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Improving quality of service of home healthcare units with health information technologies Juan Gabriel Cegarra-Navarro, Anthony K.P.Wensley and Maria Teresa Sánchez-Polo

Abstract Deployment of health information technologies (HITs) provides home care units with the means to generate improvements in accuracy and timeliness of information required to meet dynamic patient demands and provide high quality patient care. Increasing availability of information can also facilitate organisational learning, which leads to the invocation of processes that result in improved responses and decisions. This study examined crucial links between HITs and quality of service provided through an empirical investigation of 252 patients in a hospital-in-the-home unit (HHU) in a Spanish regional hospital. The study sought to test the relationship between HITs and the quality of service using factor analysis and structural equation modeling (SEM) to investigate how HITs mediate effects of organisational learning on quality of service. Findings support the notion that the relationship between organisational learning and quality of service can be mediated by HITs. This study provides HHU managers with guidelines for understanding the role of organisational learning processes with respect to HITs and quality of service. Key words (MeSH): Learning, Information Science; Information Management; Health Services; Health Information; Quality Improvement; Hospital Home Care Services Supplementary term: Organisational Learning Introduction Home healthcare units (HHUs) can improve quality of life for patients in a variety of ways, ranging from a reduction in the variety and severity of risks typically associated with hospital admissions to a decrease in the level of care the family needs to provide (Gideon et al. 1999). To capture potential benefits (or the lack of) measures of HHU should include performance standards (e.g. the HHU responds to patient inquiries in a timely manner) and measures to gauge the extent to which the HHU follows internal procedures, directives, regulations or technical aspects of the relationship between healthcare delivery processes and patients (Lockamy & Smith 2009). The composite measure of quality of service (QoS) fulfils these two objectives because effective deployment of services requires healthcare practitioners to use appropriate technologies and apply their knowledge to critical healthcare delivery processes, which in turn influences patients’ perception of value and satisfaction with these services (Asubonteng et al. 1996). Quality of service is determined, at least in part, by development and application of health information technologies (HITs), which enable provision and maintenance of care services. For instance, when services are provided in a homecare setting, patients can access information from the Internet and potentially contribute to their own health care (Field 1996), and patient satisfaction may increase along with quality of care. However, for HITs to reach their full potential, they must be implemented in line with medical process that HHU practitioners routinely use. HITs that can provide exchange of rich information may require significant modification of existing routines or adoption of radically 30

new routines. Reardon and Davidson (2007) pointed out that introducing HITs into the learning environment can encourage collaboration and build on patients’ desire to communicate and share understanding. This, in turn, facilitates organisational learning because adapting to and deriving benefits from change, whether instigated through the adoption of HISs or otherwise, requires that organisational learning take place. This perspective on organisational learning is based on the work of March (1991), who suggested that learning is a dynamic process consisting of two sub-processes: (i) exploration of new knowledge and skills and (ii) exploitation of existing knowledge, skills and processes. The aim of this paper is to raise awareness of homecare practitioners of the necessity of connecting previous learning with successful implementation of HITs. The authors hope this study may assist home healthcare agencies to understand better the nature and role of previous knowledge in the successful implementation of HITs as well as the potential benefits in terms of improved quality of service delivery.

Conceptual framework HITs can be viewed as both computer hardware and software that deal with the storage, retrieval, sharing, use of healthcare information, data, and knowledge for communication and decision-making (Brailer & Thompson 2004). Utilisation of HITs has potential to enhance quality value of service delivery and support multi-functional and inter-organisational communications (Chaudhry et al. 2006). Bhatt, Gupta and Kitchens (2005) identified groupware that capture, store, and manipulate information, and HITs such as email and mailing lists

HEALTH INFORMATION MANAGEMENT JOURNAL Vol 40 No 2 2011 ISSN 1833-3583 (PRINT) ISSN 1833-3575 (ONLINE)

Research support knowledge management process. (Appendix 1 includes a full list of HITs included in this study). The growth in variety and capabilities of HITs has provided many opportunities for HHUs to facilitate communication and collaboration between colleagues, patients and carers while patients are in the home environment (Lockamy & Smith 2009). HITs allow exchange of information between non co-located hospital staff and patients, resulting in potential improvements in delivery of care, particularly with patients in a home-care setting. Through the use of HITs patients are able to augment their learning with respect to relevant healthcare issues, generate new knowledge, and obtain feedback from other users (e.g. practitioners and other patients). Taking the Internet as one example, Robinson et al. (1998) argued that it has not only facilitated healthcare information acquisition by patients but it has also changed the nature of patient-physician interactions and facilitated a variety of cost-reduction strategies. For example, practitioners can upload information about appointments as well as new techniques or research protocols and data relating to experiments that they are carrying out. Although HITs can be effective tools to achieve a HHU’s objectives, there are difficulties associated with using HITs in a home healthcare setting. Confidentiality and security issues may present challenges for practitioners attempting to access sensitive patient information stored on a remote server; and implementation of HITs often requires modifications to existing routines and processes or their replacement by new routines and processes (Cegarra & Cepeda 2010). It is important to remember that existing routines and processes have typically co-evolved with paper-based information systems and may have many characteristics contingent on such systems (Winthereik & Bansler 2007); and information available in one setting may not be easily available in another setting and routines will have to be adjusted or augmented to reduce the incidence of adverse events and medical errors. For example, a portable pulse oximeter may not provide information with respect to the patient’s carbon dioxide levels and the amount of oxygen being used, so implementing routines that depend on richer information provided by non-portable pulse oximeters in a home healthcare setting will depend on other ways to obtain the additional information (Campbell et al. 2007). Applying new and existing HITs in new settings frequently generates problems when information provided or routines supported conflict with current knowledge (Starbuck 1996), for example, patients and practitioners may use different terms, their understanding of HITs and how they function may be different, and each may operate from a different knowledgebase, which can lead to inappropriate actions and potential misunderstandings. Starbuck (1996) noted that using new technology typically requires a change in people’s knowledge, habits and routines, which requires that they forget old knowledge, habits and routines and replace them with new ones. Therefore, an organisa-

tional learning process that improves existing skills and knowledge and extinguishes outdated routines and knowledge is an essential requirement for organisations generally, and HHUs in particular. Organisational learning, at its heart, facilitates creation and application of new knowledge and new knowledge structures by reorienting organisational values, norms and/or cognitive structures (Crossan, Lane & White. 1999); it involves the acquisition, distribution, interpretation and storage of knowledge that facilitates a rapid improvement of business processes, tools and methods whose improvements are critical to success. Researchers distinguish between processes that support the exploration of knowledge and those that support the exploitation of knowledge. Bontis, Crossan and Hollund (2002) and Mom, van Den Bosch and Volberda (2007) maintained that the essence of exploration is to create variety in experience, associated with broadening a manager’s existing knowledgebase; while the essence of exploitation is to create reliability in experience, associated with deepening a manager’s existing knowledgebase. Put another way, knowledge exploitation refers to the effective and efficient allocation of resources into valuable and competitive organisational platforms based on existing knowledge. While knowledge exploration retains knowledge within the organisation, in the healthcare situation, typically the hospital, knowledge exploitation may well release the knowledge into the external environment. In the context of home healthcare, provision of sub-activities associated with knowledge exploitation include targeting output to a particular target (patient, patient’s family, healthcare providers) and producing output involving activities such as interpreting, packaging and delivering information for the target (Bohmer & Edmondson 2001). We would add that HHU can further encourage the process of knowledge exploration by implementing some combination of formal or informal meetings between internal and external parties or by creating external communities of practice where patients and practitioners interact and work together in order to achieve mutually beneficial objectives. Through the development of relational trust, common language and confidence, organisational members are able to articulate, share and internalise knowledge (Cegarra & Cepeda 2010). In the healthcare environment, organisational learning impacts upon several elements of organisational experience that contribute to quality, such as nursing care, job satisfaction, and patient safety (Aiken et al. 2002). However, the knowledge resulting from the two aspects of organisational learning that we have proposed (viz. exploration and exploitation) might be stored in different forms and locations, including HITs (Nonaka & Takeuchi 1995). There is an ongoing dynamic interaction between knowledge created through exploitation and exploration and the existing stock of individual and organisational knowledge: improved understanding of one stage becomes the precursor for the next (Klein & Myers 1999).

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Research Further, as Darroch and McNaughton (2002) indicated, successful implementation of technical innovations requires concerted effort as well as experience in recognising and obtaining new knowledge. Otherwise, lack of previous learning may lead individuals to duplicate work, develop conflicting data exchange protocols and apply incompatible business models (Rodgers, Chen & Chou 2002). The learning process provides a way to explain and resolve problems associated with implementing and using HITs (Robey, Boudreau & Rose 2000), and supports HHUs to modify or develop new routines and knowledge by providing necessary external and internal knowledge (Rodgers, Chen & Chou 2002). Where some HITs have potential to provide ways of classifying and preserving what has already been learned, they can also be viewed as providing secondary steps in learning (Sorensen & Lundh-Snis 2001). This consideration allows us to frame the first and second hypotheses relating to our research: H1: The level of knowledge exploration impacts positively on HITs. H2: The level of knowledge exploitation impacts positively on HITs. The relationship between the use of HITs and quality of service has received considerable attention in the literature. Barlow et al. (2007) reported that although almost 9,000 studies reporting on telecare trials and pilot projects have been published, little strong conclusive evidence has emerged pointing to factors that lead to successful telecare implementation. Whitten and Adams (2003) reported that considerable investments in HITs have failed to boost performance in a healthcare setting, and more successful projects relating to the development of telemedicine applications possess a more formalised organisational structure. Gagnon et al. (2006) pointed to the relevance of a more contingent approach that emphasises the importance of investigating the context in which HITs exists prior to implementation. Thus, empirical studies provide mixed support for the hypothesis that the use of HITs has a direct effect on performance. We propose that the use of HITs allows patients to gain a deeper insight into their situation and health challenges, enabling them to make more informed decisions. When patients visit a health-portal or e-portal they are in a powerful position because they can exercise control over data and information provided about themselves, and decide whether or not to engage in the relationship (Leben et al. 2006). Such actions lead to improved response times, improved quality of care and knowledge sharing and creation. We argue that HITs can be important tools to help hospital administrators to meet patient needs by providing access to more and better information, aid in routine administrative tasks, and provide models and simulations of effective learning practices. HITs can also enable learner support networks, both in face-to-face and distance learning environments, and provide for interaction in real time or asynchronously (Lockamy & Smith 2009). This leads to improved patient service levels and a higher level of perceived quality 32

(Asubonteng et al. 1996). As Barlow and Hendy (2009) noted, the main beneficiaries of HITs are patients and family carers, through the provision of independence, security, confidence, quality of life, and ability to stay in one’s own home. The hypothesis we propose under this framework is: H3: The use of HITs will affect the patient’s assessment of the quality of service provided.

Method Sample and data collection To test these three hypotheses, patients in a Hospital-inthe-Home Unit (HHU) in a Spanish Regional Hospital with a capacity of 880 beds were considered. This HHU covers all necessary medical services, including orthopedic surgery, gynaecology, obstetrics, anaesthesiology, radiology and a clinical laboratory; coordinates treatment; offers psychological and social services; and provides in-patient, emergency, post-discharge and alternative care, such as hospice home healthcare. It was established in April 1998, with a few hospitals in almost every other city in the Region developing similar programs. Initially, this unit was founded to improve hospital patient flow, creating greater acute care capacity. Ethical approval was granted by the Research Ethics Board of the participating hospital and the program was approved by the hospital’s HHU Board in February 2007. We chose to study this HHU for two main reasons: (a) despite patient satisfaction reported as high (see Sanchez et al. 2007), evaluation of the underlying causes of these high levels of satisfaction was underdeveloped (Baño et al. 2007); (b) HHU is an ideal platform for implementing telemedicine networks because two or more individuals (e.g. patients, carers, doctors and nurses) work together with different web-based technologies and complementary capacities, which are changing enabler factors (Yu & Yang, 2006). Therefore, the HHU at this hospital was an appropriate setting for an investigation of organisational learning and its impact on HIT, with web-based interaction allowing for exchange of information within the patients’ social context, which may make consultation more effective for all participants (Sanchez et al. 2007; Baño et al. 2007). HHU professionals at this hospital offer a service that is both high quality and compassionate; they turn homes into ‘healing environments’ where selected patients and their families learn to provide appropriate care. Selection criteria for patients’ participation in HHU care services include a stable medical condition that can be managed at home without unexpected emergency interventions, availability of a carer, an appropriate standard of housing, a telephone connection, and patient consent. Patients are visited and intravenous drugs administered by the HHU nursing team comprised of four nurses. Every patient receives a written Emergency Plan explaining the 24-hour telephone backup service, which is provided by an HHU nurse and the HHU director. The HHU medical members

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Research (two internal medical doctors) undertake medical supervision while the patient is at home and carry out ‘ward rounds’ every day. At the conclusion of treatment, the patient, who has retained the full status of a hospital inpatient throughout the period of HHU care, is formally ‘discharged’.

Measures We used Churchill’s (1979) approach to questionnaire development, combining scales from several other relevant empirical studies with new items to make an initial list of 26 items (four measuring range of knowledge exploration; four measuring knowledge exploitation; 13 measuring the existence of HIT, and five relating to quality of service). Appendix 1 provides an overview of the final 26 questions.

Knowledge exploration and knowledge exploitation Based on Mom et al. (2007), we adopted a broader notion of organisational learning. While the Knowledge Exploration Scale (ER) determines the extent to which the HHU supports activities that encourage individuals to learn new skills or knowledge by tracking changing markets and sharing market intelligence with patients and other external agents (Mom, van Den Bosch & Volberda 2007), the Knowledge Exploitation Scale (ET) focused on utilisation of knowledge embedded in the HHU to develop plans and response to implementation of plans (Bontis, Crossan & Hulland 2002).

Customer perceptions of service quality SERVQUAL, a multi-item scale first proposed by Parasuraman, Zeithaml and Berry (1985), was used to measure customer perceptions of service quality across a wide variety of service environments, including healthcare in the USA (Carrillat, Jaramillo & Mulki 2007). The version of the scale used was based on the work of Carrillat, Jaramillo and Mulki (2007) and adapted to the context of the study by the authors. The scale consists of five statements to measure performance across five dimensions that indicate whether or not patient expectations have been achieved: (i) physical facilities; (ii) service performance ability; (iii) prompt and helpful service; (iv) ability to inspire trust and confidence; and (v) caring individual attention. Based on patients’ answers to these questions, we created a new variable with a minimum value of zero and a maximum value of five. (See Appendix 1 for details).

The HIT Scale Measures relating to the existence of the HIT Scale consisted of 13 items adapted from a scale designed by Bhatt, Gupta and Kitchens (2005) to measure features of technologies associated with communication modalities (e.g. fax, email, voicemail). Respondents were asked to represent the current status of HIT from their point of view on a dichotomous scale of 13 items (See Appendix 1). From answers respondents supplied, we found a new variable with a minimum value of zero and a maximum value of 13; where zero was not at all and 13 was to a very large extent.

Reliability and validity of measures Research models and hypothesised relationships were empirically tested using structural equation modelling (SEM), supported by EQS 6.1 software (Bentler 1988). EQS was selected because of the characteristics of our model and sample. Our data are non-normal and other techniques of structural equation modelling (e.g. the covariance-based model performed by LISREL or AMOS) cannot be applied in these circumstances (e.g. see Diamantopoulos & Winklhofer 2001). Using EQS entails a two-stage approach (Bentler 1988). The first step requires assessment of the measurement model, which allows relationships between observable variables and theoretical concepts to be specified. This analysis is performed in relation to attributes of individual item reliability, construct reliability, average variance extracted (AVE), and discriminant validity of indicators of latent variables. In the second step, the structural model is evaluated, to test the extent to which causal relationships specified by the proposed model are consistent with available data.

Data analysis and results Hypotheses were tested simultaneously using structural equation modelling (SEM), supported by EQS 6.1 software (Bentler 1988). SEM used 252 cases. Referring to hospital records, we pre-selected all patients admitted to the home care unit during 2007, which resulted in 300 patients being contacted by the HHU and asked to participate in the study, 252 of whom agreed to participate (response rate=84%). In March 2008, we conducted telephone interviews using a simple structured questionnaire. Items on the proposed model were evaluated with exploratory techniques to assess reliability and dimensionality of measures. Initially, each construct was assessed using item-to-total correlation and exploratory factor analysis. The decision to retain items was based on Hair et al.’s (1998) recommendation with regard to statistical criteria (loadings and regression weights), which resulted in two items being dropped (ER3 and ET4). Thus, psychometric properties of measures improved the original proposal. To achieve a more robust evaluation of the quality of measures, a confirmatory analysis was performed using the covariance matrix as input via the robust maximum likelihood method. In addition, fit indices that are less sensitive to non-normal data (Satorra-Bentler χ2, comparative fit index and incremental-fit index) were used to interpret the model fit. The Satorra-Bentler χ2 difference test was employed using available software (Crawford 2007) to provide a significance test of the relative goodness of fit between nested models. With regard to the measurement model, we began by assessing individual item reliability. The fit statistics for the resulting eight items, which are summarised in Table 1, indicate a reasonable data fit. The fit index of the Root Mean Square Error of Approximation (RMSEA) is below .08, and the Goodness of Fit Index (GFI), the

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Research exceed this condition (see Table 2). Discriminant validity was found to be robust, with confidence intervals (± 2 standard errors) around estimated correlations between any two latent indicators never including 1.00 (Anderson & Gerbing 1988). Hypothesis tests resulting from structural equation modelling analyses are summarised in Table 3, where the explained variance of endogenous variables (R2) and standardised path coefficients are shown. The fit of the model is satisfactory (GFI=.942; CFI=.892; IFI=.901), suggesting the nomological network of relationships fits our data, another indicator of support for validity of these scales (Churchill 1979). The hypotheses were evaluated by examining R2 values and size of structural path coefficients. All hypotheses presented were significant and verified. The hypothesised link between level of knowledge exploration and HITs (H1) was supported (γ=.675 at a level of p=.032); and knowledge exploitation was found to have a positive and strong influence on HITs, which provided support for H2 (γ=.200 at a level of p=.032). With respect to H3, the effect of HITs on quality of service was fully verified (β=.853, p=.030). According to variance explained by each construct, explorative and exploitive constructs explained 6.1% of the ‘HITs’ construct, and HITs explained 25.5% of quality of service.

Comparative Fit Index (CFI) and the Incremental Fit Index (IFI) are above the common standard of 0.9 (Hair et al. 1998). These results suggest the use of a single variable with a minimum value of zero and a maximum value of 13 to measure the existence of the HIT, and the use of another single variable with a minimum value of zero and a maximum value of 5 to measure quality of service. For selected explorative and exploitive measures, Bagozzi and Yi´s (1998) composite reliability index and Fornell and Larker´s (1981) average variance extracted index are higher than the evaluation criteria of .7 for the composite reliability and .5 for the average variance extracted. These results suggest the use of three items (ER1, ER2 and ER4) to measure knowledge exploration (pcSCR=.871, pcAVE=.702) and another three (ET1, ET2 and ET3) to measure knowledge exploitation (pcSCR=.850, pcAVE=.664). The constructs correlation matrix, shared variances, means and standard deviations are shown in Table 2. Examination of these results shows that all constructs are reliable. On average, each construct is more strongly related to its own measures than to others (Fornell & Larcker 1981). The AVE should be greater than .5, meaning that 50% or more variance of the indicators should be accounted for (Fornell & Larcker 1981). Explorative and exploitive constructs of our model

Table 1: Confirmatory factor analysis and scale reliability

Note: a b na

CONSTRUCT

VALUE

t-VALUE

RELIABILITY (SCRa, AVEb)

ER1: Co-operation and search in social networks for new possibilities with respect to services or processes ER2: Co-operation and search in social networks for the renewal of outdated services or processes ER4: Work meetings with patients and carers searching for new possibilities with respect to services or processes

.561

7.227

SCR=.871

.763

3.898

AVE=.702

.461

8.692

ET1: Homecare goals are communicated to patients and their families ET2: Policies and procedures aid practitioner work ET3: Homecare operational procedures allow your homecare practitioners to work efficiently Health information technology HIT: Quality of service QS:

.735 .894 .525

6.223 2.787 12.308

SCR=.850 AVE=.664

1.000 1.000

– –

na na

The fit statistics for the measurement model were: Satorra-Bentler χ2(14)=40.95; χ2/d.f= 2.925; GFI=0.955; CFI=0.921; IFI=0.924; RMSEA= 0.076; Scale Composite Reliability (SCR) of pc= (Σλi)2 var (ξ) / [(Σλi)2 var (ξ) +Σ θii] (Bagozzi and Yi, 1998); Average variance extracted (AVE) of pc= (∑λi2 var (ξ))/[∑λi2 var (ξ) + ∑θii] (Fornell and Larcker, 1981). The asymptotic covariance matrices were generated to obtain the scaled chi-square (Satorra & Bentler 1988) and robust estimation of standard errors; not applicable.

Table 2: Descriptive statistics and correlation matrix M

SD ER

1. 2. 3. 4.

Knowledge exploration (ER) Knowledge exploitation (ET) Health information technology (HIT) Quality of service (QS)

5.157 4.015 2.433 3.258

1.216 1.769 3.162 1.315

.702 -.104 .032 .302 ***

CORRELATION MATRIX ET HIT

.042 .664 .220 *** .068

.000 .054 na -.124 **

QS

.113 .006 .015 na

Note.

***