (PAM). After excluding patients with missing data, we had an analytical sample of 15,877 patients. Data were analysed using negative binomial regression and logistic regression models depending on the outcome variable. Results Patients had a mean activation score of 55.1 and a standard deviation (SD) of 17.7 (range: 0–100). They had an average of 5.4 GP visits (SD = 8.0), 26.8 non-GP visits (SD = 23.4) and 6.0 outpatient attendances (SD = 7.9) within a one-year follow-up. About 24.7% patients had at least one elective admission, 24.2% had one or more emergency admissions, and 42.3% had one or more A&E attendance within the follow-up. After accounting for a number of demographic and health factors, we found a linear (or proximately linear) association between patient activation and the number of GP visits, emergency admissions and A&E attendance, but a non-linear relationship between patient activation and the number of non-GP visits, the number of outpatient attendance and elective inpatient admission. Conclusions This study has provided strong empirical evidence from England linking patient activation with healthcare service utilisation. It suggests the value of supporting patient activation as a potential pathway to ease the burden of healthcare system.

scholarly journals What Explains the Regional Variation in the Use of General Practitioners in Australia?

2020 ◽  
Author(s):  
Chunzhou Mu ◽  
Jane Hall
Keyword(s):  
Health Care ◽  
Population Health ◽  
General Practitioners ◽  
Regional Variation ◽  
Regional Differences ◽  
Outcome Variable ◽  
Supply Side ◽  
Demand Side ◽  
Gp Visits ◽  
Supply Side Factors

Abstract Background: Regional variation in the use of health care services is widespread. Identifying and understanding the sources of variation and how much variation is unexplained can inform policy interventions to improve the efficiency and equity of health care delivery. Methods: We examined the regional variation in the use of general practitioners (GPs) using data from the Social Health Atlas of Australia by Statistical Local Area (SLAs). 756 SLAs were included in the analysis. The outcome variable of GP visits per capita by SLAs was regressed on a series of demand-side factors measuring population health status and demographic characteristics and supply-side factors measuring access to physicians. Each group of variables was entered into the model sequentially to assess their explanatory share on regional differences in GP usage. Results: Both demand-side and supply-side factors were found to influence the frequency of GP visits. Specifically, areas in urban regions, areas with a higher percentage of the population who are obese, who have profound or severe disability, and who hold concession cards, and areas with a smaller percentage of the population who reported difficulty in accessing services have higher GP usage. The availability of more GPs led to higher use of GP services while the supply of more specialists reduced use. 30.56% of the variation was explained by medical need. Together, both need-related and supply-side variables accounted for 32.24% of the regional differences as measured by the standard deviation of adjusted GP-consultation rate. Conclusions: There was substantial variation in GP use across Australian regions with only a small proportion of them being explained by population health needs, indicating a high level of unexplained clinical variation. Supply factors did not add a lot to the explanatory power. There was a lot of variation that was not attributable to the factors we could observe. This could be due to more subtle aspects of population need or preferences and therefore warranted. However, it could be due to practice patterns or other aspects of supply and be unexplained. Future work should try to explain the remaining unexplained variation.

scholarly journals EP.WE.727Upper GI Surgery Clinic Referrals; Can We Improve the Patient Experience?

2021 ◽  
Vol 108 (Supplement_7) ◽  
Author(s):  
Mohamed Abouelazayem ◽  
Raluca Belchita
Keyword(s):  
Patient Experience ◽  
Waiting Time ◽  
Waiting Times ◽  
Patient Records ◽  
Electronic Patient Records ◽  
Post Discharge ◽  
Average Waiting Time ◽  
Upper Gi

Abstract Aim To review the new referrals to the Upper GI surgery clinic for appropriateness, investigations requested, and waiting times and to identify potential pathways to reduce waiting times and improve the patient experience. Method Patients who attended the UGI clinic over 2 months period were identified. Data were collected from GP referrals and Electronic Patient Records. Follow up, post-discharge appointments, and Did Not Attends were excluded. Data collected included time from referral to first clinic, symptoms, investigations requested, suitability for a pathway, and appropriateness of referral. A first clinic outcome was concluded from reading the GP referral, there were 5 outcomes to choose from; direct to another specialty, discharge back to GP, clinic, surgery, pre-investigate and clinic. Results 147 referrals were analysed. The average waiting time from referral to the first clinic was 51 days (range 7-119 days). 73% of the referrals were GP referrals and 27% from other specialties. The most common referral was for gallstones and the most common 2 outcomes were Pre-investigate and surgery. Conclusion Most of the investigations and outcomes suggested from the project were the same as those from clinic letters. The following pathways can be developed to cut waiting times and costs for the trust:

Access to cataract services in selected rural communities in South Africa

2019 ◽  
Author(s):  
Lunic Base Khoza ◽  
Wilfred Njabulo Nunu ◽  
Bumani Solomon Manganye ◽  
Pfungwa Mambanga ◽  
Shonisani Tshivhase ◽  
...  
Keyword(s):  
South Africa ◽  
Health Care ◽  
Rural Communities ◽  
Rural Areas ◽  
Access To Health Care ◽  
Health Care Services ◽  
Secondary Data ◽  
Demographic Characteristics ◽  
Outcome Variable ◽  
Patient Records

Abstract Background Despite government efforts to improve access to health care services through the re-engineered Primary Health Care and National Health insurance platform, access still remain a challenge particularly in rural areas. The aim of this study was to analyse secondary data on cataract patients who were attended to in selected hospitals in rural Limpopo of South Africa. Methodology A cross section survey was conducted on 411 patient records from five selected hospitals in Vhembe district. A pre tested structured checklist was used to guide retrieval of variables from patient records. The collected data was entered into excel spreadsheet, cleaned and imported into Statistical Package for Social Sciences version 26 for analysis. Proportions of demographic characteristics were presented and these were cross tabulated with the outcome variable “success of operation” using Chi Squared tests. Results Findings point out that majority of patients who attended hospital for eye services were aged 65 years above and females (63%). There was no association between the tested demographic characteristics and the outcome variable. Most patients were diagnosed in the period 2015-2018 (60%). Over 90% of those that were operated had successful operations. Of the remaining 10% that had unsuccessful operations, 30% cited complications as being the reason why these operations were unsuccessful. Conclusions It is evident from the findings that cataract services offered in rural areas have low impact as they are not accessible to the patient. It is critical to have a worker retention strategy to retain experts.

scholarly journals Boosting Chinese healthcare service providers’ utilization of behavioural and pharmacotherapy interventions for cigarette smoking cessation by ‘WeChat WeQuit’ program

2019 ◽  
Author(s):  
Yanhui Liao ◽  
Yunfei Wang ◽  
Zhenzhen Wu ◽  
Yuhang Liu ◽  
Chudong Wang ◽  
...  
Keyword(s):  
Health Care ◽  
Smoking Cessation ◽  
Cigarette Smoking ◽  
Training Program ◽  
Healthcare Service ◽  
Service Providers ◽  
Intervention Group ◽  
Control Group ◽  
Health Care Settings

AbstractIntroductionIn China, standard smoking cessation practices are rarely used by healthcare service providers (HSPs). WeChat, a popular social media app, has been widely used in China.MethodsIn this single-blind, randomized trial, undertaken in China with 8-week interventions and follow-up to 52 weeks, about 2,200 providers from different health care settings will be randomly selected to the intervention or control group. This trial will be conducted in China between June 2018 to October 2019. The intervention group will receive regular smoking cessation training program messages by the professional team to 8 weeks and follow to 52 weeks. A hard copy of the manual will be sent to each provider from the intervention group by mail after randomization. The Control group will only communicate by themselves and receive thanks messages for 8 weeks, and follow-up to 52 weeks. The trial will be carried out in two phases. The first phase is the pilot study (n=200, 8-week intervention and follow-up to 16 weeks) and the second is the main study (n=2000, 8-week intervention and follow-up to 52 weeks). The primary outcome measure will be the utilization rate of behavioural and pharmacotherapy interventions for smoking patients from 8 to 52 weeks. This trial is registered at ClinicalTrials.gov (number NCT03556774).ConclusionsThis program will be the first evidence-based educational program in smoking cessation designed specifically for the improvement of Chinese HSPs’ utilization of behavioural and pharmacotherapy interventions for cigarette smoking cessation in health care settings by the ‘WeChat WeQuit’ program.ImplicationsThis protocol may show that ‘WeChat WeQuit’ training program will be effective in increasing the provision of effective tobacco cessation interventions by Chinese-speaking HSPs, especially therapists, to patients with cigarette smoking, which will provide valuable insights into bridging the gap between need and services for smoking cessation in China. Overall, we believe this program will be likely to have very substantial public health benefits if it would provide a widely accessible and efficacious smoking cessation information for Chinese HSPs.

scholarly journals Associations Between Engagement With an Online Health Community and Changes in Patient Activation and Health Care Utilization: Longitudinal Web-Based Survey

10.2196/13477 ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. e13477 ◽  
Author(s):  
Ruth E Costello ◽  
Amrutha Anand ◽  
Matt Jameson Evans ◽  
William G Dixon
Keyword(s):  
Health Care ◽  
Health Status ◽  
Health Care Utilization ◽  
Patient Activation ◽  
Care Utilization ◽  
Chi Square ◽  
Web Based ◽  
Health Communities ◽  
Very High

Background Participation in online health communities (OHCs) is a popular trend in the United Kingdom. However, so far, no evidence exists to indicate an association between participation in OHCs and improved health outcomes. Objective This study aimed to (1) determine changes in patient activation over 3 months in new users of an OHC, (2) describe patterns of engagement with an OHC, (3) examine whether patients’ characteristics at baseline were associated with subsequent patterns of engagement, and (4) determine if patterns of engagement during the 3 months were associated with changes in patient activation, health care utilization, and health status. Methods Active new OHC users on HealthUnlocked (HU) were surveyed to measure demographics, levels of patient activation (describing a person’s confidence in managing their own health; scale 0-100 with 4 categories), health care utilization, and health status using a Web-based survey at baseline and 3 months. Patient activation at baseline and 3 months was compared (aim 1). Alongside, for a sample of HU users and survey responders, daily OHC website usage data were automatically captured. This was used to identify clusters of engagement with HU (aim 2). For survey responders, baseline characteristics, patient activation, health care utilization, and health status were compared at baseline and 3 months, overall, and between engagement clusters using t tests and chi-square tests (aims 3 and 4). Results In 329 people who completed both surveys, baseline activation was most frequently level 3, described as taking action but still lacking confidence. At follow-up, a change of 2.6 points was seen, with the greatest change seen in those at lowest baseline activation levels. In addition, 4 clusters of engagement were identified: low, medium, high, and very high, who were active on HU for a mean of 4, 12, 29, and 59 days, respectively. Survey responders were more commonly high or very high engagers. Baseline activation was highest in low and very high engagers. Overall activation increased over time in all engagement groups. Very high engagers had the greatest improvement in activation (5 points), although the average change was not above what is considered clinically meaningful for any group. Fewer accident and emergency visits were seen at follow-up in those with higher engagement, although this trend was not seen for other health care utilization measures. There was no change in health status at 3 months. Conclusions This observational study provides some insight into how patterns of engagement with OHCs are associated with changes in patient activation, health care utilization, and health status. Over 3 months, overall, the change in activation was not clinically significant, and there were some indications that OHCs may be of benefit to particular groups. However, the study limitations prevent firm conclusions about causal relationships.

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