Implementation Science is a newly emerging field promoting the integration of research and evaluation findings into health care practice and policy. Implementation Science seeks to understand the behavior of health care providers, patients, and other stakeholders in order to identify and disseminate best practices for the sustainable implementation of evidence-based interventions.

Altarum engages in research and evaluation projects focused on analyzing health information technology (IT) implementation and utilization as well as quality improvement interventions in physician practices, using mixed-methods research and evaluation designs including surveys, interviews, and cognitive task analysis tools. By understanding the granular level of practices’ experience with health IT and quality interventions, our research identifies themes to shape best practices, identify program deficiencies, and prepare potential solutions.

Our approach to Implementation Science relies on qualitative research techniques which can provide rich and detailed descriptions of processes and experiences related to health IT use, health information exchange, and efforts to improve the quality of care. Using qualitative methods to gather and analyze data provides an examination of the human experience in ways that offer new insights into the ways teams work, leverage health IT systems, and coordinate activities. To this end, Altarum has cultivated extensive experience in qualitative research methods and analysis techniques.

Data Collection Methods and Instruments: Our staff is experienced in designing open-ended survey questions, semi-structured interview protocols, observation guides, and focus group protocols.

Semi-Structured Qualitative Interviewing: In-depth qualitative interviews are guided by semi-structured interview protocols that probe respondents on their experience related to a project’s research questions. Qualitative interviews typically cover a number of key topics and questions determined in advance, and encourage respondents to discuss their experiences in detailed narrative. We typically conduct semi-structured qualitative interviewing in teams of two, consisting of a lead interviewer and a note taker. The lead interviewer is responsible for guiding the discussion using the interview protocol and probing the respondent in key areas of interest. The note taker records key narrative, monitors the progress through the interview protocol, and records observations during the interview that cannot be captured through audio recordings. With the permission of the respondent, the interviews are recorded and transcribed to ensure detailed data is available for analysis. Interview teams debrief following each interview and type up their notes as soon as possible. Our staff is particularly adept at collecting data in busy clinical settings with limited disruption to patient care.

Cognitive Task Analysis: Altarum staff members are specifically trained in cognitive task analysis (CTA). CTA is a set of highly structured quantitative methods designed to elicit the often invisible or highly automatized thought processes of individual experts or high-functioning teams in real world environments. CTA is used across a wide range of disciplines as diverse as military command, neonatal intensive care nursing, and endoscopic surgery. CTA offers value in guiding implementations of new technologies and in measurement of system change including the implementation of health IT. Altarum staff, along with our academic research partners, are studying how CTA can offer insight into the implementation context to inform how best to customize health IT and adapt existing routines to best leverage technology.

Qualitative Analysis: Our team uses several different coding techniques and qualitative analysis methods. In-depth qualitative analysis can be quite time consuming but often results in identification of detailed themes and lessons learned. Our staff members conduct thematic analysis of coded data using standard text-based analysis software (i.e., NVivo). We analyze interview data and other qualitative data sets coding for broad themes as well as specific topics. This often involves several iterations of coding and assessment of inter-rater reliability.