Skip to main content
Download PDF

Current developments in delivering customized care: a scoping review

BMC Health Services Research volume 21, Article number: 575 (2021) Cite this article

Abstract

Background

In recent years, there has been a growing interest in health care personalization and customization (i.e. personalized medicine and patient-centered care). While some positive impacts of these approaches have been reported, there has been a dearth of research on how these approaches are implemented and combined for health care delivery systems. The present study undertakes a scoping review of articles on customized care to describe which patient characteristics are used for segmenting care, and to identify the challenges face to implement customized intervention in routine care.

Methods

Article searches were initially conducted in November 2018, and updated in January 2019 and March 2019, according to Prisma guidelines. Two investigators independently searched MEDLINE, PubMed, PsycINFO, Web of Science, Science Direct and JSTOR, The search was focused on articles that included “care customization”, “personalized service and health care”, individualized care” and “targeting population” in the title or abstract. Inclusion and exclusion criteria were defined. Disagreements on study selection and data extraction were resolved by consensus and discussion between two reviewers.

Results

We identified 70 articles published between 2008 and 2019. Most of the articles (n = 43) were published from 2016 to 2019. Four categories of patient characteristics used for segmentation analysis emerged: clinical, psychosocial, service and costs. We observed these characteristics often coexisted with the most commonly described combinations, namely clinical, psychosocial and service. A small number of articles (n = 18) reported assessments on quality of care, experiences and costs. Finally, few articles (n = 6) formally defined a conceptual basis related to mass customization, whereas only half of articles used existing theories to guide their analysis or interpretation.

Conclusions

There is no common theory based strategy for providing customized care. In response, we have highlighted three areas for researchers and managers to advance the customization in health care delivery systems: better define the content of the segmentation analysis and the intervention steps, demonstrate its added value, in particular its economic viability, and align the logics of action that underpin current efforts of customization. These steps would allow them to use customization to reduce costs and improve quality of care.

Peer Review reports

Background

Increasingly, we encounter “customization”, ‘individualization”, and “personalization” terms in health care settings. Every patient wants to feel they are receiving optimized care, tailored to their particular needs, thus the traditional notion of a “doctor-patient relationship” implies customization is at the core of caregiving for health professionals [1]. Recently, customization has been combined with “personalized medicine”, which has evolved to “precision medicine” [2]. This takes the form of developed targeted therapies based on a patient’s genetic characteristics [3]. There is also a logical link between Patient-Centered Care (PCC) and care customization, where the care process considers individual patient preferences, needs and values [4, 5], often introducing personalization as a given component of the PCC package [6]. These notions have also been applied to individualized care plans, often managed by nurses [7], or to target care to the most vulnerable populations, or specific minorities, requiring a mix of clinical and social needs [8]. It is evident that many applications currently in practice or being proposed, fit under the “care customization” rubric.

However, it is unclear how these customized approaches are effectively delivered. These approaches converge on a care and service delivery model that could implement effective customized approaches into practices and organizations. These models, that are by definition, more flexible and demand-driven, could lead to better patient outcomes and experiences, and even if problematic, could reduce or avoid extra-costs [9]. But little is known about how these different approaches are effectively implemented and ultimately interacted in health care delivery systems. Some experiences remain experimental while others are disseminated without clear consensus on guidelines for implementing a customized effort in healthcare.

The research question

Our research question is to understand how current personalization efforts that rely on multiple approaches are concretely implemented in healthcare delivery systems. For example, how are clinical or psycho-sociological patient characteristics selected and taken into accounts? And how are services and care established, assembled and combined for a specific patient? The answers to these questions can help to understand the benefits of care customization in practices and organizations. Aiming to address this gap in understanding, we conducted a scoping review in order to systematically map the literature on current efforts for delivering care customization. We selected this type of review rather than a systematic review because we addressed a broad topic where many approaches may be applicable. In order to determine how current approaches are actually implemented, it is also necessary to define the components of a customized delivery model and, therefore, to have an analytical framework. The “Mass Customization” (MC) concept is a benchmark in the field of customized delivery, illustrating a demand-driven model as opposed to a supply driven model [10].

A conceptual framework for analyzing the literature

The concept of Mass Customization emphasizes the need to respond to the needs and preferences of the consumer or user, providing goods and services at or near mass production prices. Four elements must converge to generate an effective customized delivery [11,12,13]: (i) Design-based on a segmentation analysis of the user, often referred to as “customer relationship management”, where the goal is to precisely match demand to the service or product [14, 15]; (ii) Fabrication, where the system (factory, service unit or team) adapts production to meet each of those needs; (iii) Assembly, where sub-systems or “modular” service structures flexibly combine multiple standardized products and services [11, 16]; and (iv) Distribution, where coordination and integration of different products and services facilitate timely delivery to the customer [17, 18]. “Personalization” is also used in this context, and describes the employees’ attitudes, so a customer’s preference is catered for [19]. These four stages of MC define two phases of any customization delivery approach: (i) segmentation analysis of individual user characteristics, and (ii) customized intervention (i.e. fabrication, assembly and distribution), and modularity related issues [20].

The transfer of MC to the health sector must be considered carefully, because it comes up against several limitations. The objectives pursued in commercial and public health sectors are quite different. Similarly, consumer and patient needs differ accordingly. In respect of these limits, several researches have studied how to apply MC in the health care field. In their early and seminal paper on customization, Lampel and Minzberg [21] provided multiple examples related to care, stressing how much the patient’s clinical condition already lent itself to personalization (i.e. cataract surgery in a healthy person is much less customizable than a complex cardiac surgery, thus it proceeds differently). Recently, Mannion and Exworthy [22] reactivated a debate on the antagonism between standardization and customization, making service delivery homogeneous, but anonymous and customizable [23], thereby requiring different combinations of customization/standardization in care activities. Minvielle et al. [24] underscored the conditions for implementing a MC model along the care pathway. Several studies also proposed care services in a modular format, using a menu of options adapted to individual patients or unit needs, in different care contexts [25,26,27,28,29]. All these approaches converge to demonstrate how to implement MC in health care. In contrast, there are no studies that apply the concept of MC to assess current efforts to develop customized care.

The use of this concept seemed relevant to us within the framework of our research because even if the objectives are not the same, the steps of delivery of a customized service are identical from one sector to another. Thus, this conceptual framework has helped to identify how these developments address the two key-components of segmentation analysis and customized intervention. Likewise, it has also made it possible to document the characteristics of the patients for whom these approaches were used, the goals for which they were designed as well as the evidence of their added value, and the very type of customized interventions carried out.

Methods

This review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for scoping reviews [30], according to the five steps of such a review methodology: (i) identifying the research question (describe in the previous section), (ii) identifying relevant studies, (iii) study selection, (iv) charting the data, and (v) collating, summarizing and reporting the results [31, 32].

To identify potentially relevant documents (step 2), the following bibliographic databases were searched from January 2008 to March 2019: MEDLINE, Scopus, JSTOR, Web of Science, Pubmed, Psycinfo and Science Direct. This period was selected as it reflected recent/current efforts in delivering customized care. Searches were limited to English-language articles. The search was focused on articles that included “care customization”, “personalized service and health care”, individualized care” and “targeting population” in the title or abstract. These words as well as the following have been selected because they are conventionally used in the healthcare sector in the name of care customization. Following the principle of “narrowing” the topic [31, 33], we instigated a second Boolean search strategy on Web of Science using the following keywords: (1) care customization OR personalized medicine OR personalized service OR targeting populations OR individualized care AND (2) segmentation analysis OR profiling patients OR categorizing patients AND (3) patient needs OR patient characteristics OR patient preferences OR population characteristics OR patient demands (see appendix 1 for an example of an entire search strategy). In total, 6390 articles were identified. The search results were exported into Mendeley, and duplicates were removed by a reviewer (AF) at each iteration.

Inclusion and exclusion criteria were defined to identify the final sample of articles (step 3). We included any studies addressing aspects of segmentation analysis and customized intervention, in real health care delivery contexts. We defined “segmentation analysis” as any analysis of patient characteristics, even without explicit reference to specific customization methods, but with the objective of customizing delivered care and services. We also defined “customized interventions” as any developments in health supply that implemented targeted answers, no matter how the care team/process was designed, assembled or delivered, and regardless of the type of practice or organization (i.e. individual, small or large group). Lastly, we did not predefine how segmentation analysis and interventions interacted, and we accepted critical analyses and reviews of care customization strategies when they addressed both key parts of segmentation analysis and customized intervention, as we defined it. We tested our selection criteria on 20 articles (randomly extracted from the first literature review) to determine criteria modification. Accordingly, a second inclusion and exclusion criteria list was defined to refine our final selection.

Inclusion criteria

  1. (1).

    Full-text in English.

  2. (2).

    The notion of customization and/or targeting and/or individualized and/or personalization in association with health care.

  3. (3).

    Patient needs, demands or preferences were addressed to create a customized strategy, related to a segmentation analysis in a health care delivery context.

  4. (4).

    The development or design implementation, sustainability, and/or dissemination of a customized intervention using a segmentation analysis. Articles were included if they explicitly reported a customization approach (i.e., population mapping, assessment of tailored interventions and properties, or analysis of a MC approach).

Exclusion criteria:

  1. (1).

    Care customization was mentioned, but segmentation analysis and/or customized interventions were not reported (i.e. the primary focus was human or technological support, or other related topics on care customization, without documenting a segmentation analysis and customized intervention; the customization approach was only expressed as a recommendation or a requirement).

  2. (2).

    Segmentation analysis was not followed by a customized intervention in the care delivery system (i.e. genomic studies that suggested new targeted therapies, or questionnaires assessing patient quality of life; segmentation analyses from a technical or methodological viewpoint (i.e. selection bias, regression analyses), or analysis of patient needs, without reporting a customized intervention).

  3. (3).

    Segmentation analysis and/or customized intervention as a strategy for a clinical trial, fundamental research purposes or reviews, articles not based in the real world of care delivery (i.e. randomized trials assessing a real-world intervention were included).

  4. (4).

    Articles investigating a customized intervention without referring to a segmentation analysis.

  5. (5).

    Any stratification method or regression analysis unrelated to a customized intervention (i.e. analyzing relationships between a set of factors (i.e. patient behaviors), and an outcome; or customized clinical scores predicting outcomes, without an explicit customized strategy of care).

  6. (6).

    The customization approach is applied outside health care, or to any health care delivery aspect, but not to patient characteristics (i.e. electronic health records or monitoring tools).

  7. (7).

    Articles comprising comments, commentaries, dissertations or conference proceedings.

  8. (8).

    To increase consistency among reviewers, two investigators screened the same 200 first abstracts (AF, EM). Titles and abstracts were then independently reviewed by one investigator (EM). Abstract analyses generated 240 full-text articles which were independently assessed for review eligibility by the two same reviewers. Consensus was agreed for uncertain cases through discussion between the two reviewers, and in three cases with a third reviewer (MW). Last, the analysis was supplemented by the manual review of eligible bibliographies for inclusion (six articles were added).

The last steps 4 (charting the data) and 5 (collating, summarizing and reporting the results) led to coding the data contained in the articles according to the components of the concept of Mass Customization. This resulted in the definition of three broad categories (the first and third being descriptive while the second refers to the MC’s framework): 1) context characteristics (publication year; sample country/location; health topic; type of review); 2) customized strategy (applied versus potential/proposed interventions; patient characteristics and type of patient characteristics supporting the segmentation analysis; intervention; impact evaluation), and 3) use of a conceptual basis/theory (articles that mentioned MC; other theories informing a conceptual basis related to care customization because we hypothesized that in this exploratory field other references could be mobilized) (see Table 1 in the result section). For each article, the methodological characteristics, topics and the customization process including the two components of segmentation analysis and customized intervention of the concept of MC were also described (see Table 2 in the result section). Finally, we did not analyze methodological qualities in our studies, which were heterogeneous. However, our goal was not to uncover evidence on specific customized delivery models, but to qualitatively extract information from the literature.

Table 1 Summary of study articles
Full size table
Table 2 Description of the methodological characteristics, topics and the customization process (n = 70)
Full size table

All the steps of the literature search, numbers of citations screened, duplicates removed and full-text documents assessed, are reported using a PRISMA flow diagram (Fig. 1).

Fig. 1
figure 1

PRISMA flow diagram. * The Boolean search strategy introduced the search term “personalized medicine”. This search term refers to a very vast literature which does not allow it to be treated like the others. With the Boolean search strategy, it becomes possible to identify delivery issues related to “personalized medicine”

Full size image

Results

The final selection included 70 articles. According to our framework, we have organized the results as follows: in a first part, we present the characteristics of the material; then, in a second part, we review the content of current efforts to implement care customization in delivery systems with reference to the concept of MC; finally, in a third part, we identify the potential conceptual basis of these efforts.

Materials

Health topics varied from clinical conditions to socio-economic patient characteristics (i.e. gender characteristics; marginalizing conditions, immigrants). The most common health topics were cancer (n = 13) and the elderly (n = 9). Additionally, some articles (n = 8) addressed care customization strategies, using cost and resource allocation generated by patients (i.e. “Higher Utilizers” or “High needs High costs”). Lastly, some articles (n = 8) addressed care customization strategies in a general way, applicable to any type of patient (in this case, we use the world “transversal”).

Most articles (n = 42) were published between 2016 and 2019, with few published in the early years (i.e. five between 2008 and 2011, and 23 between 2012 and 2015). Approximately half the articles were based in the United States (n = 29). The remaining articles were outside the US: seven in Asia (China, India, Indonesia, Iran, Japan, Philippines, Taiwan), 20 in Europe (seven in the Netherlands, four in the United Kingdom, and one each in Denmark, Finland, France, Germany, Italy, Switzerland), and five across different countries representing international consortia.

Care customization efforts

Two categories emerged when organizing the articles: those that applied care customization to an existing intervention (n = 28), and those that proposed the application of care customization to improve delivery systems (n = 48).

Segmentation analysis

We also categorized the articles according to the patient characteristics that support the segmentation analysis. Four categories emerged: (1) clinical needs (including genomic and molecular features); (2) psychosocial needs (i.e. needs due to financial and social status, psychological and personality traits, religious and cultural values; preferences related to behavioral attitudes in relation to health issues); (3) service needs (i.e. demands or preferences about aspects of life, apart from health issues); (4) costs (i.e. costs or resources allocated by the care process). These categories often overlapped, but for the purposes of this review, it was important to understand which patient characteristics were strongly informed by the segmentation analysis. Most articles reported on clinical needs (n = 51), but also psychosocial (n = 39) and service needs (n = 30) were covered. Clinical needs are often related to genomic characteristics, but they also cover new personalized plans, based on risk stratification (i.e. the decision to provide preoperative sedation is stratified by autism spectrum severity levels, before surgical procedures) [34]. Psychosocial needs included financial barriers (i.e. for: vision care appointments [35]; services related to social status [8, 36]; psychological characteristics (i.e. personality traits) [37, 38]; emotional attitudes [39]; and cultural values [40]. The service category emerged from analyses, while answers to patient demands were beyond what health care systems could usually deliver. Examples of service needs included home services for older persons, such as home energy assistance, legal services [41] or appointment scheduling [42].

Several types of patient characteristics could be identified during the segmentation analysis phase. Associations between different types of patient characteristics were distributed between as follow: homogeneously between 0, 1 and 2 associations and less for 3 associations (n = 5). The most usual situations were segmentation analyses based on clinical, psychosocial and service needs (n = 19), and clinical and psychosocial needs (n = 11). For example, Van der Laan et al. [43], in a study in Dutch older adults, defined five groups of patients based on a set of clinical and psychosocial need assessments. The assessment tool was proposed as a first triage step that offered more contextual information than purely disease-based information. The second most frequent segmentation analysis was based only on clinical needs (n = 12).

Customized interventions

Interventions were also categorized according to the three steps of any MC effort described in the theoretical part: (i) fabrication (i.e. any new care or service catering to the needs and demands of patients); (ii) assembly (i.e. any new structural combination of multiple standardized products and services, based on the needs and demands of patients) and (iii) distribution (i.e. any new coordination effort and integration of different products and services allowing timely delivery to patients). Examples of new care and services were various (n = 20): from concierge services offering transportation assistance for medical tourism [44], to new digital and artificial intelligence (AI) technologies that personalized physician choice for patients [45, 46], creating specific facilities depending on gender [47, 48], genomic tests [49,50,51] and biomedical informatics support [52]. Examples of assembly were less frequent (n = 7); one proposal aimed to combine care and services produced by two hospitals and an inpatient team in the same individualized care package, facilitating an inter-organizational infrastructure [53], nursing homes facilities [54], or accountable care organizations [55]. In contrast, distribution examples (n = 56) were mainly represented by coordination efforts in various clinical situations (i.e. during transitional care,) [56]. Distribution efforts also stressed communication efforts, in particular for personalizing preventive messages according to lifestyle [45, 57,58,59,60,61], or cancer survivorship [62]. Several studies also referred to the creation of multidisciplinary teams [63,64,65], including carers [66] and family members in specific conditions (i.e. to care for older persons with cognitive impairments) [67]. Stratification of interventions was also often reported, and was based on need requirements [68], and/or taking new needs and demands into account (i.e. behavioral and psychological characteristics) [69, 70]; personality traits [37, 71]; lifestyles for obesity prevention [61]; and cultural attitudes [62]. Organizational aspects like the organizational climate [72], the adaptation of routines [73], patient-nurse assortment [74], integration of new data and information in electronic health records [49, 75], were factors promoting customized interventions. Lastly, these distributional aspects were dependent on preferences and needs that could change over the course of care [76].

Impact evaluation

Several articles (n = 18) reported added-value evidence from customization. These were relatively equally distributed between patient experience (n = 7), cost (n = 6) and quality of care (n = 5), including quality of life [77]. However, none demonstrated customization, incorporating all three criteria.

Theories

We observed that only 10% of articles explicitly applied MC theory for interventions (n = 6), and more generally that theoretical frameworks of any kind were often missing. Twenty-two different theories (i.e. inter-organizational collaboration or optimization of personalized assortment) informing segmentation and customized intervention were identified in 38 articles. Precision and personalized medicine (n = 5), individualized care (n = 6) and patient-centered care (n = 5) were the most represented theories.

Discussion

In this scoping review, we identified 70 articles addressing how customization approaches are delivered in health care. Our results indicate the absence of a common strategy for delivering customized care based on a conceptual basis.

The lack of a common strategy to deliver customized care

First, while the benefit of a customized delivery model is often advanced in recent years (the majority of papers have been published in the past 4 y), there is little conceptual basis that supports current efforts (the reference to a conceptual basis is only mentioned in about half of the studies). In this case, studies refer to a variety of models and theories (i.e., individualized care or consumer-centered care; patient-centered care or precision medicine; social marketing theories or models equity-oriented health care and “essential care”) [41, 77]. The theory of the “MC” is for its part little cited [24, 26, 78].

Second, the current efforts appear to be carried out in parallel, according to different logics of action. By logic of action, we refer to the definition of Strauss et al. [79] in which specific participants reflect common motivation for acting (i.e. categorizing patient characteristics and customized interventional responses) [79]. Depending on the focus given to one patient characteristic, or to the different association between them, four distinct logics of action can be defined: 1. Clinical; 2. Population health; 3. Patient-centered care and 4. Financial. Segmenting patients according to their clinical characteristics is “natural” in health care, and flows from the fact each patient is a single case. This clinical logic has recently been updated with the emergence of personalized and precision medicine, as highlighted by different articles [52, 80]. Beside this clinical logic, a second logic orients customization development to the psychosocial needs of the patient. In this instance, customization efforts have generally the goal to reduce inequalities to access and outcomes across populations. A good example of this comes from Ford-Gilboe et al. [42], who demonstrated that providing more equity-oriented health care improved health outcomes for people living in marginal conditions. The responsibility for structuring population health customization is most often borne by public health professionals, academics engaged with communities, local decision-makers, and social workers [81, 82]. The third logic, patient-centered care, describes how health care organizations respond to patient demands and preferences. In this context, care-giving is seen as a “service” on behalf of, and at the direction of patients. This logic is often referred to as “patient-centered care”, but in some cases is viewed through the notion of individualized care or marketing theories [44]. When patients are targeted by the costs their processes of care generate, they fall into a fourth financial logic. In this case, customization strategies have a common goal to target higher resource users, and to propose a customized intervention specifically to this subpopulation, applying it in various conditions. This financial logic generally refers to managers of health organizations and health systems who wish to rationalize costs. These four customization logics are not separate or independent, as evidenced by the combinations between clinical, psychosocial and service needs. The most common combinations observed in our review are segmentation analyzes associating clinical, psychosocial and service needs (n = 19), and clinical and psychosocial needs (n = 11). However, each logic reflects a potentially strong force that guides the division of work in health care. As such, they may create a set of parallel but unconnected approaches where participant groups develop a specific point of view of their organizational and professional goals, and similarly, the structure of their work follows this view, potentially undermining the promise of a common care customized delivery model. For example, the enthusiasm for personalized and precision medicine contributes to the spread of individually-focused clinical practices. This may, on its own, lead to quality improvements, and thereby advance the health of an individual population [83]. But this type of customization could be viewed by public health leaders as “premature” or inappropriately individualistic, thus missing out on the potential offered by broader applications of customization [84].

Research and managerial implications

Given the nascent and heterogeneous state of the knowledge in this area, this is an important time to reflect on the definition and use of theoretical frameworks for building a theory-based customized care delivery model. We highlight three areas for researchers and managers involved interested in this field to move in this direction.

A first area of research could focus on the content of the segmentation analysis and the customized intervention steps in order to define a better conceptual basis. A first step consists in clarifying and increasing the characteristics of the patients taken into account during the segmentation analysis [67]. If we set aside the cost criterion which embodies another logic (i.e. the measurement of efficiency), many criteria are used to cover the needs, demands and preferences of patients. Beyond clarifying the concept of clinical and social needs [43], different articles stress the role of other patient characteristics: patient behaviors (i.e. the impact of negative personality traits in care delivery [37, 71], and preferences or demands expressed on subjects unrelated to health, but related to certain aspects of daily life, when encountering a health issue (i.e. lifestyle changes to prevent childhood obesity) [63]. They suggest that the usual characteristics of age, sex, clinical condition and education, may not predict how patients tolerate the adaptation of particular interventions [70], and call for opening up of patient characteristics used during segmentation analysis, and the type of data collected [69, 85]. In this consideration of a greater number of patient characteristics, modern profiling methods based on the processing of big data, may also have a role [46]. They can help develop forms of segmentation that identify the needs and demands of each patient [86]. This trend, if it is confirmed, could influence the conceptual basis of any care customization delivery model. More than a “MC” model (from mass to customization), it is a model of “singularity on a large scale” (from singularity to mass) that acknowledges the uniqueness of each patient by capturing a variety of needs and demands, which would serve as a conceptual basis.

Research could also help structure the content of a customized intervention. Several articles underlined the importance of coordination and communication efforts in customizing interventions, in particular by emphasizing the role of structural integration or developing multidisciplinary teams [63,64,65]. Other articles have stressed the importance of personalizing professional-patient relationships [87] while some others have stressed the key-role of modular packages [26]. The variety of actions listed during customization interventions calls for a more precise content, and how to apply them to the different stages of the patient pathway, within hospitals, but also during transitional care and primary care.

A second area of research relates to the added-value of a care customization delivery model. Our review reveals some positive impacts of care customization on quality, patient experience and costs. However, if we assume that more customization brings better outcomes and more satisfying experiences, the impact on costs requires more investigations. Investments for increased customization may bring additional costs at the different steps: investment in new methods of segmentation analysis (even if analytical algorithms can facilitate it) [88]; new services (i.e. new therapies, “concierge” systems, home services) [89]; or new forms of coordination between different health care professionals, managers, and social workers. Equally, several studies reported savings generated by customization, by reducing unnecessary hospitalization [56, 64], treatment costs [75], and duplication [90]. Other studies [8, 53] highlighted that an earlier and more focused identification of “complex” patients and/or high user can help health care organizations design more appropriate and efficient organizational responses. Chaudhuri and Lillrank [78] also argued that a customized strategy applied to high volumes of similar patients, and could be economic by implementing common standards of care. However, these data are sparse, and require more research of any care customization interventions to give evidence of their added-value, and in particular, of their economic viability [91].

A third last area of research could explore how to align the logics of action that underlie the efforts of care customization. While a single approach to customization of care is probably unrealistic, the compartmentalization of different logics may limit the impact of current customization efforts, resulting in additional costs. Researchers can help unravel the logics of action that support such developments, and find ways to facilitate their alignment, as in the case of customizing “high-need, high-cost patients” [8].

Limitations

There are several limitations to this scoping exercise. Firstly, customization of care is rapidly growing and changing. There may be recent or more current efforts in developing interventions that have not yet appeared in the literature, or during our search, and as such, we may have inadvertently missed recently published articles. We excluded articles that addressed care customization, but not in routine health care delivery contexts (i.e. in clinical trials where they are not applied in real life contexts, fundamental research, or innovations at early development stages). As such, it is likely we underestimated the attributes of recent care customization strategies. The number of excluded clinical trials involving personalized medicine suggests these represent a dominant area of care customization in the mid-term.

Secondly, although we conducted an extensive literature review using a wide variety of terms capturing customization relevant articles, it is possible some articles may not have been identified. Our decision to include only studies that reported care customization may have excluded studies that addressed patient characteristics analysis in a customized effort, but were not labeled as such, or were not accurately represented in the abstract. To limit the impact of this selection process, we studied bibliographies of selected articles, and added six more relevant articles. We also selected only articles identifying customization strategies (i.e. segmentation analysis that led to a customized intervention) as the driver of this review, and we excluded segmentation initiatives that may have added insights (i.e. patient-profiling questionnaires and machine learning methods). In some cases, it was difficult to assess if segmentation analyses were accompanied by a customized intervention, or to circumscribe the notion of segmentation analysis itself, by comparison with stratification methods. To limit the incorrect exclusion of some articles, investigators discussed these cases. Last, we did not include “grey” literature; including newsletters, professional association or institutional news, and publicity publications. As care delivery systems and its analysis are not delineated research elements in medicine, this absence may have represented a bias that overlooked recently developed customized strategies, but not yet published. However, the analytical review of bibliographies in each selected articles did not uncover any more relevant information. Third, it is likely our selection process omitted specific research on one of the four customization approach steps, potentially missing important studies. For example, the definition of a “modular package” requires a better understanding of how the range of care and services could be combined into packages, and be pre-grouped as studies have shown [26, 27]. The same was also true for related issues such as care customization from regulatory and ethical perspectives (i.e. individual privacy, segmentation and discrimination of sub-populations based on ethnicity). These elements represent interesting research perspective for the future, and can help improve the conceptual of a customized care delivery model.

Conclusions

This study is the first systematic review to examine current care customization as a new way of delivering health care and related services. The analysis shows that there is no theoretically-derived common strategy for delivering customized care. Consequently, we have identified three priority areas of research to advance in the development of a common customization delivery model: better define the content of the segmentation analysis and the intervention steps, demonstrate its added value, in particular its economic viability, and align the logics of action that underpin current efforts of customization. It would allow them to use customization to reduce costs and improve quality of care.

Availability of data and materials

The data sets analysed in this study are available from the corresponding author on request.

Abbreviations

CRF:

Cancer-related fatigue

ADHD:

Children with attention-deficit/hyperactivity disorder

CGA:

Comprehensive Geriatric Assessment

EHR:

Electronic health record

ED:

Emergency Department

HNHC:

High needs high costs

ICPs:

Individualized care plans

IT:

Information technology

LGBT:

Lesbian, Gay, Bisexual and Transgender

LDL:

Low Density Lipoprotein

MC:

Mass Customization

PCC:

Patient-Centered Care

PRISMA:

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

SCD:

Sickle cell disease

References

  1. Peabody, F.W. The care of the patient. JAMA. 1927, doi: https://0-doi-org.brum.beds.ac.uk/10.1001/jama.252.6.813, 1984

  2. Hodson, R. Precision medicine. Nature. 2016, doi: https://0-doi-org.brum.beds.ac.uk/10.1038/537S49a.

  3. Hamburg, M.A., Collins, F.S. The path to personalized medicine. N. Engl. J. Med. 2010, doi; https://0-doi-org.brum.beds.ac.uk/10.1056/NEJMp1006304.

  4. Mead N, Bower P. Patient-centeredness: a conceptual framework and review of the empirical literature. Soc Sci Med. 2000;99:1923–39. https://0-doi-org.brum.beds.ac.uk/10.1016/S0277-9536(00)00098-8.

    Article  Google Scholar 

  5. Bardes, C.L. Defining 'patient-centered medicine'. N. Engl. J. Med., 2012, doi: https://0-doi-org.brum.beds.ac.uk/10.1056/NEJMp1200070.

  6. Coulter A, Entwistle VA, Eccles A, Ryan S, Shepperd S, Perera R. Personalized care planning for adults with chronic or long-term health conditions. Cochrane Database Syst Rev. 2015;3:CD010523. https://0-doi-org.brum.beds.ac.uk/10.1002/14651858.CD010523.pub2.

    Article  Google Scholar 

  7. Suhonen, R., Välimäki, M., Leino-Kilpi, H. A review of outcomes of individualized nursing interventions on adult patients. J. Clin. Nurs., 2008, doi: https://0-doi-org.brum.beds.ac.uk/10.1111/j.1365-2702.2007.01979.x

  8. Blumenthal, D., Anderson, G., Burke, S., Fulmer, T., Jha, A.K., Long, P. Tailoring complex-care management, coordination, and integration for high-need, high-cost patients: a vital direction for health and health care. NAM Perspect, 2016, doi: https://0-doi-org.brum.beds.ac.uk/10.31478/201609q

  9. McLaughlin CP, Kaluzny AD. Building client centered systems of care: choosing a process direction for the next century. Health Care Manag Rev. 2000;25:73–82. https://0-doi-org.brum.beds.ac.uk/10.1097/00004010-200001000-00009.

    Article  CAS  Google Scholar 

  10. Davis SM. Future perfect. Reading. MA: Addison-Wesley; 1987.

    Google Scholar 

  11. Pine, B.J. Mass customizing products and services. Plan. Rev., 1993, doi: https://0-doi-org.brum.beds.ac.uk/10.1108/eb054420

  12. Kotha, S. Mass customization: implementing the emerging paradigm for competitive advantage. Strateg. Manag. J., 1995, doi: https://0-doi-org.brum.beds.ac.uk/10.1002/smj.4250160916

  13. Da Silveira, G., Borenstein, D., Fogliatto, F.S. Mass customization: literature review and research directions. Int. J. Prod. Econ., 2001, doi: https://0-doi-org.brum.beds.ac.uk/10.1016/S0925-5273(00)00079-7

  14. Payne, A., Frow, P. A strategic framework for customer relationship management. J. Mark., 2005, doi: https://0-doi-org.brum.beds.ac.uk/10.2307/30166559

  15. Venter, P., Wright, A., Dibb, S. Performing market segmentation: a performative perspective. J. Mark. Manag., 2015, doi: https://0-doi-org.brum.beds.ac.uk/10.1080/0267257X.2014.980437

  16. Meyer, M.H., DeTore, A. Perspective: creating a platform-based approach for developing new services. J. Prod. Innov. Manag., 2001, doi: https://0-doi-org.brum.beds.ac.uk/10.1016/S0737-6782(01)00070-4

  17. Jitpaiboon, T., Dobrzykowski, D.D., Ragu-Nathan, T.S., Vonderembse, M.A.. Unpacking IT use and integration for mass customization: a service-dominant logic view. Int. J. Prod. Res. 2013, doi: https://0-doi-org.brum.beds.ac.uk/10.1080/00207543.2012.720727

  18. Zhang M, Zhao X, Qi Y. The effects of supply chain integration on customer satisfaction and financial performance: an organizational learning perspective. Int.J Prod Econ. 2014;146(1):346–58. https://0-doi-org.brum.beds.ac.uk/10.1016/j.ijpe.2013.07.023.

    Article  Google Scholar 

  19. Gwinner, K.P., Bitner, M.J., Brown, S.W., Kumar, A. Service customization through employee adaptiveness. J. Serv. Res., 2005, doi: https://0-doi-org.brum.beds.ac.uk/10.1177/1094670505279699

  20. Schilling, M.A. Toward a general modular systems theory and its application to inter-firm product modularity. Acad. Manag. Rev., 2000, doi: https://0-doi-org.brum.beds.ac.uk/10.2307/259016

  21. Lampel J, Mintzberg H. Customizing customization. Sloan Manag Rev. 1996;38(1):21–30.

    Google Scholar 

  22. Mannion, R., Exworthy, M. (Re) making the Procrustean bed? Standardization and customization as competing logics in health care. Int. J. Heal. Policy Manag., 2017, doi: https://0-doi-org.brum.beds.ac.uk/10.15171/ijhpm.2017.35

  23. Bohmer, R.M.J. Medicine’s service challenge: blending custom and standard care. Health care Manage Rev., 2005, doi: https://0-doi-org.brum.beds.ac.uk/10.1097/00004010-200510000-00006

  24. Minvielle E, Waelli M, Sicotte C, Kimberly JR. Managing customization in health care: a framework derived from the services sector literature. Health Policy. 2014;117:216–27. https://0-doi-org.brum.beds.ac.uk/10.1016/j.healthpol.2014.04.005.

    Article  PubMed  Google Scholar 

  25. Meyer MH, Jekowsky E, Crane FG. Applying platform design to improve the integration of patient services across the continuum of care. Manag Serv Qual. 2007;23:151. https://0-doi-org.brum.beds.ac.uk/10.1108/09604520710720656.

    Article  Google Scholar 

  26. De Blok C, Luijkx K, Meijboom B, Schols J. Modular care and service packages for independently living elderly. Int J Oper Prod Manag. 2010;17:68–84. https://0-doi-org.brum.beds.ac.uk/10.1108/01443571011012389.

    Article  Google Scholar 

  27. De Blok, C., Meijboom, B., Luijkx, K., Schols, J. The human dimension of modular care provision: Opportunities for personalization and customization. Int. J. Prod. Econ., 2013, doi: https://0-doi-org.brum.beds.ac.uk/10.1016/j.ijpe.2012.05.006

  28. Soffers R, Meijboom B, Van Zaanen J, Van Der Feltz-Cornelis C. Modular health services: a single case study approach to the applicability of modularity to residential mental health care. BMC Health Serv Res. 2014;14:210. https://0-doi-org.brum.beds.ac.uk/10.1186/1472-6963-14-210.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Silander, K., Torkki, P., Lillrank, P., Peltokorpi, A., Brax, S.A., Kaila, M. Modularizing specialized hospital services: constraining characteristics, enabling activities and outcomes. Int J Oper Prod Manag. 2017, do: https://0-doi-org.brum.beds.ac.uk/10.1108/IJOPM-06-2015-0365

  30. Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467–73. https://0-doi-org.brum.beds.ac.uk/10.7326/M18-0850.

    Article  PubMed  Google Scholar 

  31. Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19–32. https://0-doi-org.brum.beds.ac.uk/10.1080/1364557032000119616.

    Article  Google Scholar 

  32. Daudt HM, van Mossel C, Scott SJ. Enhancing the scoping study methodology: a large, inter-professional team’s experience with Arksey and O’Malley’s framework. BMC Med Res Methodol. 2013;13:1–9.

    Article  Google Scholar 

  33. Hart C. Doing a literature review: releasing the research imagination. London: Sage; 2009.

    Google Scholar 

  34. Swartz JS, Amos KE, Brindas M, Girling LG, Graham MR. Benefits of an individualized perioperative plan for children with autism spectrum disorder. Pediatr Anesth. 2017;27:856–62. https://0-doi-org.brum.beds.ac.uk/10.1111/pan.13189.

    Article  Google Scholar 

  35. Aggarwal S, Ju D, Allen AM, Rose LA, Gill KP, Shen SA, et al. Regional differences in vision health: findings from Mwanza, Tanzania. Int Health. 2018;10:457–65. https://0-doi-org.brum.beds.ac.uk/10.1093/inthealth/ihy046.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Seeleman C, Essink-Bot ML, Stronks K, Ingleby D. How should health service organizations respond to diversity? A content analysis of six approaches. BMC Health Serv Res. 2015;15:510. https://doi.org/10.1186/s12913-015-1159.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Friedman B, Veazie PJ, Chapman BP, Manning WG, Duberstein PR. Is personality associated with health care use by older adults? Milbank Q. 2013;91(3):491–527. https://0-doi-org.brum.beds.ac.uk/10.1111/1468-0009.12024.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Lattie EG, Asvat Y, Shivpuri S, Gerhart J, O’Mahony S, Duberstein P, et al. Associations between personality and end-of-life care preferences among men with prostate cancer: a clustering approach. J Pain Symptom Manag. 2016;51:52–9. https://0-doi-org.brum.beds.ac.uk/10.1016/j.jpainsymman.2015.08.005.

    Article  Google Scholar 

  39. Hagan TL, Arida JA, Hughes SH, Donovan HS. Creating individualized symptom management goals and strategies for cancer-related fatigue for patients with recurrent ovarian cancer. Cancer Nurs. 2017;40:305–13. https://0-doi-org.brum.beds.ac.uk/10.1097/NCC.0000000000000407.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Price P, Tacey M, Koufariotis V, Stramandinoli D, Vincent R, Grigg L, et al. A contemporary phone-based cardiac coaching program: evolution and cross cultural utility. Hear Lung Circ. 2018;27:804–11. https://0-doi-org.brum.beds.ac.uk/10.1016/j.hlc.2017.07.008.

    Article  Google Scholar 

  41. Golden RL, Emery-Tiburcio EE, Post S, Ewald B, Newman M. Connecting social, clinical, and home care services for persons with serious illness in the community. J Am Geriatr Soc. 2019;67:S412–8. https://0-doi-org.brum.beds.ac.uk/10.1111/jgs.15900.

    Article  PubMed  Google Scholar 

  42. Ford-Gilboe M, Wathen CN, Varcoe C, Herbert C, et al. How equity-oriented health care affects health: key mechanisms and implications for primary health care practice and policy. Mibank Q. 2018;96:635–71. https://0-doi-org.brum.beds.ac.uk/10.1111/1468-0009.12349.

    Article  Google Scholar 

  43. Van der Laan MRE, van Offenbeek MAG, Broekhuis H, Slaets JPJ. A person-centred segmentation study in elderly care: towards efficient demand-driven care. Soc Sci Med. 2014;113:68–76. https://0-doi-org.brum.beds.ac.uk/10.1016/j.socscimed.2014.05.012.

    Article  Google Scholar 

  44. Rydback M, Hyder AS. Customization in medical tourism in the Philippines. Int J Pharm Healthc Mark. 2018;36:203–19. https://0-doi-org.brum.beds.ac.uk/10.1108/IJPHM-07-2017-0035.

    Article  Google Scholar 

  45. Larsen LB, Sondergaard J, Thomsen JL, Halling A, et al. Step-wise approach to prevention of chronic diseases in the Danish primary care sector with the use of a personal digital health profile and targeted follow-up - an assessment of attendance. BMC Public Health. 2019;19:1092. https://0-doi-org.brum.beds.ac.uk/10.1186/s12889-019-7419-4.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Pan X, Song J, Zhang F. Dynamic recommendation of physician assortment with patient preference learning. IEEE Trans Autom Sci Eng. 2019. https://0-doi-org.brum.beds.ac.uk/10.1109/TASE.2018.2839651.

  47. Lin HC. A study on women’s perceptions regarding the requirements and satisfaction of a hospital environment. GMBHS. 2011. https://0-doi-org.brum.beds.ac.uk/10.1016/j.gmbhs.2011.09.001.

  48. VHA Directive 1341, Providing health care for transgender and intersex veterans. 2018. https://www.albuquerque.va.gov/docs/ProvidingHealthcareforTransgenderandIntersexVeterans.pdf

  49. Jing X, Kay S, Marley T, Hardiker NR, Cimino JJ. Incorporating personalized gene sequence variants, molecular genetics knowledge, and health knowledge into an EHR prototype based on the continuity of care record standard. J Biomed Inform. 2012;45:82–92. https://0-doi-org.brum.beds.ac.uk/10.1016/j.jbi.2011.09.001.

    Article  PubMed  Google Scholar 

  50. Hassett MJ, Silver SM, Hughes ME, Blayney DW, Edge SB, Herman JG, et al. Adoption of gene expression profile testing and association with use of chemotherapy among women with breast cancer. J Clin Oncol. 2012;30:2218–26. https://0-doi-org.brum.beds.ac.uk/10.1200/JCO.2011.38.5740.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Manegold C. Treatment algorithm in 2014 for advanced non-small cell lung cancer: therapy selection by tumor histology and molecular biology. Adv Med Sci. 2014;59:308–13. https://0-doi-org.brum.beds.ac.uk/10.1016/j.advms.2014.08.008.

    Article  PubMed  Google Scholar 

  52. Barlow-Stewart K. Personalized medicine: more than just personal. AQ Aust Q. 2017;88(2):31–44.

    Google Scholar 

  53. Hardin L, Kilian A, Spykerman K. Competing health care systems and complex patients: an inter-professional collaboration to improve outcomes and reduce health care costs. J Interprof Educ Pract. 2017;21:96. https://0-doi-org.brum.beds.ac.uk/10.1016/j.xjep.2017.01.002.

    Article  Google Scholar 

  54. Sawamura K, Nakashima T, Nakanishi M. Provision of individualized care and built environment of nursing homes in Japan. Arch Gerontol Geriatr. 2013;56:416–24. https://0-doi-org.brum.beds.ac.uk/10.1016/j.archger.2012.11.009.

    Article  PubMed  Google Scholar 

  55. O’Malley AS, Rich EC, Sarwar R, Schultz, et al. How accountable care organizations use population segmentation to care for high-need, high-cost patients. Issue Brief (Commonw. Fund). 2019:1–17.

  56. Jenq GY, Doyle MM, Belton BM, Herrin J, Horwitz LI. Quasi-experimental evaluation of the effectiveness of a large-scale readmission reduction program. JAMA Intern Med. 2016;176:681–90. https://0-doi-org.brum.beds.ac.uk/10.1001/jamainternmed.2016.0833.

    Article  PubMed  Google Scholar 

  57. Brandzel SD, Bowles EJA, Wieneke A, Bradford SC, et al. Cancer screening reminders: addressing the spectrum of patient preferences. Perm J. 2017;21:17–051. https://0-doi-org.brum.beds.ac.uk/10.7812/TPP/17-051.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Geller BM, Shelly JM, Dorwaldt AL, Howe, et al. Increasing patient/physician communications about colorectal cancer screening in rural primary care practices. Med Care. 2008;46(9):S36–43.

    Article  Google Scholar 

  59. Gesser-Edelsburg A, Shalayeva S. Internet as a source of long-term and real-time professional, psychological, and nutritional treatment: a qualitative case study among former Israeli Soviet Union immigrants. J Med Int Res. 2017;19:e33. https://0-doi-org.brum.beds.ac.uk/10.2196/jmir.7130.

    Article  Google Scholar 

  60. Gordon TF, Bass SB, Ruzek SB, Wolak C, et al. Developing a typology of African Americans with limited literacy based on preventive health practice orientation: implications for colorectal cancer screening strategies. J Health Commun. 2014;19:1259–77. https://0-doi-org.brum.beds.ac.uk/10.1080/10810730.2013.872725.

    Article  PubMed  Google Scholar 

  61. Kolodinsky J, Reynolds T. Segmentation of overweight Americans and opportunities for social marketing. Int J Behav Nutr Phys Act. 2009;6:13. https://0-doi-org.brum.beds.ac.uk/10.1186/1479-5868-6-13.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Blanch-Hartigan D, Viswanath K. Socioeconomic and sociodemographic predictors of cancer-related information sources used by cancer survivors. J Health Commun. 2015;20:204–10. https://0-doi-org.brum.beds.ac.uk/10.1080/10810730.2014.921742.

    Article  PubMed  Google Scholar 

  63. Avis JL, Ambler KA, Jetha MM, Boateng H, et al. Modest treatment effects and high program attrition: The impact of interdisciplinary, individualized care for managing pediatric obesity. Pediatr Child Health. 2013;18:e59–63. https://0-doi-org.brum.beds.ac.uk/10.1093/pch/18.10.e59.

    Article  Google Scholar 

  64. Mercer T, Bae J, Kipnes J, Velazquez M, et al. The highest utilizers of care: individualized care plans to coordinate care, improve health care service utilization, and reduce costs at an academic tertiary care center. J Hosp Med. 2015;10:419–24. https://0-doi-org.brum.beds.ac.uk/10.1002/jhm.2351.

    Article  PubMed  Google Scholar 

  65. von Dadelszen P, Magee LA, Payne BA, Dunsmuir DT, Drebit S, et al. Moving beyond silos: how do we provide distributed personalized medicine to pregnant women everywhere at scale? Insights from PRE-EMPT. Int J Gynecol Obstet. 2015;131:S10–5. https://0-doi-org.brum.beds.ac.uk/10.1016/j.ijgo.2015.02.008.

    Article  Google Scholar 

  66. Braaf SC, Lennox A, Nunn A, Gabbe BJ. Experiences of hospital readmission and receiving formal carer services following spinal cord injury: a qualitative study to identify needs. Disabil Rehabil. 2017;40:1893–9. https://0-doi-org.brum.beds.ac.uk/10.1080/09638288.2017.1313910.

    Article  PubMed  Google Scholar 

  67. Petry H, Ernst J, Steinbrüchel-Boesch C, Altherr J, et al. The acute care experience of older persons with cognitive impairment and their families: a qualitative study. Int J Nurs Stud. 2019;96:44–52. https://0-doi-org.brum.beds.ac.uk/10.1016/j.ijnurstu.2018.11.008.

    Article  PubMed  Google Scholar 

  68. Pilotto A, Cella A, Pilotto A, Daragjati J, et al. Three decades of comprehensive geriatric assessment: evidence coming from different health care settings and specific clinical conditions. J Am Med Dir Assoc. 2017;18:192. https://0-doi-org.brum.beds.ac.uk/10.1016/j.jamda.2016.11.004.

    Article  PubMed  Google Scholar 

  69. McConnell H, White R, Maher J. Categorizing cancers to enable tailored care planning through a secondary analysis of cancer registration data in the UK. BMJ Open. 2017;7:e016797. https://0-doi-org.brum.beds.ac.uk/10.1136/bmjopen-2017-016797.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Rose PM. Patients’ characteristics informing practice: improving individualized nursing care in the radiation oncology setting. Support Care Cancer. 2018;26:3609–18. https://0-doi-org.brum.beds.ac.uk/10.1007/s00520-018-4210-5.

    Article  PubMed  Google Scholar 

  71. Cotrell V, Carder PC. Health-related needs assessment of older residents in subsidized housing. Cityscape. 2010;12(2):47–66.

    Google Scholar 

  72. Papastavrou E, Acaroglu R, Sendir M, Berg A, et al. The relationship between individualized care and the practice environment: an international study. Int J Nurs Stud. 2015;52:121–33. https://0-doi-org.brum.beds.ac.uk/10.1016/j.ijnurstu.2014.05.008.

    Article  PubMed  Google Scholar 

  73. Seyyed Rasooli A, Zamanzadeh V, Rahmani A, Shahbazpoor M. Patients’ point of view about nurses’ support of individualized nursing care in training hospitals affiliated with Tabriz University of Medical Sciences. J Caring Sci. 2013;2:203–9. https://0-doi-org.brum.beds.ac.uk/10.5681/jcs.2013.025.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Dewi WN, Evans D, Bradley H, Ullrich S. Person-centred care in the Indonesian health-care system. Int J Nurs Pract. 2014;20:616–22. https://0-doi-org.brum.beds.ac.uk/10.1111/ijn.12213.

    Article  PubMed  Google Scholar 

  75. Williams MS, Buchanan AH, Davis FD, Faucett WA, et al. Patient-centered precision health in a learning health care system: Geisinger’s genomic medicine experience. Health Aff (Millwood). 2018;37:757–64. https://0-doi-org.brum.beds.ac.uk/10.1377/hlthaff.2017.1557.

    Article  Google Scholar 

  76. dos Reis S, Park A, Ng X, Frosch E, Reeves G, et al. Caregiver treatment preferences for children with a new versus existing attention-deficit/hyperactivity disorder diagnosis. J Child Adolesc Psychopharmacol. 2017;27:234–42. https://0-doi-org.brum.beds.ac.uk/10.1089/cap.2016.0157.

    Article  Google Scholar 

  77. McCabe MP, Beattie E, Karantzas G, Mellor D, et al. Consumer directed care in residential aged care: an evaluation of a staff training program. Aging Ment Health. 2019;24:673–8. https://0-doi-org.brum.beds.ac.uk/10.1080/13607863.2019.1574711.

    Article  PubMed  Google Scholar 

  78. Chaudhuri A, Lillrank P. Mass personalization in health care & colon; insights and future research directions. J Adv Manag Res. 2013. https://0-doi-org.brum.beds.ac.uk/10.1108/JAMR-05-2013-0033.

  79. Strauss A, Fagerhaugh S, Suczek B, Wiener C. The work of hospitalized patients. Soc Sci Med. 1982;16(9):977–86. https://0-doi-org.brum.beds.ac.uk/10.1016/0277-9536(82)90366-5.

    Article  CAS  PubMed  Google Scholar 

  80. Topol EJ. Deep medicine : how artificial intelligence can make health care human again. New York: Ingram Publisher services US; 2019.

    Google Scholar 

  81. Kertesz SG, Holt CL, Steward JL, Jones RN, Roth DL, Stringfellow E, et al. Comparing homeless persons’ care experiences in tailored versus nontailored primary care programs. Am J Public Health. 2013;103:S331–9. https://0-doi-org.brum.beds.ac.uk/10.2105/AJPH.2013.301481.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Volpp KG, Krumholz HM, Asch DA. Mass customization for population health. JAMA Cardiol. 2018;3:363–4. https://0-doi-org.brum.beds.ac.uk/10.1001/jamacardio.2017.5353.

    Article  PubMed  Google Scholar 

  83. Bayer R, Galea S. Public health in the precision-medicine era. N Engl J Med. 2015;373(6):499–501. https://0-doi-org.brum.beds.ac.uk/10.1056/NEJMp1506241.

    Article  CAS  PubMed  Google Scholar 

  84. Horton R. Offline: in defence of precision public health. Lancet. 2018;392(10157):1504. https://0-doi-org.brum.beds.ac.uk/10.1016/S0140-6736(18)32741-7.

    Article  PubMed  Google Scholar 

  85. Vuik SI, Mayer EK, Darzi A. Patient segmentation analysis offers significant benefits for integrated care and support. Health Aff. 2016;35:769–75. https://0-doi-org.brum.beds.ac.uk/10.1377/hlthaff.2015.1311.

    Article  Google Scholar 

  86. Dekkers T, Hertroijs DFL. Tailored health care: two perspectives on the development and use of patient profiles. Adv Ther. 2018. https://0-doi-org.brum.beds.ac.uk/10.1007/s12325-018-0765-2.

  87. Hunter PV, Hadjistavropoulos T, Thorpe L, Lix LM, et al. The influence of individual and organizational factors on person-centred dementia care. Aging Ment Heal. 2016;20:700–8. https://0-doi-org.brum.beds.ac.uk/10.1080/13607863.2015.1056771.

    Article  Google Scholar 

  88. Basu A, Carlson JJ, Veenstra DL. A framework for prioritizing research investments in precision medicine. Med Decis Mak. 2015;36:567–80. https://0-doi-org.brum.beds.ac.uk/10.1177/0272989X15610780.

    Article  Google Scholar 

  89. Dorfman EH, Brown Trinidad S, Morales CT, Howlett K, et al. Pharmacogenomics in diverse practice settings: implementation beyond major metropolitan areas. Pharmacogenomics. 2015;16:227–37. https://0-doi-org.brum.beds.ac.uk/10.2217/pgs.14.174.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Züger G, Honegger F. Essential requirements for the parameterization of food waste in hospitals. Int J Facil Manag. 2014;5(2):111–20.

    Google Scholar 

  91. Padula WV, Millis MA, Worku AD, Pronovost PJ, Bridges JFP, Meltzer DO. Individualized cost-effectiveness analysis of patient-centered care: a case series of hospitalized patient preferences departing from practice-based guidelines. J Med Econ. 2017;20(3):288–96. https://0-doi-org.brum.beds.ac.uk/10.1080/13696998.2016.1254091.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

No

Funding

No

Author information

Authors and Affiliations

  1. i3-Centre de Recherche en Gestion, Institut Interdisciplinaire de l’Innovation (UMR 9217), École polytechnique, Batiment Ensta, 828, Boulevard des Maréchaux, 91762, Palaiseau Cedex, France

    Etienne Minvielle

  2. Institut Gustave Roussy, 114, rue Edouard Vaillant, 94800, Villejuif, France

    Etienne Minvielle & Aude Fourcade

  3. University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

    Thomas Ricketts

  4. MOS (EA 7418), French School of Public Health, Rennes, France

    Mathias Waelli

Authors
  1. Etienne Minvielle
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aude Fourcade
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas Ricketts
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mathias Waelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

EM and AF made substantial contributions to the study design and drafting of the manuscript. TR made a substantial contribution to preparation of the manuscript. MW designed the study and reviewed the final version of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Etienne Minvielle.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix 1

Appendix 1

Boolean equation - Web of sciences Search Strategy)

  1. 1.

    (care customization OR personalized medicineOR personalized service OR targeting populations OR individualized care)

  2. 2.

    AND (segmentation analysis OR profiling patients OR categorizing patients)

  3. 3.

    AND (patient needs OR patient characteristics OR patient preferences OR population characteristics OR patient demands)

  4. 4.

    limit 1 + 2 + 3 to English

  5. 5.

    limit 4 to Article

  6. 6.

    limit 5 to yr = 2008–2019

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Minvielle, E., Fourcade, A., Ricketts, T. et al. Current developments in delivering customized care: a scoping review. BMC Health Serv Res 21, 575 (2021). https://0-doi-org.brum.beds.ac.uk/10.1186/s12913-021-06576-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://0-doi-org.brum.beds.ac.uk/10.1186/s12913-021-06576-0

Keywords

BMC Health Services Research

ISSN: 1472-6963

Contact us