Denise Calaprice-Whitty

Recent innovations in technology and information flow have created a population of patients that is active in, and has high expectations of, their healthcare and treatment choices. This shift in the marketplace requires that key stakeholders in the clinical trial process understand the perceptions and motivations of their newly empowered customers. It is to aid this understanding that the Avoca Quality Consortium (AQC) commissioned a patient survey as part of its ongoing commitment to transform clinical trials. The overarching objective of the survey was to identify actions that pharmaceutical companies, CROs, and study teams might take to positively influence clinical trial participation and impressions of the clinical trial experience, and to increase the relevance of clinical trial results to patients.

	Traditionally, clinical development teams seeking to understand patient experiences and motivations have relied heavily upon healthcare professionals as proxies for the patients themselves. Healthcare providers have partnered with clinical development teams both to define what represent clinically meaningful outcomes, and to address drivers of patient participation and retention in clinical trials. At present, however, the sufficiency of this approach is being questioned.

 With the advent of the internet and social media, researchers now have an unprecedented level of direct access to the patient “voice,” and gaps between perceptions of the patient experience that have developed through the traditional model, and what we are hearing from patients directly, have been revealed. Hence, one of the objectives of the survey was to evaluate the extent to which patients perceive that their medical care providers understand the patient experience, and to understand how this may vary across patients with different types of illnesses and different demographic characteristics.

	Despite increased levels of patient interest in treatment options, appreciation of clinical trials as a viable care alternative remains limited.

 The factors that drive patients’ decisions to enroll and remain in clinical trials play a key role in decisions not only about protocol design, but also about site selection, expectations of and training for site staff, technologies employed, information provided, etc., and thus are important for industry professionals to understand. For this reason, another key objective of AQC’s 2016 Patient Survey was to understand how patients view clinical trial participation: how they feel about the “quality” and value of clinical research, their confidence level in the clinical trial process and the information with which clinical trial patients are provided, and why they would or would not choose to participate in a clinical trial. Again, the survey sought to understand not just “general” patient perceptions in this area, but also the disease and demographic characteristics that drive differences across patient subsets.

	This article presents the results of the Avoca Quality Consortium’s patient survey. This was a standalone survey, but incorporated parallels with questions that had been asked in other AQC and Avoca surveys of sponsors and clinical survey providers, to allow for comparisons where applicable. After the research plan and survey were reviewed and granted exemption status by Western IRB, potential participants were contacted by email using email addresses from the PMG Research, PLM (PatientsLikeMe), CISCRP (Center for Information and Study on Clinical Research Participation), and Avoca Quality Consortium databases, all of which are “opt in” databases comprised of individuals who have elected to receive email contacts from the relevant organizations. The introductory emails notified prospective participants about the survey, and included a description of its objectives, the types of participants being recruited (adults who self-identify as patients with any medical condition), and the fact that the survey was anonymous. In the email, potential participants were provided with a link to The Avoca Group’s web-based survey platform, where they were first directed to an Informed Consent page providing information about the study, asked to verify that they were at least 18 years of age and US Citizens, and then given the option to consent or decline. Those who both qualified and consented were then directed to a webpage where they could complete the survey anonymously.

	The survey itself was comprised of: non-identifying demographic questions; questions about perceptions of how well medical service providers, research professionals, and regulators understand their conditions; and questions about confidence in the clinical trial process. Approximately 10-20 minutes were required for completion, and no question required a response to continue with the survey. All statistical analyses were performed using JMP software.

	Five hundred and eighty-two patients participated. Participants represented those with chronic (59%), degenerative (29%), episodic (9%), and temporary (1%) conditions, as well as a few who didn’t know how to designate their conditions (Figure 1). The impact of these patients’ conditions on their day-to-day lives ranged from virtually none (7%) to severe (29%), with medical care required between once or less per year (12%) and more than 12 times per year (10%). Thirty-seven percent were employed; 56% held at least a college degree; and 42% supported families. Most were female (73%), married (54%), and/or over the age of 45 (82%).Forty-five percent of patient participants had participated in a clinical trial in the past.

	On average, survey participants did not feel that their healthcare providers had a very good understanding of what it was like to be a patient with their condition. Mean “perceived understanding” ratings ranged from only 2.9 to 3.4 on a scale of 1 (no perceived understanding at all) to 5 (perceived to understand “very well”), and for each question, between 26% and 39% of patients gave a rating of 1 or 2 (Figure 2).

	Figure 2. Patient perceptions of how well their healthcare providers understand what it’s like to be a patient with their condition

	As can be seen from the distributions of ratings given for each question, there was pronounced heterogeneity across patients in the extent to which they felt understood. Multivariate analyses that accounted for multiple disease and demographic characteristics at once were performed to better understand this heterogeneity. In these models, the strongest drivers of differences across patients were age group (patients aged 76+ generally felt the most understood by their providers, and those aged 31-45 the least), marital status (single and married patients generally felt the most understood, and divorced and widowed patients the least), and role (or not) in providing financial support or care for family members (providers generally felt less understood than non-providers). Disease characteristics (as depicted in Figure 1) and history of clinical trial participation had relatively little impact on these results (data available upon request).

	Themes in patients’ open-ended explanations for their responses included: a perceived inability for anyone to understand the patient experience/pain/symptoms; lack of vocabulary or self-tracking; perception that medical providers don’t have time or don’t concern themselves with understanding; and “partitioning” of patient care across providers so that each only understands “a piece” of the patient; Interestingly, 28% stated that there were aspects of their conditions and treatments that they elected not to share with their health care practitioners at all. This purposeful withholding of information by patients was often done out of embarrassment, for financial reasons, or to influence access to continued treatment.

	Forty-five percent of survey participants had participated in a clinical trial in the past. Within this group, the most common reason for participation was the opportunity to contribute to science, followed by the opportunity to learn more about their conditions, and then by financial considerations (respondents could select as many of the offered reasons as they felt relevant, as well as offer “other” responses; Figure 3). Sixty-five percent reported feeling more educated and involved in their overall health as a result of their participation.


	Figure 3. Reasons for clinical trial participation, among those with participation history

	The type and impact of medical condition (categories as depicted in Figure 1) did not have a significant impact on the top few reasons for clinical trial participation. As for the less frequently endorsed reasons, patients with chronic and degenerative conditions were more likely than those with episodic conditions to be motivated by additional contact with and access to better medical professionals (additional contact: 17%-18% vs. 0%; access to better professionals: 13%-16% vs 4%), and generally endorsed more reasons for trial participation overall.  Patients with mild disease impacts were more likely to be influenced by access to free healthcare than were those with more serious conditions (28% vs. 14%-18%), and those with no disease impact were more likely to be influenced by payment (50% vs. 33%-39%).

	Demographic characteristics also had some influence on the reasons for clinical trial participation. For older patients (76+), the additional expected contact with doctors and nurses was a key reason for participation (36% vs. 12%-17% for other age groups), whereas for younger patients, access to better doctors and hospitals was more likely to be key (17%-22% for ages 31-60 vs. 7%-14% for ages 61+). Females on average endorsed more reasons for participating in clinical trials than did males, though the overall pattern of top reasons was the same.

	Sixty-seven percent of survey participants would recommend clinical trial participation to a loved one or friend; 29% were unsure; and 4% would not. Among those who would recommend participation, responses carried themes of consideration of risks vs. benefits, of the institutions at which the studies are being performed, and of a wish to “do good” for society. Among those who would not, responses generally reflected a distrust of the clinical research industry, or concerns about the possibility of receiving ineffective treatment or placebo.

	Confidence in the Clinical Trial Process

	With respect to confidence in the information provided to prospective clinical trial participants, most of those surveyed had at least a fair level of confidence that trial participants are fully and honestly informed (Figure 4). Participants were most likely to be confident in information provided about the right to withdraw, procedures, and possible benefits, and were most skeptical about information relating to treatment alternatives, risks and side effects, and doctors’ motivations.

	With respect to confidence in the quality of the clinical trial process overall, most participants again had at least a fair level of confidence in the compliance, caring, and ethics of the site study teams and patients involved in clinical trials; however, many were skeptical about the honesty of pharmaceutical companies and about the extent to which they cared about study patients (Figure 4).


	Figure 4. Patient confidence in clinical trial information and process

	In both cases, the distributions reflected considerable response heterogeneity, particularly for areas that were rated on average most poorly. Interestingly, in multivariate models, the most frequent drivers of differences across patients with respect to confidence in the information provided appeared to be a history of clinical trial participation (positive impact on confidence), disease impact (highest confidence in those with mild disease), and marital status (divorced people demonstrate less confidence); all other disease and demographic characteristics had relatively little impact on the results once the effects of these three were accounted for. The strongest drivers of differences across patients with respect to confidence in clinical trial compliance and ethics appeared to be a history of clinical trial participation (positive impact on confidence), educational level (highest confidence in those with least education), and marital status (divorce associated with less confidence); all other disease and demographic characteristics were again of relatively low impact.

	Common themes in patient comments to this section included: the positive impacts of including patients in the design of both protocols and operational approaches to study execution; demonstrating respect and caring of patients in all aspects of execution including compensation; and exercising transparency at every level. Business concerns both positively and negatively impacted confidence levels: some patients were skeptical of the ethics of pharmaceutical companies given the large amounts of money involved in researching and selling therapeutics, while others saw the high stakes as providing assurance that maximum rigor would be applied. Regulatory requirements and a belief in human nature and professionalism further bolstered confidence, while media stories regarding litigation and drug recalls eroded it.

	Overall, the results from this study lead to the clear conclusion that as an industry, we can do more, today, to impact both patients’ perceptions of clinical trial participation, and the relevance of our trial designs to patients’ personal experiences of their diseases. From basic training in mindsets for key stakeholders to incorporating the patient voice in the protocol design process, there is work to be done to move the needle on patient engagement.  In fact, although at present it is still acceptable from a regulatory perspective to define the research questions to be addressed by protocols in traditional ways, the ability to obtain approval for and competitively market medical products will likely soon depend on incorporation of the patient perspective4. Moreover, in developing research questions, careful forethought must be given to whether each planned study offers opportunities for us to more fully understand, directly from our study patients, their experience of disease, and thus improve the quality of our future research.

	1. Lowe, M., Blaser, D.A., Cone, L., Arcona, S., Ko, J., Sasane, R., and Wicks, P. 2016. Increasing Patient Involvement in Drug Development.  Value Health 19(6): 869-878.

	2. Hoo, A., Anderson, J., Boutin, M. et al. Partnering with patients in the development and lifestyle of medicines: a call for action. 2015. Ther Innov Regul Sci. 49: 929–939.

	3. Shen, J., Buechler, A., Byrne, J., Hecht, J., James, J., and Pancratz, B. 2015. Clinical Research Participation as a Care Option: Driving patient experience and satisfaction. PMG Research White paper, https://www.pmg-research.com/Portals/0/Clinical%20Research%20as%20a%20Care%20Option_PMG%20Research%20White%20Paper%20Web2.pdf

	4. Parmar, A. 2016. How FDA is driving interest in patient engagement. Med City News, Sept 8, 2016.

	Denise Calaprice, PhD, is a Senior Consultant, with The Avoca Group; Jennifer Byrne is Chief Executive Officer of PMG Research, and Jeremy Gilbert isVice President, Product and Strategy, PatientsLikeMe.

Articles

A survey from the Avoca Group examines issues related to quality of clinical trials and trial participation from the patients' view. 

Bridging the Gap for Better Patient Engagement

Finding ways to effectively and efficiently manage quality is one of the central issues faced by clinical development teams today. A great deal of effort is devoted to associated processes, systems, and metric collection and analysis. However, the industry still struggles when it comes to measuring quality, making it difficult not only to ascertain whether adequate quality is being achieved, but also to verify that processes designed to promote quality are doing so. This article presents a case study in which Pfizer and The Avoca Group collaborated on an in-depth review of Pfizer’s quality metrics and related procedures, with the goal of implementing a framework for the oversight and analysis of clinical trial quality.

	At the outset of the project, Pfizer’s strengths in this area included: a comprehensive set of existing metrics; an infrastructure for metric aggregation; defined responsibilities for process-oriented metrics; strong senior management support for the idea of a more efficient approach; and a high level of motivation throughout the organization. Issues to be dealt with as part of the consulting project included the following:

		The existing catalog of “quality metrics” focused on process/“compliance” metrics (“was a task performed?”), with relatively little representation of “outcome” metrics (“did we achieve adequate quality?”)

		There was no clear definition of “quality metrics” that differentiated these from “operational metrics.”  

		The metrics were not organized into a conceptual framework that provided an understanding of the purpose of each metric, the expected relationships between metrics, and the level(s) at which each metric should be reviewed and actioned, nor did the process for introduction of a new metric involve a systematic evaluation of any of these factors. 

		Numerous, and often unsuitable, metrics were being reviewed by too many parties, resulting in a burdensome and inefficient review process. For example, since most metrics were presented only at the study level, reviewers beyond the study management level (e.g., program and resource management) found it challenging to access metric data in a manner that directly spoke to their accountabilities. 

		Methods for continuous data analysis that would identify meaningful trends in metrics, as well as relationships between metrics, were underutilized. For example, there was no systematic approach to examining either the fit-for-purpose of the metrics themselves (do they predict what they should?) or the rationale for action thresholds.

		Given Pfizer’s large quantity of metric data, the underdeveloped analytic strategy represented substantial untapped potential for predictive modeling and ultimately, for process/resource optimization strategies.

	To address these issues, the Avoca-Pfizer team developed an overarching strategy for Pfizer’s quality metrics, the principle components of which are discussed in this article. An implementation plan and technology assessment related to that strategy were also developed but are not discussed here. 

Framework for organization of quality metrics 

	To meet Pfizer’s needs, the Avoca Group sought to develop a framework for the organization of Pfizer’s quality metrics based on the fundamentals of the definition of quality. This framework was intended to define a core set of quality outcomes that required management, and then to classify Pfizer’s metrics such that each bore a defined relationship to one or more of these outcomes. Classification of metrics into this framework was intended to:

		Ensure that Pfizer’s suite of quality metrics contained the most important things that needed to be measured

		Expose excess or poorly designed metrics (i.e., those that weren’t truly quality metrics but rather operational metrics or that weren’t designed in a fit-for-purpose manner)

		Promote the development of more targeted and rational review procedures, with clear drill-up (to “upstream” metrics) or drill-down (to “downstream” metrics) cascades for analysis of status/issues and for proactive (e.g., risk) management.

	An added benefit was that the framework would also provide a structure for organizing other (non-metric) quality-related findings (e.g., textual information about SOP deviations, audit findings, etc.), thus allowing various sources of information to be logically assembled so as to promote comprehensive evaluation of quality management findings.

	The first task in developing this framework was to define quality metrics in a way that distinguished these from other types of metrics (e.g., operational), and thus set bounds around the framework and the catalog. Within the industry, there exists no standard in this respect; quality metrics may be variously defined as those that measure: performance to a quality standard (outcomes); activities that influence or predict quality (generally operational activities); quality assurance operational activities; and/or compliance with quality-related regulations. To ensure a clear distinction between quality and operational metrics, Pfizer elected to define the term quality metrics relatively restrictively, to include only those that: (1) measured quality outcomes, or (2) had been shown statistically to influence quality outcomes. 

	Figure 1. Top-Level Quality Outcome Categories

	To develop the framework, The Avoca Group began with the CTTI definition of quality; “The ability to effectively and efficiently answer the intended question about the benefits and risks of a medical product or procedure while assuring patient safety and protection of human subjects” (definition from October 2008 presentation on CTTI by Dr. Rachel Behrman, CTTI co-chair and then associate commissioner for clinical programs, FDA). From this definition, seven fundamental categories of quality outcomes were derived, as depicted in Figure 1(above). The top four categories in this figure represent the key components of any quality scientific experiment, and the two just below these represent ethical requirements associated with performing experiments on human subjects. Finally, when experimental results will be submitted in support of marketing applications, regulatory authorities must be satisfied that the activities leading to quality in these six outcome areas have been effectively managed, and thus require that certain types of documentation be maintained and/or submitted. Thus, the final category reflects the need for compliance with regulatory requirements in this respect.

	Table 1. Outcome Components for Each Major Quality Outcome Category

	Each of these major categories was then broken down into a set of specific, actionable outcome components, as described in Table 1(above). The seven major outcome categories and their associated components thus defined the complete top level of the framework, in the sense that all of Pfizer’s quality metrics must be in some way related to the achievement adequate standards for one or more of these outcomes.

	With respect to their purposes in this area, quality metrics were then defined as belonging to three different types: 

 directly answer the question of “Did we achieve the level of quality required by our standards?” 

 answer questions of “How are we progressing in our attempt to meet our quality standards?” Often, predictor metrics will be “fractional values” of an outcome metric (e.g., percent of subjects with protocol violations in the study “so far”). Otherwise, to qualify as a predictor metric, a statistical relationship between a predictor metric measured before or during a study and a quality outcome at the end of the study must be established. 

 measure conditions and activities that are logically expected to contribute to quality outcomes, but that do not have a strong enough statistical relationship to qualify as predictors. These metrics are most commonly process-related, addressing questions of, “Are we doing what we believe we need to do to achieve adequate quality?” Note that contributor metrics are not considered quality metrics according to Pfizer’s definition, but rather operational metrics with defined links to quality metrics. We, nevertheless, mention them here, because contributor metrics will often be reviewed when individuals involved in quality management are attempting to identify the root causes of quality issues, or when they are managing quality risk. When predictor or outcome metrics are not meeting expectations, such individuals will drill down to the logically related set of contributor metrics in attempt to identify specific inadequately performing process and/or resource areas that may be contributing to failure at the outcome/predictor level.

	Therefore, for each of the major outcome areas there are three discrete levels of metrics, allowing each metric to be assigned to a cell in a network such as that depicted in Table 2(seen below).

	Table 2. Structure of Quality Metric Taxonomy

	To maintain organizational discipline in adhering to this framework, it was recommended that a metric lifecycle management function be established to oversee organization of Pfizer’s quality metrics into the framework, as well as to oversee the performance of each metric. Specifically, this function would have the following remit:

		To own the quality metric framework, identifying and approving any proposed changes to the framework itself

		To approve any proposed new metrics, and to classify these metrics within the taxonomy

		To approve associated thresholds (e.g., stoplight), review plans, and metric validation plans, in consultation with relevant experts

		To assess the need for change in or retirement of any metrics

		To serve as a central body for the coordination of metric-related activities across different parts of the organization, for example, those involved in defining and collecting operational metrics.

Review and analysis of quality metric data

	As is the case with any information, the value of quality metric data, no matter how well organized, is limited by the effectiveness and efficiency with which it’s used. Thus, one of Pfizer’s objectives for this project was to improve the effectiveness and efficiency of its metric review process, by focusing each stakeholder on routine review of only a concise, targeted set of quality metrics that would answer the specific quality-related questions pertinent to that person’s accountability. A rationally pre-designed expanded review of additional metrics was then recommended to investigate root causes and/or scopes of impact if emerging issues were identified from the routine review, and/or based on known, a priori risks for a particular study. 

	For the purpose of defining the quality-related questions to be answered and, hence, both the routine review metrics and the expanded review metrics, Pfizer’s stakeholders were divided into four basic categories, as depicted in Table 3. This table describes how each of these role categories are defined, as well as the questions that individuals in each role are responsible for answering via quality metrics. Some individuals may have multiple responsibilities and may, therefore, need to consider the questions associated with more than one role, in their metric reviews.

	Table 3. Categories of Stakeholders, for the Purpose of Metric Review

	As can be seen from Table 3, a study director’s review of quality metric data must focus on ensuring, during its course, that his/her study will meet the expectations set by quality standards. Thus, the study-level reviewer will focus his/her routine metric review on predictor metrics for quality outcomes, potentially along with a set of rationally selected contributor metrics that may be dictated by the risk profile for the study (i.e., protocol complexity, region, outsource partner, etc.) If quality problems emerge, a study-level reviewer will then “drill down” to the lower level (i.e., contributor) metrics that are associated (in the framework) with the problematic predictors, as well as examining cuts of the metrics by resource, etc., in order to define the scope and isolate the root causes of the issues.

	In contrast, higher-level business unit and resource leads will focus on understanding the performance, from a quality perspective, of the domains for which they have accountability. Their routine reviews will thus focus primarily on outcome and predictor metrics for their domains (e.g., % of studies in their domains achieving quality standards), again with access to lower-level (contributor) metrics and to “cuts” of the metrics, in order to evaluate the scopes and root causes of any quality problems noted. Resource-level reviewers will also be interested in 

 (process-level/operational) metrics, in order to evaluate compliance of each resource with quality-promoting process commitments.

	Finally, process leads will review quality metrics with an eye to developing quality-promoting processes that can be complied with, and that when complied with, will reliably lead to the achievement of quality outcome standards. Thus, their reviews will focus primarily on understanding the relationships between contributor (process-level) metrics and associated outcome metrics, as well as on assessment of process compliance.

	While routine and expanded reviews by stakeholders will serve to identify and direct action on immediate issues, a higher level of analysis of quality metric data may be applied in order to achieve broader and more ambitious corporate goals, particularly when significant quantities of metric data are available. For Pfizer, one of the team’s objectives for this project was to define additional ways that Pfizer could enhance its use of metric data to these ends. Specific analytical techniques were recommended to serve four major purposes:

		Multivariate analyses to identify complex patterns across resource groups and/or business units (i.e., beyond what could be identified through simple cuts of the data), to support root cause analysis of ongoing problems

		Regression analyses to assess the efficacy of specific remediating actions in improving quality, and reciprocally, to investigate potential negative collateral effects of operational or organizational changes. For example, such analyses could be used to look broadly across studies/circumstances to determine whether a given type of remediating action is generally effective in improving quality, and even to get a sense for how long improvement generally takes to be realized.

		Regression and other relational analyses to verify that each metric achieves its intended purpose (i.e., that a predictor metric actually has downstream predictive power), and to establish rational thresholds for action (i.e., red/yellow/green lights), by understanding the nature and shape of the relationships between predictor and outcome metrics

		Multivariate analyses to characterize the relationships between baseline characteristics of a study (i.e., protocol complexity, region, outsourcing model, etc.), predictor metrics, other (e.g., operational, cost) metrics, and quality outcomes, in order to permit predictive modeling and optimization of oversight activities, resource allocation, processes, etc., for each circumstance. 

	Implementation of the strategy began in Q1, 2015. The following work streams were established:

		Taxonomy – Aligned all metrics and non-metric quality data to the quality taxonomy and proposed their classification as outcomes, predictors and contributors.

		Review – Developed stakeholder profiles regarding what quality information had to be reviewed, and established the drill downs and the cadence (review process); guided the development of reports and reporting tools.

		Analysis – Created the quality analysis plan, established thresholds for quality metrics, created standard analytic reports, developed predictive analytics and risk models, and performed statistical analyses on contributor/predictor/outcome relationships.

		Communications – Identified stakeholders for communication and formulated key messages to disseminate; controlled change management.

		Technology – Identified and implemented appropriate solutions to ensure consistent high quality data and reporting.

		Governance – Chartered to manage the metric lifecycle for the quality metrics as well as to independently ensure that each metric as designed achieves its intended purpose.

	Classification of existing metrics into the taxonomy allowed for identification of areas that were overserved or underserved with metrics, creating immediate opportunities to recommend adjustments to the catalogue. The taxonomy also created clarity in the intended relationships between contributor/predictor/outcome metrics, and Pfizer’s collection of clinical trial data offered an adequate sample to statistically test widely held beliefs about these relationships. Through this process, a considerable number of existing metrics were identified that did not contribute to quality outcomes. This will lead to the ability to retire or redesign existing metrics that do not actually bear strong relationships to quality outcomes.

	Initial analytic models based upon the taxonomy confirmed that many of the contributor metrics reflected good performance, which masked the more important performance information found in the predictor and outcome metrics. Since prior to the taxonomy all metrics were considered equivalent in importance, the more numerous contributor metrics influenced the overall enterprise performance picture positively, even though the positive performance on contributor-level metrics did not always lead to positive quality outcomes. This was a key finding of the exercise.

	The expected timeline for implementation of the taxonomy is six months. This includes an integrated technology platform, allowing Pfizer to better comprehend its clinical trial quality.

Denise Calaprice-Whitty is Senior Consultant, The Avoca Group; Jonathan Rowe is Head of Clinical Quality Performance Management, Pfizer

A case study by Pfizer & The Avoca Group on the development of an overarching strategy for Pfizer’s clinical trial quality metrics.

Quality Metrics for Clinical Trials

Each year The Avoca Group surveys industry executives and managers to understand trends in the outsourcing of clinical research. Over the years leading up to 2012, a trend was identified whereby sponsors' outsourced clinical development work was being increasingly consolidated within strategic partnerships, but there had been no industry-wide study of the outcomes of these partnerships and of the approaches that contributed most to their successful implementation. Therefore, for Avoca's "2012 Industry Survey," sponsors and service providers were asked to share their experiences regarding: 					

Prevalence, characteristics, and maturity of existing strategic partnerships

Satisfaction with outcomes, both overall and with respect to specific objectives

Tools and practices used in implementing and managing strategic partnerships

In the survey, a strategic partnership was defined as "a long-term contractual commitment between two organizations that seeks to achieve specific business objectives by maximizing the effectiveness of each participant's resources. Strategic partnerships are generally characterized by process alignment, and by risk-sharing or other provisions serving to align business objectives." 

Parallel web-based surveys were created for each of sponsor and clinical service provider respondents. For each survey, respondents were asked to select the one strategic partnership with which they had the most direct experience (if any), and to report about that relationship specifically. Responses were collected over a two-month period in late 2011 and early 2012. A total of 244 usable surveys were completed: 147 sponsor surveys from 89 companies, and 97 service provider surveys from 84 companies.  

Sponsor perspective on the success of strategic partnerships.

 Of the sponsor companies surveyed, 47% had had strategic partnerships with clinical service providers. The most common objectives for launching these partnerships included reduced costs, improved quality, improved efficiency in use of internal staff, access to operational expertise, and process improvement. Among sponsor respondents whose relationships spanned a period of greater than three years, there was considerable variability in whether expectations in each of these areas were met, and in the time it took. For example: 					

Reduced costs: slightly over half of respondents achieved cost savings in one year or less; almost a quarter reported never achieving cost savings.

Improved quality: 54% of respondents achieved improved quality within one year; 21% reported never achieving this.

Improved efficiency: less than half of the respondents felt that a reduction in effort for oversight was achieved within one year; 28% said that it was never achieved.

Process improvement: 36% saw process improvement within one year, and an additional 40% within one or two years. Almost a quarter reported that expectations in this area were never met.

Twenty-two percent of sponsor companies reported having discontinued strategic relationships. Although a few were discontinued due a reduction in the number of partners required to support the work, partnerships were most commonly discontinued because of poor quality of deliverables, followed by poor overall performance of the clinical service provider.  

Clinical service provider perspective on the success of strategic partnerships.

 Of the clinical service provider companies surveyed, 67% had had strategic partnerships with sponsors. The most common objectives for launching these strategic partnerships included increased business stability/continuity, improved efficiency in the use of staff, increased profit, access to more interesting projects, and meeting needs/desires of current customers. Among clinical service provider respondents whose relationships spanned a period of more than three years, there was considerable variability in whether expectations in each of these areas were met, and in the time it took. For example: 					

Increased business stability/continuity: 60% achieved this in one year or less; 32% said that it took between one and two years. 

Improved efficiency in the use of staff: 52% of respondents achieved improved efficiency within one year; 32% said that it took between one and two years.

Increased profit: 52% of respondents reported increased profitability within one year; 28% said it took between one and two years; 16% reported that it took longer than two years; and a small percentage indicated that this was never achieved.

Access to more interesting projects: slightly over half of the respondents reported access to more interesting projects within one year; 16% said that this was never achieved.

Sponsor and provider perspectives: implementation of strategic partnerships. 

Both sponsors and clinical service providers reported using a wide variety of tools for, and approaches to, implementing their strategic partnerships. The tools most commonly used included roles and responsibilities checklists, written project charters, joint steering and operating committees, formal communication plans regarding the new model, and training programs for the new model. All of these tools and approaches were felt by most respondents to be at least somewhat helpful; joint committees (operating, steering, and quality) and roles and responsibilities checklists were most likely to be considered "very helpful."  

When respondents were asked to describe what went well in implementing their relationships, they most commonly mentioned open and frequent communication and collaboration (including face-to-face), strong senior management support, and the establishment of clear expectations, often through written documents and training programs. Conversely, when asked to describe what they would have done differently, respondents most commonly mentioned failures in these areas.  

Based on both the data collected and consultants' experience, the survey found the strategic partnership model to be sound. However, the ability for a partnership to meet what are often high expectations for cost savings, process improvement, operational excellence, and the quality of deliverables is dependent on the foundation that is established.  

The specific approaches taken by sponsors in launching their strategic partnerships have tremendous impact. The specifics of successful approaches may vary based on the outsourcing model, the sponsors' objectives, and corporate cultures, and for this reason, there is no one-size-fits-all approach that ensures success. However, best practices that appear to be common among successful partnerships have emerged. These include:  

Clarity in the strategic and operational framework.

 This includes the objectives of, and vision for, the partnership, and the development of management practices, including a governance structure.  

 Typically includes detailed guidelines for operational teams that outline the specifics of how and by whom the relationship should be managed, as well as partnership-level quality and other agreements that provide clear expectations for operational activities and deliverables.  

 Should detail processes for discussing performance expectations with partners, for ensuring continuous bidirectional feedback, and for escalation of issues through the governance structure.  

 Robust systems for measuring both provider performance and relationship health are critical. Particularly important are metrics that reflect status in an ongoing manner. 

 Can be helpful in implementing new outsourcing models and partnerships; this includes engagement of staff as part of the process, and formal training plans.  

 Consistency in vision among senior management, and active support of the partnerships, are essential. 

Denise Calaprice-Whitty, PhD, Executive Director at The AVOCA Group

The Avoca Group's "2012 Industry Survey," sponsors and service providers were asked to share their experiences with strategic partnerships.

Denise Calaprice-Whitty

Articles

Bridging the Gap for Better Patient Engagement

Quality Metrics for Clinical Trials

Sponsors and Service Providers Share Their Experiences