BackgroundGlobally, 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.MethodsBased 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.DiscussionThe 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.ConclusionThis conceptual framework provides a step‐wise roadmap to guide health‐care systems and organizations in the provision PCC across various health‐care sectors.

IntroductionAdministrative health data have been used to study sepsis in large population-based studies. The validity of these study findings depends largely on the quality of the administrative data source and the validity of the case definition used. We systematically reviewed the literature to assess the validity of case definitions of sepsis used with administrative data.MethodsEmbase and MEDLINE were searched for published articles with International Classification of Diseases (ICD) coded data used to define sepsis. Abstracts and full-text articles were reviewed in duplicate. Data were abstracted from all eligible full-text articles, including ICD-9- and/or ICD-10-based case definitions, sensitivity (Sn), specificity (Sp), positive predictive value (PPV) and negative predictive value (NPV).ResultsOf 2,317 individual studies identified, 12 full-text articles met all eligibility criteria. A total of 38 sepsis case definitions were tested, which included over 130 different ICD codes. The most common ICD-9 codes were 038.x, 790.7 and 995.92, and the most common ICD-10 codes were A40.x and A41.x. The PPV was reported in ten studies and ranged from 5.6% to 100%, with a median of 50%. Other tests of diagnostic accuracy were reported only in some studies. Sn ranged from 5.9% to 82.3%; Sp ranged from 78.3% to 100%; and NPV ranged from 62.1% to 99.7%.ConclusionsThe validity of administrative data in recording sepsis varied substantially across individual studies and ICD definitions. Our work may serve as a reference point for consensus towards an improved and harmonized ICD-coded definition of sepsis.Electronic supplementary materialThe online version of this article (doi:10.1186/s13054-015-0847-3) contains supplementary material, which is available to authorized users.

ObjectiveAdministrative health data are important for health services and outcomes research. We optimised and validated in intensive care unit (ICU) patients an International Classification of Disease (ICD)-coded case definition for sepsis, and compared this with an existing definition. We also assessed the definition's performance in non-ICU (ward) patients.Setting and participantsAll adults (aged ≥18 years) admitted to a multisystem ICU with general medicosurgical ICU care from one of three tertiary care centres in the Calgary region in Alberta, Canada, between 1 January 2009 and 31 December 2012 were included.Research designPatient medical records were randomly selected and linked to the discharge abstract database. In ICU patients, we validated the Canadian Institute for Health Information (CIHI) ICD-10-CA (Canadian Revision)-coded definition for sepsis and severe sepsis against a reference standard medical chart review, and optimised this algorithm through examination of other conditions apparent in sepsis.MeasuresSensitivity (Sn), specificity (Sp), positive predictive value (PPV) and negative predictive value (NPV) were calculated.ResultsSepsis was present in 604 of 1001 ICU patients (60.4%). The CIHI ICD-10-CA-coded definition for sepsis had Sn (46.4%), Sp (98.7%), PPV (98.2%) and NPV (54.7%); and for severe sepsis had Sn (47.2%), Sp (97.5%), PPV (95.3%) and NPV (63.2%). The optimised ICD-coded algorithm for sepsis increased Sn by 25.5% and NPV by 11.9% with slightly lowered Sp (85.4%) and PPV (88.2%). For severe sepsis both Sn (65.1%) and NPV (70.1%) increased, while Sp (88.2%) and PPV (85.6%) decreased slightly.ConclusionsThis study demonstrates that sepsis is highly undercoded in administrative data, thus under-ascertaining the true incidence of sepsis. The optimised ICD-coded definition has a higher validity with higher Sn and should be preferentially considered if used for surveillance purposes.

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.

IntroductionThe concept of patient-centred care (PCC) is changing the way healthcare is understood, accepted and delivered. The Institute of Medicine has defined PCC as 1 of its 6 aims to improve healthcare quality. However, in Canada, there are currently no nationwide standards in place for measuring and evaluating healthcare from a patient-centred approach. In this paper, we outline our scoping review protocol to systematically review published and unpublished literature specific to patient-centred quality indicators that have been implemented and evaluated across various care settings.Methods and analysisArksey and O'Malley's scoping review methodology framework will guide the conduct of this scoping review. We will search electronic databases (MEDLINE, EMBASE, the Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO, Social Work Abstracts, Social Services Abstracts), grey literature sources and the reference lists of key studies to identify studies appropriate for inclusion. 2 reviewers will independently screen all abstracts and full-text studies for inclusion. We will include any study which focuses on quality indicators in the context of PCC. All bibliographic data, study characteristics and indicators will be collected and analysed using a tool developed through an iterative process by the research team. Indicators will be classified according to a predefined conceptual framework and categorised and described using qualitative content analysis.Ethics and disseminationThe scoping review will synthesise patient-centred quality indicators and their characteristics as described in the literature. This review will be the first step to formally identify what quality indicators have been used to evaluate PCC across the healthcare continuum, and will be used to inform a stakeholder consensus process exploring the development of a generic set of patient-centred quality indicators applicable to multiple care settings. The results will be disseminated through a peer-reviewed publication, conference presentations and a one-day stakeholder meeting.

BackgroundSepsis has a high prevalence, mortality-rate and cost. Sepsis patients usually enter the hospital through the Emergency Department (ED). Process or structural issues related to care may affect outcome.MethodsMulti-centered retrospective observational cohort study using administrative databases to identify adult patients (> = 18 years) with sepsis and severe sepsis admitted to Alberta Health Services Calgary zone adult multisystem intensive care units (ICU) through the ED between January 1, 2006 and September 30, 2009. We examined the association between ICU occupancy and hospital outcome. We explored other associations of hospital outcome including the effect of ED wait time, admission from ED during weekdays versus weekends and ED admission during the day versus at night.ResultsOne thousand and seven hundred seventy patients were admitted to hospital via ED, 1036 (58.5 %) with sepsis and 734 (41.5 %) with severe sepsis. In patients with sepsis, ICU occupancy > 90 % was associated with an increase in hospital mortality even after adjusting for age, sex, triage level, Charlson index, time of first ED physician assessment and ICU admission. No differences in hospital mortality were found for patients who waited more than 7 h, were admitted during the day versus night or weekdays versus weekends.ConclusionsIn patients with sepsis admitted via the ED, increased ICU occupancy was associated with higher in-hospital mortality.

IntroductionAdministrative health data from emergency departments play important roles in understanding health needs of the public and reasons for health care resource use. International Classification of Disease (ICD) diagnostic codes have been widely used to code reasons of clinical encounters for administrative purposes in emergency departments.

ObjectivesPrevalence, and associated morbidity and mortality of chronic sleep disorders have been limited to small cohort studies, however, administrative data may be used to provide representation of larger population estimates of disease. With no guidelines to inform the identification of cases of sleep disorders in administrative data, the objective of this study was to develop and validate a set of ICD-codes used to define sleep disorders including narcolepsy, insomnia, and obstructive sleep apnea (OSA) in administrative data. MethodsA cohort of adult patients, with medical records reviewed by two independent board-certified sleep physicians from a sleep clinic in Calgary, Alberta between January 1, 2009 and December 31, 2011, was used as the reference standard. We developed a general ICD-coded case definition for sleep disorders which included conditions of narcolepsy, insomnia, and OSA using: 1) physician claims data, 2) inpatient visit data, 3) emergency department (ED) and ambulatory care data. We linked the reference standard data and administrative data to examine the validity of different case definitions, calculating estimates of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). ResultsFrom a total of 1186 patients from the sleep clinic, 1045 (88.1%) were classified as sleep disorder positive, with 606 (51.1%) diagnosed with OSA, 407 (34.4%) with insomnia, and 59 (5.0%) with narcolepsy. The most frequently used ICD-9 codes were general codes of 307.4 (Nonorganic sleep disorder, unspecified), 780.5 (unspecified sleep disturbance) and ICD-10 codes of G47.8 (other sleep disorders), G47.9 (sleep disorder, unspecified). The best definition for identifying a sleep disorder was an ICD code (from physician claims) 2 years prior and 1 year post sleep clinic visit: sensitivity 79.2%, specificity 28.4%, PPV 89.1%, and NPV 15.6%. ICD codes from ED/ambulatory care data provided similar diagnostic performance when at least 2 codes appeared in a time period of 2 years prior and 1 year post sleep clinic visit: sensitivity 71.9%, specificity 54.6%, PPV 92.1%, and NPV 20.8%. The inpatient data yielded poor results in all tested ICD code combinations. ConclusionSleep disorders in administrative data can be identified mainly through physician claims data and with some being determined through outpatient/ambulatory care data ICD codes, however these are poorly coded within inpatient data sources. This may be a function of how sleep disorders are diagnosed and/or reported by physicians in inpatient and outpatient settings within medical records. Future work to optimize administrative data case definitions through data linkage are needed.