ImportanceInternational efforts are being made towards a person-centred care (PCC) model, but there are currently no standardised mechanisms to measure and monitor PCC at a healthcare system level. The use of metrics to measure PCC can help to drive the changes needed to improve the quality of healthcare that is person centred.ObjectiveTo develop and validate person-centred care quality indicators (PC-QIs) measuring PCC at a healthcare system level through a synthesis of the evidence and a person-centred consensus approach to ensure the PC-QIs reflect what matters most to people in their care.MethodsExisting indicators were first identified through a scoping review of the literature and an international environmental scan. Focus group discussions with diverse patients and caregivers and interviews with clinicians and experts in quality improvement allowed us to identify gaps in current measurement of PCC and inform the development of new PC-QIs. A set of identified and newly developed PC-QIs were subsequently refined by Delphi consensus process using a modified RAND/UCLA Appropriateness Method. The international consensus panel consisted of patients, family members, community representatives, clinicians, researchers and healthcare quality experts.ResultsFrom an initial 39 unique evidence-based PC-QIs identified and developed, the consensus process yielded 26 final PC-QIs. These included 7 related to structure, 16 related to process, 2 related to outcome and 1 overall global PC-QI.ConclusionsThe final 26 evidence-based and person-informed PC-QIs can be used to measure and evaluate quality incorporating patient perspectives, empowering jurisdictions to monitor healthcare system performance and evaluate policy and practice related to PCC.

Albertans4HealthResearch, supported by the Alberta Strategy for Patient-Oriented Research Patient Engagement Team, hosted a virtual round table discussion to develop a list of considerations for successful partnerships in patient-oriented research. The group, which consists of active patient partners across the Canadian province of Alberta and some research staff engaged in patient-oriented research, considered advice for academic researchers on how to best partner with patients and community members on health research projects. The group identified four main themes, aligned with the national strategy for patient-oriented research (SPOR) patient engagement framework, highlighting important considerations for researchers from the patient perspective, providing practical ways to implement SPOR’s key principles: inclusiveness, support, mutual respect, and co-building. This commentary considers the process behind this engagement exercise and offers advice directly from active patient research partners on how to fulfill the operational patient engagement mandate. Academic research teams can use this guidance when considering how to work together with patient partners and community members.

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Objective: Sleep disturbance is a key contributor to posthospital syndrome; a transient period of vulnerability following discharge from hospital. We sought to examine the relationship between patient-reported hospital quietness at night, via a validated survey, and unplanned hospital readmissions among hospitalized seniors (ages 65 and older) in Alberta, Canada. Design: Retrospective, cross-sectional analysis of survey responses, linked with administrative inpatient records. Setting: Using the Canadian Patient Experiences Survey—Inpatient Care and Discharge Abstract Database, patients aged 65 and older, and living with one or more chronic conditions were identified. Participants: Of all, 25 674 respondents discharged from hospital between April 2014 and December 2017. Main Outcome Measure: All-cause, unplanned readmission within 30 or 90 days (yes vs no). Results: Approximately half (50.5%) of the respondents reported that the area around their room was always quiet at night. Eight (8.1%) percent of respondents (2066) were readmitted within 30 days (2241 total readmissions), while 15.6% (4000) were readmitted within 90 days (5070 total readmissions). When controlling for a variety of demographic and clinical factors, patients not reporting “always” to the survey question regarding hospital quietness at night had slightly greater odds of readmission within 30 (adjusted odds ratio [aOR] = 1.32, 95% confidence interval [CI]: 1.20-1.45) and 90 days (aOR = 1.14, 95% CI: 1.06-1.23). Conclusion: Our results demonstrate a clear association between patient-reported hospital quietness at night and subsequent readmission within the first 30 and 90 days following discharge. Efforts to minimize hospital noise, particularly at night, may help promote a restful environment, while reducing readmissions among older patients living with chronic conditions.

ObjectivesPatient feedback is critical to identify and resolve patient safety and experience issues in healthcare systems. However, large volumes of unstructured text data can pose problems for manual (human) analysis. This study reports the results of using a semiautomated, computational topic-modelling approach to analyse a corpus of patient feedback.MethodsPatient concerns were received by Alberta Health Services between 2011 and 2018 (n=76 163), regarding 806 care facilities in 163 municipalities, including hospitals, clinics, community care centres and retirement homes, in a province of 4.4 million. Their existing framework requires manual labelling of pre-defined categories. We applied an automated latent Dirichlet allocation (LDA)-based topic modelling algorithm to identify the topics present in these concerns, and thereby produce a framework-free categorisation.ResultsThe LDA model produced 40 topics which, following manual interpretation by researchers, were reduced to 28 coherent topics. The most frequent topics identified were communication issues causing delays (frequency: 10.58%), community care for elderly patients (8.82%), interactions with nurses (8.80%) and emergency department care (7.52%). Many patient concerns were categorised into multiple topics. Some were more specific versions of categories from the existing framework (eg, communication issues causing delays), while others were novel (eg, smoking in inappropriate settings).DiscussionLDA-generated topics were more nuanced than the manually labelled categories. For example, LDA found that concerns with community care were related to concerns about nursing for seniors, providing opportunities for insight and action.ConclusionOur findings outline the range of concerns patients share in a large health system and demonstrate the usefulness of using LDA to identify categories of patient concerns.