Introduction
Electronic health records (EHRs) have great potential to improve the quality of care by promoting effectiveness, efficiency, timely, patient-centered care, safety and equity [
1,
2]. This digital technology has become a key strategy in health care reform efforts with the EHR adoption by health care organizations being incentivized by policies such as the Affordable Care Act and the Health Information Technology for Economic and Clinical Health Act. Research on EHRs has focused primarily on outcomes related to efficiency and effectiveness with positive findings related to accessing health care, reducing costs, monitoring population health care and improving disease specific outcomes [
3,
4]. However, there is also evidence showing how EHRs can be a detriment to health care delivery due to disruptions in workflow, provider resistance and lack of compatibility with existing health practices [
5]. This has led to calls for research examining the “black box” of EHR adoption to fully understand how adoption and sustainment influences clinical practice.
While the majority of the research on EHRs has been in medical settings, this study focuses on how they influence the delivery of person-centered care in behavioral health settings. Although slower to adopt than medical settings, a survey by the National Council for Behavioral Health in 2012 [
6] found that 56% of behavioral organizations used some form of EHR system and an additional 26% of agencies stated were planning to implement an electronic system in the near future. The predominant impetus for the adoption of EHRs within behavioral health has been large-scale efforts to integrate care. Driven by the poor health outcomes among people with severe mental illnesses, there is now widespread integration of primary care, mental health and substance abuse to ensure that people are treated holistically [
7,
8]. The electronic health record is the bedrock of these care coordination efforts, serving as the primary mechanism for facilitating communication among providers both within fully integrated settings and across networked providers [
9]. One of the most prevalent models within behavioral health has been the Health Home model which requires the adoption of an EHR system or utilization of HIT in some form [
10].
Integrated care is by definition person-centered care, a way of delivering services that pays attention to the whole person. Part of a larger movement within health care, person-centered care is broadly defined as care that is “respectful of and responsive to individual patient preferences, needs, and values, and ensures that patient values guide all clinical decisions” [
2]. Within integrated health care models, the emphasis has been on promoting the activated patient, who is empowered to be fully involved in making decisions and shaping their own care. Person-centered care is also a key component of the mental health recovery movement, a paradigm shift in service orientation that has been driving mental health reform since it became a federal priority in the early 2000s [
11]. Recovery challenges the medical model by reorienting care from symptom reduction to focusing care on supporting people on their own unique recovery journey [
12]. This entails providers working towards treatment goals that look beyond symptom management to helping people live a meaningful life in the community.
An emerging recovery-oriented practice is person-centered care planning, which uses the service planning process to develop and implement an action plan to assist the person in achieving his or her unique, personal life goals [
13,
14]. Providers complete a service plan with clients on entry into a program and then update the plan every 3 to 6 months. This plan addresses the specific mental health and/or substance use barriers interfering with the person’s goal achievement using both professional services and natural supports. Throughout the care planning process, providers elicit and empathize with their clients’ subjective experiences, regard clients holistically as people rather than as patients, and help people to articulate their personal recovery goals. Providers then reflect this process in the service plan wherever possible using the service user’s own words. Skills include reframing symptoms and impairments as barriers to goal attainment; reframing the use of medications as tools for overcoming these barriers and moving ahead in one’s life; instilling hope and identifying short-term, realistic, and measurable objectives. Overall, this approach is framed by a perspective that focuses on the application of clients’ strengths rather than the treatment of their deficits [
14].
Person-centered care is therefore reflected, communicated to others, and promoted by the service plan, which embodies both the spirit and specifics of treatment. As the act of service planning transitions over to a digital medium, this process is increasingly embedded within the electronic health record. In this regard, the functioning of the EHR and the delivery person-centered practices are necessarily shaping and shaped by one another. In a study examining the value of the EHR in the delivery of mental health care in Ontario, researchers describe how EHR driven initiatives did promote person-centered care but stressed that careful planning and consideration is needed to optimally integrate clinical decision support as EHR functions become more complex [
1].
Electronic health records have many purposes, including documentation for reimbursement and tracking population health data, but must also have the ability to capture individualized data that can be communicated among multiple providers and inform ongoing care. The shift from paper to electronic documentation has obvious benefits in terms of ease of access, less error, and more legibility, but how it impacts the quality of documentation is less clear [
15]. Research in nursing has found that person-centered care is often being practiced among providers but not documented [
16]. This finding reflects a common provider perception that documentation is “busywork” without clinical value and detracts from their therapeutic interactions with service users.
The reason for a lack of person-centeredness in documentation can be that the EHR itself is formatted in such a way that precludes providers capturing the individual person in terms of key biographical details [
17,
18]. In mental health settings, where a client’s narrative is a central part to the treatment plan, the need to fit these narratives to the constraints of the fields of entry is part of the service planning process. Triplett [
19] describes how open-ended questions in a clinical interview can become closed ended questions when they are driven by drop down boxes and computerized decision trees. The role of the template, while also a factor in service planning on paper, takes on an added significance as the information entered has multiple purposes and is made available to different providers.
Some research suggests that the impact of EHRs on clinical quality may change over time, particularly as a function of the organization’s stage of EHR implementation. For example, a systematic review examining the impact of EHR systems on clinical documentation found that, while providers spent more time on documentation immediately following EHR adoption, this effect may reverse over time, as both organizations and individual providers become more familiar with the system [
20]. Similarly, a study exploring the role of EHRs in integrating primary care and behavioral health found that optimizing the system was both an iterative and long-term process [
3]. Additional research has found that the unique clinical benefit of these systems can take between 2 and 4 years to be fully realized [
21,
22]. Consequently, considering the stage of EHR adoption, including how long a particular system has been in use, may be a critical component in understanding its differential impact on care.
In an extensive review of how patient-centered care can be facilitated by health information technology, Finkelstein [
23] found that “depersonalization” was one of the barriers cited in multiple studies and called for more research on the how health information technology impacts person-centered care. This mixed methods study explores how the electronic health record and its stage of development influenced the implementation of person-centered care planning in community mental health clinics.
Methods
Using a convergent parallel mixed methods design, the study utilized quantitative data to examine differences in fidelity to PCCP among clinics according to EHR development stage and qualitative data to explore the role of the electronic health record in PCCP fidelity from the provider perspective. This study was part of a larger multi-site randomized controlled trial of person-centered care planning (Authors, 2013). The study received approval from a university Institutional Review Board.
Study setting
The study was set within five mental health clinics across two states that had been randomized to the PCCP condition in the randomized controlled trial. These clinics provided a range of services including outpatient therapy, crisis intervention, medication management, case management, residential programs, community support programs, and rehabilitation services. Within these clinics, leadership, supervisors, and direct care staff participated in the study. The clinics were selected for this study on the basis that they were in the experimental condition and were actively using an EHR. Each of the clinics received training in PCCP, which consisted of a two-day in-person training session at the agency followed by monthly technical assistance (TA) calls facilitated by external PCCP consultants over a 12-month period. Each clinic had two TA calls a month. One call was with supervisors to reinforce their translation of the practice to their direct care provider teams and one call was with a clinic team which provided a service plan for feedback from the consultants.
Qualitative methods
Qualitative data was collected from 11 focus groups, 12 interviews and 24 consultant reports from each site. The focus groups and leadership interviews asked questions about the implementation process of PCCP. One supervisor focus group and one direct care focus group was conducted at each of the five clinics except for one clinic in which two direct care focus groups were conducted due to the large number of direct care staff participating. Twelve in-depth interviews were conducted with participant executive leaders of the clinics. The third data source was consultant reports that were completed after each of the bi-monthly technical assistance calls. Consultants were external experts who conducted the trainings and led the technical assistance calls. Using a template, the consultant completed a monthly narrative report on progress made in the implementation of PCCP, including barriers, strategies to address barriers, and leadership involvement for each clinic.
At the conclusion of the 12-month intervention period for each site, focus groups and leadership interviews were conducted at each experimental site to explore barriers and facilitators to implementation. Topical domains addressed during the focus groups included: perspectives on PCCP, experiences with implementing PCCP and training staff, including the role of the electronic health record. Each of the focus groups was conducted by two masters-level interviewers with experience working in community mental health clinics and training in the PCCP intervention. Focus groups lasted approximately 60 min. All participants received $20 compensation for their time. The qualitative sample included 31 clinical supervisors participating across the five supervisor focus groups and 52 direct care staff participating in the six direct care focus groups. Focus groups consisted of three to twelve participants each. In-depth interviews were completed individually with each of the 12 executive leadership participants.
Focus groups and interviews were digitally recorded and transcribed verbatim with names and identifying information removed. Transcripts were then entered into
Atlas.ti for data management and analysis. Analysis of all qualitative data was conducted by three researchers, who examined the five cases both within developmental categories and across these categories to discern similarities in experiences. Data was analyzed within each agency and then compared to other agencies within that category. Any inconsistencies in coding were resolved through consensus. In addition, consultant reports were analyzed by two researchers using inductive, thematic analysis to capture commonalities both between and within sites. Data was organized using memos, which provide a structured summary of key experiences and themes [
24]. Memoing is an approach often associated with grounded theory [
25] but has become a commonly employed tool across qualitative methods as a mechanism to draw out core meanings within complex data and facilitate team debriefing [
24]. To ensure trustworthiness throughout the coding process, the researchers engaged in strategies to ensure rigor, including weekly team debriefings and the use of an audit trail [
26].
Quantitative methods
A chart review of service plans completed by study participants was conducted by researchers trained in the PCCP intervention. At each of the 5 clinics, service plans were sampled from plans completed by providers that had received PCCP training. Twenty service plans were randomly selected at each of the timepoints (baseline, 12 months and 18 months) generating a total sample of 300 service plans. Each service plan reviewed was from a unique client.
Clinics were categorized according to the following development stages of their electronic health record: 1) electronic health record established and no change planned (N = 1); 2) electronic health record in transition with changes being actively implemented (N = 2); and 3) electronic health record established but further changes planned (N = 2). Clinic 1 was categorized as ‘no change’. This site had an existing EHR that remained stable throughout the PCCP implementation process, with no major transitions related to their system. Clinics 2 and 3 were categorized as ‘changing EHR’, as both were in the process of rolling out a new system at the baseline study period. Clinic 2 was in the midst of a phased roll-out of a new EHR system, while Clinic 3 had just implemented a new EHR across all programs. Finally, Clinics 4 and 5 were categorized as ‘planning change’. These sites both maintained an existing electronic system but were actively planning to transition to a new EHR platform during the implementation process. Clinic 4 was planning to upgrade from a basic computerized system to a commercial EHR, and Clinic 5 was part of a larger health care network that was collectively transitioning from one commercial system to another.
PCCP fidelity was measured by an objective measure, the PCCP Assessment Measure (PCCP-AM), that was applied to the service plans selected for chart review. This 13-item measure was developed by experts based on the PCCP manual [
14]. The items assessed person-centeredness and technical proficiency across the key service planning domains including assessment, narrative summary, objectives, goals and interventions. The items were rated from 1 to 4 indicating level of competency: 1 indicated “needs improvement”; 2 indicated “approaching standard”; 3 indicated “meets standard”; 4 indicated “exceed standard.” For the purposes of the study, the scores were dichotomized by recoding items which scored 1 or 2 into 0 (not competent) and items which scored 3 or 4 into 1 (competent). Scores for each service plan ranged from 1 to 13 with higher scores indicating greater person-centeredness. For the parent study, 12 service plans were co-coded by two raters at each of the 14 research sites, which yielded an inter-rater reliability of 80%. The PCCP-AM had a Cronbach’s Alpha of .7 when calculated across all the service plans reviewed in the parent study (
N = 798).
Univariate statistics were used to report PCCP fidelity at baseline, 12 months and 18 months. At each of the three time points, an ANOVA was run to test for differences in PCCP implementation between sites by the three stages of EHR adoption: planning a change, changing, or no change.
Discussion
The study findings explored the role of the EHR in the implementation of a new practice initiative aimed at promoting person-centered care in behavioral health settings. When comparing stage of EHR and PCCP fidelity, the clinic with the most established and high functioning EHR demonstrated the highest level of fidelity both at the baseline and 18 months. Clinics differed significantly in their PCCP fidelity according to their development stages suggesting it to be a salient implementation factor. The qualitative findings provided insight into each clinic in terms of their difficulties related to individualizing the record and their capacity to address these difficulties. A fundamental aspect of the PCCP intervention is to have service planning flow out of personalized goals that were meaningful to the service user and be able to express these goals and their objectives using the service user’s own words. Drop down boxes and predetermined goals that created the logic and workflow of these clinics’ EHRs could not adequately reflect the uniquely varied nature of goals determined by PCCP. This limitation made it impossible for providers to practice according to the recovery model, which acknowledges the uniqueness of each person’s recovery trajectory and that goals must be personal and meaningful. Problem solving on how to address these barriers consumed a considerable part of the technical assistance calls for most clinics taking away time spent reflecting on the more clinical aspects of the practice.
Some of the problems that clinics encountered were more generic to launching a new practice initiative while simultaneously making changes to the electronic health record. The clinics that were actively changing their EHRs experienced technical difficulties and general disruption due to their EHR being in flux. The clinics that were planning changes to the EHR also faced technical problems, which in part were driving the need to upgrade their systems. The findings reflected the iterative nature of embedding an EHR system into a clinic, which can create challenges for the day-to-day clinical practice. This echoes prior findings which demonstrate that while there can be benefits in terms of efficiency and coordination care are clear, the reality of shifting to EHR systems can often undermine these goals leading to mixed outcomes [
3,
27].
Solutions were found either in the form of customization, or if that was not possible with the clinic’s software by devising “workarounds”. The clinic with a well-established EHR had the flexibility and support to make the necessary structural changes relatively easily. Similarly, Goh and colleagues [
5] found that a key part of a successful EHR transition was having the capacity to modify a HIT system quickly when it is found to be incompatible with a routine practice, or in this case a new practice. The clinics which were not able to respond quickly to these barriers were the clinics that had less control over the formatting of their record due to being part of a larger system or sharing EHR infrastructure with other clinics. The only option for the clinics that could not alter their record was to develop “workarounds”, which in the case of individualizing the record meant “picking other” to have the option to enter free text or editing pre-determined goals to reflect individual preferences. These workarounds while enabling a more person-centered approach were time consuming and undermined the efficiency and uniformity of the data entry. Workarounds are a common compromise in using electronic health records and often considered to be essential by providers [
28]. However, others have argued they ultimately reflect “mismatches between the capabilities of the existing HIT system and the clinical practices need to perform” (3, p.S65).
The reason for mismatches with clinical practices is partly rooted in the fact that the EHR does not have the sole purpose of facilitating person-centered care but instead has multiple purposes that often require uniformity for the purposes of aggregation. The increased focus on improving population level outcomes and the potential for the EHR to support this goal is an incentive for constructing EHRs in ways that promote aggregation [
29]. While person-centered care and population level care both represent key elements of the Triple Aim – they require potentially contrary EHR functionality. Another predominant use of the EHR is to inform billing, which requires establishing “medical necessity criteria” for multiple clients across different programs in a way that is feasible for the agency. This inevitably leads to uniformity in the ways the problem and the solutions are conceptualized, which in turn drives more medical model approaches to care. As several of our agencies experienced, the logic of the record flows specifically from the diagnostic problem rather than personalized goals. This study demonstrated that despite electronic health records being a key strategy in improving quality through coordination of care and efficiency, the EHR can also be a barrier to the delivery of person-centered care.
Limitations
There are several limitations to this study which indicate the need for further exploration of this subject area. First, there are other factors not accounted for in our quantitative analysis that could have led to variation in fidelity to PCCP, though qualitative data supports the finding that the EHR had a substantial influence on the implementation process. Because of this, while it can be concluded that EHRs played a significant role in the adoption of this new practice, these findings cannot quantify the magnitude of this influence, or speak to the isolated role of EHRs relative to other factors that may have also impacted implementation. Furthermore, only one site experienced no change in their EHR system, minimizing variation within this category, and limiting the differential impact EHR change stage had on PCCP fidelity.
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