Applying sequence clustering techniques to explore practice-based ambulatory care pathways in insurance claims data

Exploring heterogeneity in coxarthrosis medication use patterns before total hip replacement: a State Sequence Analysis

OBJECTIVE

Evidence of geographical variation in total hip replacement (THR) and deviations from treatment guidelines persists. In this exploratory study, we aim to gain an in-depth understanding of patients' healthcare trajectories by identifying and visualising medication use patterns in coxarthrosis patients before surgery. We examine their association with patient characteristics and THR, and compare them with recommendations on mild analgesics, opioid prescription and exhaustion of conservative therapy.

METHODS

In this exploratory study, we apply State Sequence Analysis (SSA) on German health insurance data (2012-2015). We analyse a cohort of coxarthrosis patients, half of whom underwent THR after a 1 year observation period and half of whom did not undergo surgery until at least 1 year after the observation period. Hierarchical states are defined based on prescriptions. We construct sequences, calculate sequence similarity using optimal matching and identify medication use patterns via clustering. Patterns are visualised, descriptive statistics are presented and logistic regression is employed to investigate the association of medication patterns with subsequent THR.

RESULTS

Seven distinct medication use patterns are identified, correlating strongly with patient characteristics and subsequent THR. Two patterns leading to THR demonstrate exhaustion of pharmacological therapy. Opioid use is concentrated in two small patterns with low odds for THR. The most frequent pattern lacks significant pharmacological therapy.

CONCLUSIONS

This SSA uncovers heterogeneity in medication use patterns before surgery in coxarthrosis patients. Cautious opioid handling and adherence to a stepped prescription approach are observed, but many patients display low medication therapy usage and lack evidence of exhausting conservative options before surgery.

Cardiovascular disease healthcare trajectories: descriptions, similarities, mortality rates of heart failure in France

AIMS

The primary objectives of this study were to analyse the nationwide healthcare trajectories of heart failure (HF) patients in France, 2 years after their first hospitalization, and to measure sequence similarities. Secondary objectives were to identify the association between trajectories and the risk of mortality.

METHODS AND RESULTS

A retrospective, observational study was conducted using data extracted from the Echantillon Généraliste des Bénéficiaires database, covering the period from 1 January 2008 to 31 December 2018. Follow-up concluded upon death or at the end of the study. We developed a methodology specific to healthcare data by extracting frequent healthcare trajectories and measuring their similarity for use in a survival machine learning analysis. In total, 11 488 HF patients were included and followed up for an average of 2.9 ± 1.3 years. The mean age of the patients was 78.0 ± 13.2 years. The first-year mortality rate was 31.7% and increased to 78.8% at 5 years. Fifty per cent of patients experienced re-hospitalization for reasons related to cardiovascular diseases. We identified 1707 hospitalization sequences, and 21 sequences were associated with survival, while 15 sequences were linked to mortality. In all our models, age and gender emerged as the most significant predictors of mortality (permutation feature importance: 0.099 ± 0.00078 and 0.0087 ± 0.00018, respectively; weights could be interpreted in relative terms). Specifically, the age at initial hospitalization for HF was positively associated with mortality. Gender (male: 49.5%) was associated with poorer prognoses. Healthcare trajectories, including non-surgical device treatments, valve replacements, and atrial fibrillation ablation, were associated with a better prognosis (permutation feature importance: 0.0047 ± 0.00011, 0.0014 ± 0.000073, and 0.00095 ± 0.000097, respectively), except in cases where these invasive treatments preceded or followed hospitalization for cardiac decompensation. The predominant negative prognosis sequences were mostly those that included HF-related hospitalizations before or after other-related hospitalizations (permutation feature importance: 0.0007 ± 0.000091 and 0.00011 ± 0.000045, respectively).

CONCLUSIONS

We highlight the value of healthcare trajectories on frequent hospitalization sequences, mortality, and prognosis and indicate the necessary prognostic value of HF re-hospitalization. Our work may be an essential tool for better identification of at-risk patients in order to increase and improve personalized care in the future.

[Five Key Questions for Health Services Research: are SHI Claims Data Suitable for Your Research Project?]

Health services research examines the structures and processes of health care under everyday conditions. Routine data of the statutory health insurance (SHI) - the so-called routine practice data - represent real health care and are therefore an important data source for health services research. This paper presents 5 key questions that researchers and data-holding institutions can use to assess the suitability of this data source for answering their health services research question. The aim of these guiding questions is to generate a common understanding between researchers and data-holding institutions of the research project, the research objective, and the feasibility of implementation in health services research. The five guiding questions cover the formulation of the research question, the planned method, the target population, the relevant study periods, and the required information from SHI data. These methodologically oriented guiding questions are supplemented by the question of how the results of the research project could improve care. Thus, for researchers, the five guiding questions provide an initial structuring for data requests; for data-holding institutions, they provide a framework for considering possible involvement in or support of a research idea in health services research.

Clustering care pathways of people with alcohol dependence using a data linkage of routine data in Bremen, Germany

BACKGROUND

Although many individuals with alcohol dependence (AD) are recognized in the German healthcare system, only a few utilize addiction-specific treatment services. Those who enter treatment are not well characterized regarding their prospective pathways through the highly fragmented German healthcare system. This paper aims to (1) identify typical care pathways of patients with AD and their adherence to treatment guidelines and (2) explore the characteristics of these patients using routine data from different healthcare sectors.

METHODS

We linked routinely collected register data of individuals with a documented alcohol-related diagnosis in the federal state of Bremen, Germany, in 2016/2017 and their addiction-specific health care: two statutory health insurance funds (outpatient pharmacotherapy for relapse prevention and inpatient episodes due to AD with and without qualified withdrawal treatment (QWT)), the German Pension Insurance (rehabilitation treatment) and a group of communal hospitals (outpatient addiction care). Individual care pathways of five different daily states of utilized addiction-specific treatment following an index inpatient admission due to AD were analyzed using state sequence analysis and cluster analysis. The follow-up time was 307 days (10 months). Individuals of the clustered pathways were compared concerning current treatment recommendations (1: QWT followed by postacute treatment; 2: time between QWT and rehabilitation). Patients' characteristics not considered during the cluster analysis (sex, age, nationality, comorbidity, and outpatient addiction care) were then compared using a multinomial logistic regression.

RESULTS

The analysis of 518 individual sequences resulted in the identification of four pathway clusters differing in their utilization of acute and postacute treatment. Most did not utilize subsequent addiction-specific treatment after their index inpatient episode (n = 276) or had several inpatient episodes or QWT without postacute treatment (n = 205). Two small clusters contained pathways either starting rehabilitation (n = 26) or pharmacotherapy after the index episode (n = 11). Overall, only 9.3% utilized postacute treatment as recommended.

CONCLUSIONS

A concern besides the generally low utilization of addiction-specific treatment is the implementation of postacute treatments for individuals after QWT.

Applying sequence analysis to uncover 'real-world' clinical pathways from routinely collected data: a systematic review

OBJECTIVES

This systematic review aims to elucidate the methodological practices and reporting standards associated with sequence analysis (SA) for the identification of clinical pathways in real-world scenarios, using routinely collected data.

STUDY DESIGN AND SETTING

We conducted a methodological systematic review, searching five medical and health databases: MEDLINE, PsycINFO, CINAHL, EMBASE and Web of Science. The search encompassed articles from the inception of these databases up to February 28, 2023. The search strategy comprised two distinctive sets of search terms, specifically focused on sequence analysis and clinical pathways.

RESULTS

19 studies met the eligibility criteria for this systematic review. Nearly 60% of the included studies were published in or after 2021, with a significant proportion originating from Canada (n = 7) and France (n = 5). 90% of the studies adhered to the fundamental SA steps. The optimal matching (OM) method emerged as the most frequently employed dissimilarity measure (63%), while agglomerative hierarchical clustering using Ward's linkage was the preferred clustering algorithm (53%). However, it is imperative to underline that a majority of the studies inadequately reported key methodological decisions pertaining to SA.

CONCLUSION

This review underscores the necessity for enhanced transparency in reporting both data management procedures and key methodological choices within SA processes. The development of reporting guidelines and a robust appraisal tool tailored to assess the quality of SA would be invaluable for researchers in this field.

Care pathways of sepsis survivors: sequelae, mortality and use of healthcare services in France, 2015-2018

BACKGROUND

Individuals who survive sepsis are at high risk of chronic sequelae, resulting in significant health-economic costs. Several studies have focused on aspects of healthcare pathways of sepsis survivors but comprehensive, longitudinal overview of their pathways of care are scarce. The aim of this retrospective, longitudinal cohort study is to identify sepsis survivor profiles based on their healthcare pathways and describe their healthcare consumption and costs over the 3 years following their index hospitalization.

METHODS

The data were extracted from the French National Hospital Discharge Database. The study population included all patients above 15 years old, with bacterial sepsis, who survived an incident hospitalization in an acute care facility in 2015. To identify survivor profiles, state sequence and clustering analyses were conducted over the year following the index hospitalization. For each profile, patient characteristics and their index hospital stay and sequelae were described, as well as use of care and its associated monetary costs, both pre- and post-sepsis.

RESULTS

New medical (79.2%), psychological (26.9%) and cognitive (18.5%) impairments were identified post-sepsis, and 65.3% of survivors were rehospitalized in acute care. Cumulative mortality reached 36.6% by 3 years post-sepsis. The total medical cost increased by 856 million € in the year post-sepsis. Five patient clusters were identified: home (65.6% of patients), early death (12.9%), late death (6.8%), short-term rehabilitation (11.3%) and long-term rehabilitation (3.3%). Survivors with early and late death clusters had high rates of cancer and primary bacteremia and experienced more hospital-at-home care post-sepsis. Survivors in short- or long-term rehabilitation clusters were older, with higher percentage of septic shock than those coming back home, and had high rates of multiple site infections and higher rates of new psychological and cognitive impairment.

CONCLUSIONS

Over three years post-sepsis, different profiles of sepsis survivors were identified with different mortality rates, sequels and healthcare services usage and cost. This study confirmed the importance of sepsis burden and suggests that strategies of post-discharge care, in accordance with patient profile, should be further tested in order to reduce sepsis burden.

Analytical methods for identifying sequences of utilization in health data: a scoping review

BACKGROUND

Healthcare, as with other sectors, has undergone progressive digitalization, generating an ever-increasing wealth of data that enables research and the analysis of patient movement. This can help to evaluate treatment processes and outcomes, and in turn improve the quality of care. This scoping review provides an overview of the algorithms and methods that have been used to identify care pathways from healthcare utilization data.

METHOD

This review was conducted according to the methodology of the Joanna Briggs Institute and the Preferred Reporting Items for Systematic Reviews Extension for Scoping Reviews (PRISMA-ScR) Checklist. The PubMed, Web of Science, Scopus, and EconLit databases were searched and studies published in English between 2000 and 2021 considered. The search strategy used keywords divided into three categories: the method of data analysis, the requirement profile for the data, and the intended presentation of results. Criteria for inclusion were that health data were analyzed, the methodology used was described and that the chronology of care events was considered. In a two-stage review process, records were reviewed by two researchers independently for inclusion. Results were synthesized narratively.

RESULTS

The literature search yielded 2,865 entries; 51 studies met the inclusion criteria. Health data from different countries ([Formula: see text]) and of different types of disease ([Formula: see text]) were analyzed with respect to different care events. Applied methods can be divided into those identifying subsequences of care and those describing full care trajectories. Variants of pattern mining or Markov models were mostly used to extract subsequences, with clustering often applied to find care trajectories. Statistical algorithms such as rule mining, probability-based machine learning algorithms or a combination of methods were also applied. Clustering methods were sometimes used for data preparation or result compression. Further characteristics of the included studies are presented.

CONCLUSION

Various data mining methods are already being applied to gain insight from health data. The great heterogeneity of the methods used shows the need for a scoping review. We performed a narrative review and found that clustering methods currently dominate the literature for identifying complete care trajectories, while variants of pattern mining dominate for identifying subsequences of limited length.

Understanding categories of postpartum care use among privately insured patients in the United States: a cluster-analytic approach

The postpartum period is critical for the health and well-being of birthing people, yet little is known about the range of health care services and supports needed during this time. Maternity care patients are often targeted for clinical interventions based on "low risk" or "high risk" designations, but dichotomized measures can be imprecise and may not reflect meaningful groups for understanding needed postpartum care. Using claims data from privately insured patients with childbirths between 2016 and 2018, this study identifies categories and predictors of postpartum care utilization, including the use of maternal care and other, nonmaternal, care (eg, respiratory, digestive). We then compare identified utilization-based categories with typical high- and low-risk designations. Among 269 992 patients, 5 categories were identified: (1) low use (55% of births); (2) moderate maternal care use, low other care use (25%); (3) moderate maternal, high other (8%); (4) high maternal, moderate other (7%); and (5) high maternal, high other (5%). Utilization-based categories were better at differentiating postpartum care use and were more consistent across patient profiles, compared with high- and low-risk dichotomies. Identifying categories of postpartum care need beyond a simple risk dichotomy is warranted and can assist in maternal health services research, policymaking, and clinical practice.

Capturing the variety of clinical pathways in patients with schizophrenic disorders through state sequences analysis

BACKGROUND

Care pathways are increasingly being used to enhance the quality of care and optimize the use of resources for health care. Nevertheless, recommendations regarding the sequence of care are mostly based on consensus-based decisions as there is a lack of evidence on effective treatment sequences. In a real-world setting, classical statistical tools were insufficient to consider a phenomenon with such high variability adequately and have to be integrated with novel data mining techniques suitable for identifying patterns in complex data structures. Data-driven techniques can potentially support empirically identifying effective care sequences by extracting them from data collected routinely. The purpose of this study is to perform a state sequence analysis (SSA) to identify different patterns of treatment and to asses whether sequence analysis may be a useful tool for profiling patients according to the treatment pattern.

METHODS

The clinical application that motivated the study of this method concerns the mental health field. In fact, the care pathways of patients affected by severe mental disorders often do not correspond to the standards required by the guidelines in this field. In particular, we analyzed patients with schizophrenic disorders (i.e., schizophrenia, schizotypal or delusional disorders) using administrative data from 2015 to 2018 from Lombardy Region. This methodology considers the patient's therapeutic path as a conceptual unit, composed of a succession of different states, and we show how SSA can be used to describe longitudinal patient status.

RESULTS

We define the states to be the weekly coverage of different treatments (psychiatric visits, psychosocial interventions, and anti-psychotic drugs), and we use the longest common subsequences (dis)similarity measure to compare and cluster the sequences. We obtained three different clusters with very different patterns of treatments.

CONCLUSIONS

This kind of information, such as common patterns of care that allowed us to risk profile patients, can provide health policymakers an opportunity to plan optimum and individualized patient care by allocating appropriate resources, analyzing trends in the health status of a population, and finding the risk factors that can be leveraged to prevent the decline of mental health status at the population level.

Clustering of prostate cancer healthcare pathways in the French National Healthcare database

BACKGROUND

Healthcare pathways of patients with prostate cancer are heterogeneous and complex to apprehend using traditional descriptive statistics. Clustering and visualization methods can enhance their characterization.

METHODS

Patients with prostate cancer in 2014 were identified in the French National Healthcare database (Système National des Données de Santé-SNDS) and their data were extracted with up to 5 years of history and 4 years of follow-up. Fifty-one-specific encounters constitutive of prostate cancer management were synthesized into four macro-variables using a clustering approach. Their values over patient follow-ups constituted healthcare pathways. Optimal matching was applied to calculate distances between pathways. Partitioning around medoids was then used to define consistent groups across four exclusive cohorts of incident prostate cancer patients: Hormone-sensitive (HSPC), metastatic hormone-sensitive (mHSPC), castration-resistant (CRPC), and metastatic castration-resistant (mCRPC). Index plots were used to represent pathways clusters.

RESULTS

The repartition of macro-variables values-surveillance, local treatment, androgenic deprivation, and advanced treatment-appeared to be consistent with prostate cancer status. Two to five clusters of healthcare pathways were observed in each of the different cohorts, corresponding for most of them to relevant clinical patterns, although some heterogeneity remained. For instance, clustering allowed to distinguish patients undergoing active surveillance, or treated according to cancer progression risk in HSPC, and patients receiving treatment for potentially curative or palliative purposes in mHSPC and mCRPC.

CONCLUSION

Visualization methods combined with a clustering approach enabled the identification of clinically relevant patterns of prostate cancer management. Characterization of these care pathways is an essential element for the comprehension and the robust assessment of healthcare technology effectiveness.

Health service research definition builder: An R Shiny application for exploring diagnosis codes associated with services reported in routinely collected health data

Many administrative health data-based studies define patient cohorts using procedure and diagnosis codes. The impact these criteria have on a study's final cohort is not always transparent to co-investigators or other audiences if access to the research data is restricted. We developed a SAS and R Shiny interactive research support tool which generates and displays the diagnosis code summaries associated with a selected medical service or procedure. This allows non-analyst users to interrogate claims data and groupings of reported diagnosis codes. The SAS program uses a tree classifier to find associated diagnosis codes with the service claims compared against a matched, random sample of claims without the service. Claims are grouped based on the overlap of these associated diagnosis codes. The Health Services Research (HSR) Definition Builder Shiny application uses this input to create interactive table and graphics, which updates estimated claim counts of the selected service as users select inclusion and exclusion criteria. This tool can help researchers develop preliminary and shareable definitions for cohorts for administrative health data research. It allows an additional validation step of examining frequency of all diagnosis codes associated with a service, reducing the risk of incorrect included or omitted codes from the final definition. In our results, we explore use of the application on three example services in 2016 US Medicare claims for patients aged over 65: knee arthroscopy, spinal fusion procedures and urinalysis. Readers can access the application at https://kelsey209.shinyapps.io/hsrdefbuilder/ and the code at https://github.com/kelsey209/hsrdefbuilder.

Identifying and Investigating Ambulatory Care Sequences Before Invasive Coronary Angiography

BACKGROUND

The concept of care pathways is widely used to provide efficient, timely, and evidence-based medical care. Recently, the investigation of actual empirical patient pathways has gained attention. We demonstrate the usability of State Sequence Analysis (SSA), a data mining approach based on sequence clustering techniques, on comprehensive insurance claims data from Germany to identify empirical ambulatory care sequences. We investigate patients with coronary artery disease before invasive coronary angiography (CA) and compare identified patterns with guideline recommendations. This patient group is of particular interest due to high and regionally varying CA rates.

METHODS

Events relevant for the care of coronary artery disease patients, namely physician consultations and medication prescriptions, are identified based on medical guidelines and combined to define states. State sequences are determined for 1.5 years before CA. Sequence similarity is defined for clustering, using optimal matching with theory-informed substitution costs. We visualize clusters, present descriptive statistics, and apply logistic regression to investigate the association of cluster membership with subsequent undesired care events.

RESULTS

Five clusters are identified, the included patients differing with respect to morbidity, urbanity of residential area, and health care utilization. Clusters exhibit significant differences in the timing, structure, and extent of care before CA. When compared with guideline recommendations, 3 clusters show signs of care deficits.

CONCLUSIONS

Our analyses demonstrate the potential of SSA for exploratory health care research. We show how SSA can be used on insurance claims data to identify, visualize, and investigate care patterns and their deviations from guideline recommendations.

Referral trajectories in patients with vertigo, dizziness and balance disorders and their impact on health-related quality of life and functioning: results from the longitudinal multicenter study MobilE-TRA

Background

Due to reported barriers in the management of patients with vertigo, dizziness and balance problems (VDB), referral trajectories starting from primary care might be determined by other factors than medical necessity. The objective of this paper was to examine the impact of disease-related and other determinants on referral trajectories of older patients with VDB and to investigate, how these trajectories affect the patients’ functioning and health-related quality of life (HRQoL).

Methods

Data originate from the longitudinal multicenter study MobilE-TRA, conducted in two German federal states. Referrals to neurologists or ear-nose-throat (ENT) specialists were considered. Referral patterns were visualized using a state sequence analysis. Predictors of referral trajectories were examined using a multinomial logistic regression model. Linear mixed models were calculated to assess the impact of referral patterns on the patients’ HRQoL and functioning.

Results

We identified three patterns of referral trajectories: primary care physician (PCP) only, PCP and neurologist, and PCP and ENT. Chances of referral to a neurologist were higher for patients with a neurological comorbidity (OR = 3.22, 95%-CI [1.003; 10.327]) and lower for patients from Saxony (OR = 0.08, 95%-CI [0.013; 0.419]). Patients with a PCP and neurologist referral pattern had a lower HRQoL and lower functioning at baseline assessment. Patients with unspecific diagnoses also had lower functioning.

Conclusion

Referral trajectories were determined by present comorbidities and the regional healthcare characteristics. Referral trajectories affected patients’ HRQoL. Unspecific VDB diagnoses seem to increase the risk of ineffective management and consequently impaired functioning.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00415-022-11060-8.

Clinical and operational insights from data-driven care pathway mapping: a systematic review

Background

Accumulated electronic data from a wide variety of clinical settings has been processed using a range of informatics methods to determine the sequence of care activities experienced by patients. The “as is” or “de facto” care pathways derived can be analysed together with other data to yield clinical and operational information. It seems likely that the needs of both health systems and patients will lead to increasing application of such analyses. A comprehensive review of the literature is presented, with a focus on the study context, types of analysis undertaken, and the utility of the information gained.

Methods

A systematic review was conducted of literature abstracting sequential patient care activities (“de facto” care pathways) from care records. Broad coverage was achieved by initial screening of a Scopus search term, followed by screening of citations (forward snowball) and references (backwards snowball). Previous reviews of related topics were also considered. Studies were initially classified according to the perspective captured in the derived pathways. Concept matrices were then derived, classifying studies according to additional data used and subsequent analysis undertaken, with regard for the clinical domain examined and the knowledge gleaned.

Results

254 publications were identified. The majority (n = 217) of these studies derived care pathways from data of an administrative/clinical type. 80% (n = 173) applied further analytical techniques, while 60% (n = 131) combined care pathways with enhancing data to gain insight into care processes.

Discussion

Classification of the objectives, analyses and complementary data used in data-driven care pathway mapping illustrates areas of greater and lesser focus in the literature. The increasing tendency for these methods to find practical application in service redesign is explored across the variety of contexts and research questions identified. A limitation of our approach is that the topic is broad, limiting discussion of methodological issues.

Conclusion

This review indicates that methods utilising data-driven determination of de facto patient care pathways can provide empirical information relevant to healthcare planning, management, and practice. It is clear that despite the number of publications found the topic reviewed is still in its infancy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01756-2.

Machine Learning-Based Analysis of Treatment Sequences Typology in Advanced Non-Small-Cell Lung Cancer Long-Term Survivors Treated With Nivolumab

PURPOSE

Immune checkpoint inhibitors substantially changed advanced non-small-cell lung cancer (aNSCLC) management and can lead to long-term survival. The aims of this study were (1) to use a machine learning method to establish a typology of treatment sequences on patients with aNSCLC who were alive 2 years after initiating a treatment with anti-programmed death-ligand 1 monoclonal antibody nivolumab and (2) to describe the patients' characteristics according to the typology of treatment sequences.

MATERIALS AND METHODS

This retrospective observational study was based on data from the comprehensive French hospital discharge database for all patients with lung cancer with at least one line of platinum-based chemotherapy, starting nivolumab between January 1, 2015, and December 31, 2016, and alive 2 years after nivolumab treatment initiation. Patients were followed until December 31, 2018. A typology of most common treatment sequences was established using hierarchical clustering with time sequence analysis.

RESULTS

Two thousand two hundred twelve study patients were, on average, 63.0 years old, 69.9% of them were men, and 61.9% had a nonsquamous cell carcinoma. During the 2 years after nivolumab treatment initiation, clusters of patients with four basic types of treatment sequences were identified: (1) almost continuous nivolumab treatment (44% of patients); (2) nivolumab most of the time followed by a treatment-free interval or a chemotherapy (15% of patients); and a short or medium nivolumab treatment, followed by (3) a long systemic treatment-free interval (17% of patients) or (4) a long chemotherapy (23% of patients).

CONCLUSION

This machine learning approach enabled the identification of a typology of four representative treatment sequences observed in long-term survival. It was noted that most long-term survivors were treated with nivolumab for well over 1 year.

Modified Needleman-Wunsch algorithm for clinical pathway clustering

Clinical pathways are used to guide clinicians to provide a standardised delivery of care. Because of their standardisation, the aim of clinical pathways is to reduce variation in both care process and patient outcomes. When learning clinical pathways from data through data mining, it is common practice to represent each patient pathway as a string corresponding to their movements through activities. Clustering techniques are popular methods for pathway mining, and therefore this paper focuses on distance metrics applied to string data for k-medoids clustering. The two main aims are to firstly, develop a technique that seamlessly integrates expert information with data and secondly, to develop a string distance metric for the purpose of process data. The overall goal was to allow for more meaningful clustering results to be found by adding context into the string similarity calculation. Eight common distance metrics and their applicability are discussed. These distance metrics prove to give an arbitrary distance, without consideration for context, and each produce different results. As a result, this paper describes the development of a new distance metric, the modified Needleman-Wunsch algorithm, that allows for expert interaction with the calculation by assigning groupings and rankings to activities, which provide context to the strings. This algorithm has been developed in partnership with UK's National Health Service (NHS) with the focus on a lung cancer pathway, however the handling of the data and algorithm allows for application to any disease type. This method is contained within Sim.Pro.Flow, a publicly available decision support tool.