Satisfaction with care and adherence to treatment when using patient reported outcomes to individualize follow-up care for women with early breast cancer - a pilot randomized controlled trial.

Background: The population of breast cancer survivors is increasing as a positive consequence of early detection and enhanced treatment. The disease and treatment associated side-effects or late-effects often impact on quality of life and daily life functions during survivorship. This calls for optimization of follow-up care. We aimed to evaluate the patients' satisfaction with the care provided, when using electronic patient reported outcomes (ePROs) to individualize follow-up care in women with early breast cancer receiving adjuvant endocrine therapy.Material and methods: Postmenopausal women treated for hormone receptor positive early breast cancer were included in a pilot randomized controlled trial and randomized to receive standard follow-up care with prescheduled consultations every six months or individualized follow-up care with the active use of ePROs to screen for the need of consultations. ePROs were distributed every third month over a two-year period. Primary outcomes were satisfaction with the assigned follow-up care and unmet needs. Secondary outcomes were use of consultations, adherence to treatment and quality of life.Results: Of the 207 consecutive patients who were potentially eligible for the study, 134 women were enrolled (65%). In total 64 women in standard care and 60 women in individualized care were analyzed. No statistically significant differences were reported in relation to satisfaction, unmet needs, adherence to treatment or quality of life. Women in standard care attended twice as many consultations during the two year follow-up period as women in individualized care; 4.3 (95% CI 3.9-4.7) versus 2.1 (95% CI: 1.6-2.6), p < .001.Conclusion: A significant reduction in consultations was observed for the group attending individualized care without compromising the patients' satisfaction, quality of life or adherence to treatment. For the majority of postmenopausal women treated for early breast cancer, implementation of ePROs to individualize follow-up care was feasible.

Use of PROM during follow-up of patients with ovarian cancer: the PROMova study protocol.

BACKGROUND: There is a paucity of high-level evidence on the optimal follow-up of patients with ovarian cancer after primary treatment. A debate is ongoing on the extent to which follow-up should consider patient preferences and patient-reported outcome measures. Incorporation of patient-reported outcome measures supports the dialog between patient and clinician and may be instrumental in symptom monitoring and detection of underlying issues, especially when used actively during the clinical consultation. PRIMARY OBJECTIVE AND ENDPOINT: The PROMova study aims to assess whether proactive use of patient-reported outcome measures during follow-up care increases patient involvement as perceived by the patient compared with standard care. Another objective is to measure satisfaction with the care provided. STUDY HYPOTHESIS: It is hypothesized that proactive use of patient-reported outcome measures during the clinical encounter will improve patients' experience of involvement in follow-up care. TRIAL DESIGN: PROMova is a multi-center, observational cohort study collecting data from eight departments in Denmark. Five departments use the patient-reported outcome measures proactively during the consultation and three provide standard care. Participants are followed up with patient-reported outcome measures for up to 3 years. The patient-reported outcome measures package comprises EORTC QLQ-C30, EORTC QLQ-OV28, a questionnaire screening tool for recurrence, CollaboRATE, and selected questions from the Patient Experience Questionnaire. MAJOR INCLUSION/EXCLUSION CRITERIA: Patients older than 18 years diagnosed with ovarian, fallopian tube, or primary peritoneal cancer are eligible when entering the follow-up program after primary treatment. Participants must be able to speak and read Danish. SAMPLE SIZE: 223 patients with ovarian cancer. ESTIMATED DATES FOR COMPLETING ACCRUAL AND PRESENTING RESULTS: The protocol closed for enrollment in 2019. Publication of final results is expected in spring 2022. TRIAL REGISTRATION: PROMova was registered with ClinicalTrials.gov November 2016 Identifier: NCT02916875.

Shared decision making when patients consider surgery for lumbar herniated disc: development and test of a patient decision aid.

BACKGROUND: Shared decision making (SDM) is a systematic approach aimed at improving patient involvement in preference-sensitive health care decisions. Choosing between surgical or non-surgical treatment for lumbar disc herniation, can be difficult as the evidence of a superior treatment is unclear, which makes it a preference-sensitive decision. The objectives of this study was therefore to assess the degree of SDM and afterwards to develop and test a patient decision aid (PtDA) to support SDM during the clinical encounter between surgeon and patient, when patients choose between surgical and non-surgical treatment for Lumbar disc herniation (LDH). METHODS: The study was conducted in four steps. 1) Assessment of the extent to which SDM was practiced in the spine clinic. 2) Development of a PtDA to support SDM. 3) Testing its usability and acceptability amongst potential users (patients). 4) Pilot-test of its usability in the clinical setting. RESULTS: Results from our small baseline study (n = 40) showed that between a third and two-thirds of the patients reported not being fully engaged in a shared decision. A pre-designed template (BESLUTNINGSHJÆLPER™) was adapted to support the decision about whether or not to have surgery for LDH. Testing the prototype with patients led to minor refinements. A subsequent pilot test of its usability in a clinical setting achieved positive responses from both patients and clinicians. CONCLUSION: Our baseline study demonstrated that SDM was not universally practiced in the clinic. The PtDA we have developed was rated as acceptable and usable by both patients and clinicians for helping those with LDH choose between surgical or non- surgical treatment. This tool now requires further testing to assess its effectiveness.

Connecting patient experience, leadership, and the importance of involvement, information, and empathy in the care process.

To improve health outcomes, restore trust, and create a safe and healing environment for patients, the health system needs to shift from being disease, system, and provider focused to being patient centred. Drawing from a patient story, this article focuses on three aspects of the care process that have a significant impact on patient experience: involvement in care, information about treatment and care, and empathy and respect. It will also provide recommendations for leaders in how to become more patient centred and aligned to the LEADS competency framework.

Personalised care planning for adults with chronic or long-term health conditions.

BACKGROUND: Personalised care planning is a collaborative process used in chronic condition management in which patients and clinicians identify and discuss problems caused by or related to the patient's condition, and develop a plan for tackling these. In essence it is a conversation, or series of conversations, in which they jointly agree goals and actions for managing the patient's condition. OBJECTIVES: To assess the effects of personalised care planning for adults with long-term health conditions compared to usual care (i.e. forms of care in which active involvement of patients in treatment and management decisions is not explicitly attempted or achieved). SEARCH METHODS: We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, PsycINFO, ProQuest, clinicaltrials.gov and WHO International Clinical Trials Registry Platform to July 2013. SELECTION CRITERIA: We included randomised controlled trials and cluster-randomised trials involving adults with long-term conditions where the intervention included collaborative (between individual patients and clinicians) goal setting and action planning. We excluded studies where there was little or no opportunity for the patient to have meaningful influence on goal selection, choice of treatment or support package, or both. DATA COLLECTION AND ANALYSIS: Two of three review authors independently screened citations for inclusion, extracted data, and assessed risk of bias. The primary outcomes were effects on physical health, psychological health, subjective health status, and capabilities for self management. Secondary outcomes included effects on health-related behaviours, resource use and costs, and type of intervention. A patient advisory group of people with experience of living with long-term conditions advised on various aspects of the review, including the protocol, selection of outcome measures and emerging findings. MAIN RESULTS: We included 19 studies involving a total of 10,856 participants. Twelve of these studies focused on diabetes, three on mental health, one on heart failure, one on end-stage renal disease, one on asthma, and one on various chronic conditions. All 19 studies included components that were intended to support behaviour change among patients, involving either face-to-face or telephone support. All but three of the personalised care planning interventions took place in primary care or community settings; the remaining three were located in hospital clinics. There was some concern about risk of bias for each of the included studies in respect of one or more criteria, usually due to inadequate or unclear descriptions of research methods. Physical healthNine studies measured glycated haemoglobin (HbA1c), giving a combined mean difference (MD) between intervention and control of -0.24% (95% confidence interval (CI) -0.35 to -0.14), a small positive effect in favour of personalised care planning compared to usual care (moderate quality evidence).Six studies measured systolic blood pressure, a combined mean difference of -2.64 mm/Hg (95% CI -4.47 to -0.82) favouring personalised care (moderate quality evidence). The pooled results from four studies showed no significant effect on diastolic blood pressure, MD -0.71 mm/Hg (95% CI -2.26 to 0.84).We found no evidence of an effect on cholesterol (LDL-C), standardised mean difference (SMD) 0.01 (95% CI -0.09 to 0.11) (five studies) or body mass index, MD -0.11 (95% CI -0.35 to 0.13) (four studies).A single study of people with asthma reported that personalised care planning led to improvements in lung function and asthma control. Psychological healthSix studies measured depression. We were able to pool results from five of these, giving an SMD of -0.36 (95% CI -0.52 to -0.20), a small effect in favour of personalised care (moderate quality evidence). The remaining study found greater improvement in the control group than the intervention group.Four other studies used a variety of psychological measures that were conceptually different so could not be pooled. Of these, three found greater improvement for the personalised care group than the usual care group and one was too small to detect differences in outcomes. Subjective health statusTen studies used various patient-reported measures of health status (or health-related quality of life), including both generic health status measures and condition-specific ones. We were able to pool data from three studies that used the SF-36 or SF-12, but found no effect on the physical component summary score SMD 0.16 (95% CI -0.05 to 0.38) or the mental component summary score SMD 0.07 (95% CI -0.15 to 0.28) (moderate quality evidence). Of the three other studies that measured generic health status, two found improvements related to personalised care and one did not.Four studies measured condition-specific health status. The combined results showed no difference between the intervention and control groups, SMD -0.01 (95% CI -0.11 to 0.10) (moderate quality evidence). Self-management capabilitiesNine studies looked at the effect of personalised care on self-management capabilities using a variety of outcome measures, but they focused primarily on self efficacy. We were able to pool results from five studies that measured self efficacy, giving a small positive result in favour of personalised care planning: SMD 0.25 (95% CI 0.07 to 0.43) (moderate quality evidence).A further five studies measured other attributes that contribute to self-management capabilities. The results from these were mixed: two studies found evidence of an effect on patient activation, one found an effect on empowerment, and one found improvements in perceived interpersonal support. Other outcomesPooled data from five studies on exercise levels showed no effect due to personalised care planning, but there was a positive effect on people's self-reported ability to carry out self-care activities: SMD 0.35 (95% CI 0.17 to 0.52).We found no evidence of adverse effects due to personalised care planning.The effects of personalised care planning were greater when more stages of the care planning cycle were completed, when contacts between patients and health professionals were more frequent, and when the patient's usual clinician was involved in the process. AUTHORS' CONCLUSIONS: Personalised care planning leads to improvements in certain indicators of physical and psychological health status, and people's capability to self-manage their condition when compared to usual care. The effects are not large, but they appear greater when the intervention is more comprehensive, more intensive, and better integrated into routine care.

People with limiting long-term conditions report poorer experiences and more problems with hospital care.

BACKGROUND: Long-term conditions have a significant impact on individuals, their families, and the health service. As people with these conditions represent a high proportion of hospital admissions, investigating their experiences of inpatient care has become an important area of investigation. We conducted a secondary analysis of the NHS adult inpatient survey for England to compare the hospital experiences of three groups of patients: those without long-term conditions, those with a single long-term condition, and those with multiple long-term conditions. We were particularly interested in the extent to which these patients received self-management support from hospital staff, so we developed a brief summary tool drawn from salient questions in the survey to aid the comparison. METHODS: Analysis of data from the 2011 national adult inpatient survey (n = 65,134) to compare the experiences of three groups of patients: those with no limiting long-term conditions (No-LLTC), those with one limiting long-term condition (S-LLTC), and those with two or more limiting long-term conditions (M-LLTC). The main outcome measure was patients' self-reports of their experience of inpatient care, including staff-patient interactions, information provision, involvement in decisions and support for self-care and overall ratings of care. A short form scale, the Oxford Patient Involvement and Experience scale (OxPIE) was developed from the adult inpatient survey and used to compare the groups using logistic regression. RESULTS: There were significant differences between the No-LLTC group in comparison to both the S-LLTC and M-LLTC groups. Patients with limiting long-term conditions reported significantly worse hospital experiences than those without, as measured by OxPIE: S-LLTC odds ratio = 1.23, 95% CI 1.03-1.48; M-LLTC odds ratio = 1.64, 95% CI 1.19-2.26. Responses to a single global rating question were more positive but not strongly correlated with OxPIE. CONCLUSIONS: Patients with LLTCs were more critical of their inpatient care than those with no LLTCs. Those with more than one long-term condition reported worse experiences than those with a single limiting condition. Simple rating questions may not be sufficiently sensitive to reflect important aspects of patients' experience.

Item generation in the development of an inpatient experience questionnaire: a qualitative study.

BACKGROUND: Patient experience is a key feature of quality improvement in modern health-care delivery. Measuring patient experience is one of several tools used to assess and monitor the quality of health services. This study aims to develop a tool for assessing patient experience with inpatient care in public hospitals in Hong Kong. METHODS: Based on the General Inpatient Questionnaire (GIQ) framework of the Care Quality Commission as a discussion guide, a qualitative study involving focus group discussions and in-depth individual interviews with patients was employed to develop a tool for measuring inpatient experience in Hong Kong. RESULTS: All participants agreed that a patient satisfaction survey is an important platform for collecting patients' views on improving the quality of health-care services. Findings of the focus group discussions and in-depth individual interviews identified nine key themes as important hospital quality indicators: prompt access, information provision, care and involvement in decision making, physical and emotional needs, coordination of care, respect and privacy, environment and facilities, handling of patient feedback, and overall care from health-care professionals and quality of care. Privacy, complaint mechanisms, patient involvement, and information provision were further highlighted as particularly important areas for item revision by the in-depth individual interviews. Thus, the initial version of the Hong Kong Inpatient Experience Questionnaire (HKIEQ), comprising 58 core items under nine themes, was developed. CONCLUSIONS: A set of dimensions and core items of the HKIEQ was developed and the instrument will undergo validity and reliability tests through a validation survey. A valid and reliable tool is important in accurately assessing patient experience with care delivery in hospitals to improve the quality of health-care services.

Validation of inpatient experience questionnaire.

OBJECTIVE: A validation survey was carried out in this study to assess the acceptability, validity and reliability of the Hong Kong Inpatient Experience Questionnaire (HKIEQ), which was newly developed to measure patient experiences of hospital care in Hong Kong (HK). DESIGN: Cross-sectional validation survey. MAIN OUTCOME MEASURES: Principal component exploratory factor analysis assessed the construct validity of the questionnaire. Cronbach's alpha coefficients and Spearman's rank correlation coefficient estimated the reliability of the instrument. Acceptability of the questionnaire regarding the percentage of missing value of individual items was also assessed. RESULTS: A total of 511 patients discharged from public hospitals in HK were interviewed. Low percentage of missing value (0.2 to 21.3%) showed high acceptability. Nine dimensions of hospital care explaining 75.4% of the variance were derived from factor analysis and content validity. These items showed satisfactory internal reliability consistency (0.49 to 0.97). Test-retest reliability ranged from 0.36 to 0.96. CONCLUSIONS: The HKIEQ performed well on several psychometric indicators and is a promising measure of patient experience with public hospital inpatient care in HK. The findings provided important insight on developing tools to measure patient experience in hospitals to improve the quality of care and to lay the foundation for further research on patient expectations and needs regarding hospitalization.

A systematic development process for patient decision aids.

BACKGROUND: The original version of the International Patient Decision Aid Standards (IPDAS) recommended that patient decision aids (PtDAs) should be carefully developed, user-tested and open to scrutiny, with a well-documented and systematically applied development process. We carried out a review to check the relevance and scope of this quality dimension and, if necessary, to update it. METHODS: Our review drew on three sources: a) published papers describing PtDAs evaluated in randomised controlled trials and included in the most recent Cochrane Collaboration review; b) linked papers cited in the trial reports that described how the PtDAs had been developed; and c) papers and web reports outlining the development process used by organisations experienced in developing multiple PtDAs. We then developed an extended model of the development process indicating the various steps on which documentation is required, as well as a checklist to assess the frequency with which each of the elements was publicly reported. RESULTS: Key features common to all patient decision aid (PtDA) development processes include: scoping and design; development of a prototype; 'alpha' testing with patients and clinicians in an iterative process; 'beta' testing in 'real life' conditions (field tests); and production of a final version for use and/or further evaluation. Only about half of the published reports on the development of PtDAs that we reviewed appear to have been field tested with patients, and even fewer had been reviewed or tested by clinicians not involved in the development process. Very few described a distribution strategy, and surprisingly few (17%) described a method for reviewing and synthesizing the clinical evidence. We describe a model development process that includes all the original elements of the original IPDAS criterion, expanded to include consideration of format and distribution plans as well as prototype development. CONCLUSIONS: The case for including each of the elements outlined in our model development process is pragmatic rather than evidence-based. Optimal methods for ensuring that each stage of the process is carried out effectively require further development and testing.

Patient experiences with public hospital care: first benchmark survey in Hong Kong.

OBJECTIVE. To measure patient satisfaction in relation to in-patient experience in public hospitals. DESIGN. Cross-sectional study. SETTING. Twenty-five selected Hospital Authority acute and convalescence hospitals in Hong Kong. PARTICIPANTS. Eligible patients discharged between 15 June and 27 September 2010 from the selected Hospital Authority public hospitals. MAIN OUTCOME MEASURES. A total of 54 items were used to measure patient experience on aspects of hospital care. They included the process of admission to hospital, staying in the hospital and ward (environment, food and facilities; hospital staff; patient care and treatment), the process of leaving hospital, and the overall impression of hospital care. Free-text comments from respondents were also recorded. RESULTS. A total of 5030 patients were successfully interviewed, amounting to a response rate of 52%. The findings showed that 80% (confidence interval, 79-81%) of patients rated the care they received in hospital as good or better. However, there were a few areas where performance was relatively low, including waiting time for a ward bed for accident and emergency cases, food quality, infection control, information provided about their condition/treatment, seeking patient input about their opinions and quality of care, and patient engagement in the decisions about their treatment and care, as well as the discharge process. CONCLUSIONS. This patient experience survey used a validated instrument (Hong Kong Inpatient Experience Questionnaire) to provide important insights to executives and health care professionals on their care to patients and to identify areas for improvement in public hospitals. Further surveys should be carried out to monitor changes in patient experience and satisfaction on a regular basis. Such surveys could facilitate improvements through analysis of results on patient satisfaction.

Implementing shared decision-making in UK: Progress 2017-2022.

Shared decision making has been on the policy agenda in the UK for at least twelve years, but it lacked a comprehensive approach to delivery. That has changed over the past five years, and we can now see significant progress across all aspects of a comprehensive approach, including leadership at policy, professional and patient levels; infrastructure developments, including the provision of training, tools and campaigns; and practice improvements, such as demonstrations, measurement and coordination. All these initiatives were necessary, but the last, central coordination, would appear to be key to success.

ePRO-based individual follow-up care for women treated for early breast cancer: impact on service use and workflows.

PURPOSE: The increasing population of breast cancer survivors highlights the need to (re)consider how we utilize available services for survivorship care in oncology clinics. Electronic Patient-Reported Outcomes (ePROs) can be used to identify patients' individual care needs and triage them to the right services. We examined the impact on service use, workflow and workload following the introduction of an ePRO-based individual follow-up (PIFU) for women treated for early breast cancer. METHODS: A multi-method approach was used. In a pilot randomized controlled trial, the use of consultations, telephone calls, and specialist referrals were systematically recorded. Comparison was done between PIFU and standard follow-up care (SFU). Focus group interviews with nurse navigators evaluated the impact on workflow and workload qualitatively. RESULTS: The 64 women randomized to attend SFU used a mean of 3.8 (95% CI: 3.5-4.1) planned consultations during the 2-year study period compared with a mean of 1.9 consultations (95% CI: 1.4-2.4) for the 60 women randomized to PIFU (P < 0.001). Urgent appointments were more frequent in SFU (mean of 0.47 vs 0.22 per patient, P = 0.03). No statistically significant differences were observed in the use of telephone calls and specialist referrals. The nurse navigators did not experience an increase in their workload, but implementation of PIFU may require a re-structured workflow. CONCLUSIONS: The ePRO-based individual follow-up could change organization of care and re-allocate services for those in need of it. IMPLICATIONS FOR CANCER SURVIVORS: ePRO-based individual follow-up could potentially ensure more time for those most in need of face-to-face care.

What Works in Implementing Patient Decision Aids in Routine Clinical Settings? A Rapid Realist Review and Update from the International Patient Decision Aid Standards Collaboration.

BACKGROUND: Decades of effectiveness research has established the benefits of using patient decision aids (PtDAs), yet broad clinical implementation has not yet occurred. Evidence to date is mainly derived from highly controlled settings; if clinicians and health care organizations are expected to embed PtDAs as a means to support person-centered care, we need to better understand what this might look like outside of a research setting. AIM: This review was conducted in response to the IPDAS Collaboration's evidence update process, which informs their published standards for PtDA quality and effectiveness. The aim was to develop context-specific program theories that explain why and how PtDAs are successfully implemented in routine healthcare settings. METHODS: Rapid realist review methodology was used to identify articles that could contribute to theory development. We engaged key experts and stakeholders to identify key sources; this was supplemented by electronic database (Medline and CINAHL), gray literature, and forward/backward search strategies. Initial theories were refined to develop realist context-mechanism-outcome configurations, and these were mapped to the Consolidated Framework for Implementation Research. RESULTS: We developed 8 refined theories, using data from 23 implementation studies (29 articles), to describe the mechanisms by which PtDAs become successfully implemented into routine clinical settings. Recommended implementation strategies derived from the program theory include 1) co-production of PtDA content and processes (or local adaptation), 2) training the entire team, 3) preparing and prompting patients to engage, 4) senior-level buy-in, and 5) measuring to improve. CONCLUSIONS: We recommend key strategies that organizations and individuals intending to embed PtDAs routinely can use as a practical guide. Further work is needed to understand the importance of context in the success of different implementation studies.

Qualitative and quantitative approach to assess of the potential for automating administrative tasks in general practice.

OBJECTIVE: To identify the extent to which administrative tasks carried out by primary care staff in general practice could be automated. DESIGN: A mixed-method design including ethnographic case studies, focus groups, interviews and an online survey of automation experts. SETTING: Three urban and three rural general practice health centres in England selected for differences in list size and organisational characteristics. PARTICIPANTS: Observation and interviews with 65 primary care staff in the following job roles: administrator, manager, general practitioner, healthcare assistant, nurse practitioner, pharmacy technician, phlebotomist, practice nurse, pharmacist, prescription clerk, receptionist, scanning clerk, secretary and medical summariser; together with a survey of 156 experts in automation technologies. METHODS: 330 hours of ethnographic observation and documentation of administrative tasks carried out by staff in each of the above job roles, followed by coding and classification; semistructured interviews with 10 general practitioners and 6 staff focus groups. The online survey of machine learning, artificial intelligence and robotics experts was analysed using an ordinal Gaussian process prediction model to estimate the automatability of the observed tasks. RESULTS: The model predicted that roughly 44% of administrative tasks carried out by staff in general practice are 'mostly' or 'completely' automatable using currently available technology. Discussions with practice staff underlined the need for a cautious approach to implementation. CONCLUSIONS: There is considerable potential to extend the use of automation in primary care, but this will require careful implementation and ongoing evaluation.

The Future of Health Care: Protocol for Measuring the Potential of Task Automation Grounded in the National Health Service Primary Care System.

BACKGROUND: Recent advances in technology have reopened an old debate on which sectors will be most affected by automation. This debate is ill served by the current lack of detailed data on the exact capabilities of new machines and how they are influencing work. Although recent debates about the future of jobs have focused on whether they are at risk of automation, our research focuses on a more fine-grained and transparent method to model task automation and specifically focus on the domain of primary health care. OBJECTIVE: This protocol describes a new wave of intelligent automation, focusing on the specific pressures faced by primary care within the National Health Service (NHS) in England. These pressures include staff shortages, increased service demand, and reduced budgets. A critical part of the problem we propose to address is a formal framework for measuring automation, which is lacking in the literature. The health care domain offers a further challenge in measuring automation because of a general lack of detailed, health care-specific occupation and task observational data to provide good insights on this misunderstood topic. METHODS: This project utilizes a multimethod research design comprising two phases: a qualitative observational phase and a quantitative data analysis phase; each phase addresses one of the two project aims. Our first aim is to address the lack of task data by collecting high-quality, detailed task-specific data from UK primary health care practices. This phase employs ethnography, observation, interviews, document collection, and focus groups. The second aim is to propose a formal machine learning approach for probabilistic inference of task- and occupation-level automation to gain valuable insights. Sensitivity analysis is then used to present the occupational attributes that increase/decrease automatability most, which is vital for establishing effective training and staffing policy. RESULTS: Our detailed fieldwork includes observing and documenting 16 unique occupations and performing over 130 tasks across six primary care centers. Preliminary results on the current state of automation and the potential for further automation in primary care are discussed. Our initial findings are that tasks are often shared amongst staff and can include convoluted workflows that often vary between practices. The single most used technology in primary health care is the desktop computer. In addition, we have conducted a large-scale survey of over 156 machine learning and robotics experts to assess what tasks are susceptible to automation, given the state-of-the-art technology available today. Further results and detailed analysis will be published toward the end of the project in early 2019. CONCLUSIONS: We believe our analysis will identify many tasks currently performed manually within primary care that can be automated using currently available technology. Given the proper implementation of such automating technologies, we expect considerable staff resources to be saved, alleviating some pressures on the NHS primary care staff. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/11232.

Are patient-reported outcomes useful in post-treatment follow-up care for women with early breast cancer? A scoping review.

BACKGROUND: Patient-reported outcomes (PROs) are frequently used to evaluate treatment effects and quality of life in clinical trials. The application of PROs in breast cancer clinics is evolving but their use to generate real-time information for use in follow-up care is uncommon. This proactive use might help to shift healthcare delivery toward a more patient-centered approach by acting as a screening tool for unmet needs or a dialogue tool to discuss issues proposed by the patient. AIMS: This review aims to determine the effects and feasibility of using PROs proactively during follow-up care in early breast cancer. MATERIALS AND METHODS: A systematic search was conducted in January 2019 in PubMed, Cochrane Library, Embase, and CINAHL. Studies that exclusively concerned women treated for early breast cancer where PROs were used as a proactive tool during follow-up were included. RESULTS: The search revealed a total of 653 records and four eligible studies were identified; three of which concerned the use of PROs both as a screening tool and as a dialogue tool, and one study in which PROs were used solely as a screening tool. The studies explored the feasibility of collecting and integrating PROs in the clinic and their ability to detect otherwise unrecognized problems. All of the included studies were prone to bias, but they point to potential benefits in respect of better symptom management in follow-up care. CONCLUSION: Our search identified a small number of low to moderate quality studies of the proactive use of PROs during follow-up after treatment for early stage breast cancer. The limited evidence available suggests that PROs may be useful for providing a more complete picture of the patient's symptoms and problems, possibly leading to improvements in symptom management.