Patient healthcare trajectory. An essential monitoring tool: a systematic review

  • Jessica Pinaire
  • Jérôme Azé
  • Sandra Bringay
  • Paul Landais



Patient healthcare trajectory is a recent emergent topic in the literature, encompassing broad concepts. However, the rationale for studying patients’ trajectories, and how this trajectory concept is defined remains a public health challenge. Our research was focused on patients’ trajectories based on disease management and care, while also considering medico-economic aspects of the associated management. We illustrated this concept with an example: a myocardial infarction (MI) occurring in a patient’s hospital trajectory of care. The patient follow-up was traced via the prospective payment system. We applied a semi-automatic text mining process to conduct a comprehensive review of patient healthcare trajectory studies. This review investigated how the concept of trajectory is defined, studied and what it achieves.


We performed a PubMed search to identify reports that had been published in peer-reviewed journals between January 1, 2000 and October 31, 2015. Fourteen search questions were formulated to guide our review. A semi-automatic text mining process based on a semantic approach was performed to conduct a comprehensive review of patient healthcare trajectory studies. Text mining techniques were used to explore the corpus in a semantic perspective in order to answer non-a priori questions. Complementary review methods on a selected subset were used to answer a priori questions.


Among the 33,514 publications initially selected for analysis, only 70 relevant articles were semi-automatically extracted and thoroughly analysed. Oncology is particularly prevalent due to its already well-established processes of care. For the trajectory thema, 80% of articles were distributed in 11 clusters. These clusters contain distinct semantic information, for example health outcomes (29%), care process (26%) and administrative and financial aspects (16%).


This literature review highlights the recent interest in the trajectory concept. The approach is also gradually being used to monitor trajectories of care for chronic diseases such as diabetes, organ failure or coronary artery and MI trajectory of care, to improve care and reduce costs. Patient trajectory is undoubtedly an essential approach to be further explored in order to improve healthcare monitoring.


Systematic reviews Text mining Healthcare trajectory PPS Semi-automated Word cloud 



Acute myocardial infarction


Diagnosis related group


Divisive hierarchical clustering


International classification of diseases


Interquartile interval


Latent Dirichlet allocation


Myocardial infarction


Programme de médicalisation du système d’information


Prospective payment system


  1. 1.
    Grant JB, Hayes RP, Pates RD, Elward KS, Ballard DJ. HCFA’s health care quality improvement program: the medical informatics challenge. J Am Med Inf Assoc. 1996;3:15–26.CrossRefGoogle Scholar
  2. 2.
    Holman CD, Bass AJ, Rouse IL, Hobbs MS. Population-based linkage of health records in Western Australia: development of a health services research linked database. Aust N Z J Public Health. 1999;23:453–9.PubMedCrossRefGoogle Scholar
  3. 3.
    Kendrick S, Clarke J. The Scottish record linkage system. Health Bull. 1993;51:72–9.Google Scholar
  4. 4.
    Saranummi N, Ensio A, Laine M, Nykänen P, Itkonen P. National health IT services in Finland. Methods Inf Med. 2007;46:463–9.PubMedGoogle Scholar
  5. 5.
    Le Bihan-Benjamin C, Landais P, Chatellier G. Linking hospital stays in the national PMSI MCO database improved between 2006 and 2009: analysis and consequences. J Écon Méd. 2012;30:17–30.Google Scholar
  6. 6.
    Le Manach Y, Collins G, Rodseth R, Le Bihan-Benjamin C, Biccard B, Riou B, et al. Preoperative score to predict postoperative mortality (POSPOM): derivation and validation. J Am Soc Anesthesiol. 2016;124:570–9.CrossRefGoogle Scholar
  7. 7.
    Moulis G, Lapeyre-Mestre M, Palmaro A, Pugnet G, Montastruc J-L, Sailler L. French health insurance databases: what interest for medical research? Rev Médecine Interne. 2015;36:411–7.CrossRefGoogle Scholar
  8. 8.
    Le Manach Y, Collins G, Bhandari M, Bessissow A, Boddaert J, Khiami F, et al. Outcomes after hip fracture surgery compared with elective total hip replacement. JAMA. 2015;314:1159–66.PubMedCrossRefGoogle Scholar
  9. 9.
    Colas S, Collin C, Piriou P, Zureik M. Association between total hip replacement characteristics and 3-year prosthetic survivorship: a population-based study. JAMA Surg. 2015;150:979–88.PubMedCrossRefGoogle Scholar
  10. 10.
    Van Hecke A, Heinen M, Fernandez-Ortega P, Graue M, Hendriks J, Høy B, et al. Access to effective healthcare: effective self-management support intervention for patients with a chronic condition and a low social economic status: a systematic review. BMC Nurs. 2015;14:1–2.CrossRefGoogle Scholar
  11. 11.
    Cohen AM, Hersh WR, Peterson K, Yen P-Y. Reducing workload in systematic review preparation using automated citation classification. J Am Med Inf Assoc. 2006;13:206–19.CrossRefGoogle Scholar
  12. 12.
    Ratinaud P, Déjean F. IRaMuTeQ : implémentation de la méthode ALCESTE d’analyse de texte dans un logiciel libre. MASHS2009, Toulouse; 2009.Google Scholar
  13. 13.
    Ratinaud P, Marchand P. Application de la méthode ALCESTE à de «  gros  » corpus et stabilité des «  mondes lexicaux  »  : analyse du «  CableGate  » avec IRaMuTeQ  » . Actes des 11eme Journées internationales d’Analyse statistique des Données Textuelles; 2012. p. 835–844.Google Scholar
  14. 14.
    Flament C. Similarity analysis: a technique for researches in social representations. Cah Psychol Cogn. 1981;1:375–95.Google Scholar
  15. 15.
    Frutcherman TMJ, Reingold EM. Graphed drawing by force directed placement. Softw Pract Exp. 1991;21:1129–64.CrossRefGoogle Scholar
  16. 16.
    Brandes U. A faster algorithm for betweenness centrality. J Math Sociol. 2001;25:163–77.CrossRefGoogle Scholar
  17. 17.
    Reinert A. Une méthode de classification descendante hiérarchique : application à l’analyse lexicale par contexte. Cah. L’analyse Données. 1983;8(2):187–98.Google Scholar
  18. 18.
    Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ. 2009;339:b2535.PubMedPubMedCentralCrossRefGoogle Scholar
  19. 19.
    Jay N, Nuemi G, Gadreau M, Quantin C. A data mining approach for grouping and analyzing trajectories of care using claim data: the example of breast cancer. BMC Med Inf Decis Mak. 2013;13:130.CrossRefGoogle Scholar
  20. 20.
    Burke T, Manglani Y, Altawil Z, Dickson A, Clark R, Okelo S, et al. A safe-anesthesia innovation for emergency and life-improving surgeries when no anesthetist is available: a descriptive review of 193 consecutive surgeries. World J Surg. 2015;39:2147–52.PubMedCrossRefGoogle Scholar
  21. 21.
    Cresci S, Wu J, Province MA, Spertus JA, Steffes M, McGill JB, et al. Peroxisome proliferator-activated receptor pathway gene polymorphism associated with extent of coronary artery disease in patients with type 2 diabetes in the bypass angioplasty revascularization investigation 2 diabetes trial. Circulation. 2011;124:1426–34.PubMedPubMedCentralCrossRefGoogle Scholar
  22. 22.
    Davis LA, Polk B, Mann A, Wolff RK, Kerr GS, Reimold AM, et al. Folic acid pathway single nucleotide polymorphisms associated with methotrexate significant adverse events in United States veterans with rheumatoid arthritis. Clin Exp Rheumatol. 2014;32:324–32.PubMedPubMedCentralGoogle Scholar
  23. 23.
    Park JY, Lee S-H, Shin M-J, Hwang G-S. Alteration in metabolic signature and lipid metabolism in patients with angina pectoris and myocardial infarction. PLoS ONE. 2015;10:e0135228.PubMedPubMedCentralCrossRefGoogle Scholar
  24. 24.
    Pedersen ER, Tuseth N, Eussen SJPM, Ueland PM, Strand E, Svingen GFT, et al. Associations of plasma kynurenines with risk of acute myocardial infarction in patients with stable angina pectoris. Arterioscler Thromb Vasc Biol. 2015;35:455–62.PubMedCrossRefGoogle Scholar
  25. 25.
    Peters BJM, Pett H, Klungel OH, Stricker BHC, Psaty BM, Glazer NL, et al. Genetic variability within the cholesterol lowering pathway and the effectiveness of statins in reducing the risk of MI. Atherosclerosis. 2011;217:458–64.PubMedPubMedCentralCrossRefGoogle Scholar
  26. 26.
    Suresh R, Li X, Chiriac A, Goel K, Terzic A, Perez-Terzic C, et al. Transcriptome from circulating cells suggests dysregulated pathways associated with long-term recurrent events following first-time myocardial infarction. J Mol Cell Cardiol. 2014;74:13–21.PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Nubukpo P. Place of the opioid system in biology and treatment of alcohol use disorder. L’Encéphale. 2014;40:457–67.PubMedCrossRefGoogle Scholar
  28. 28.
    Tada Y, Hiroshima K, Shimada H, Morishita N, Shirakawa T, Matsumoto K, et al. A clinical protocol to inhibit the HGF/c-Met pathway for malignant mesothelioma with an intrapleural injection of adenoviruses expressing the NK4 gene. SpringerPlus. 2015;4:358.PubMedPubMedCentralCrossRefGoogle Scholar
  29. 29.
    Zhang Y, Nester CM, Martin B, Skjoedt M-O, Meyer NC, Shao D, et al. Defining the complement biomarker profile of C3 glomerulopathy. Clin J Am Soc Nephrol CJASN. 2014;9:1876–82.PubMedCrossRefGoogle Scholar
  30. 30.
    Guldbrandt LM, Fenger-Grøn M, Rasmussen TR, Jensen H, Vedsted P. The role of general practice in routes to diagnosis of lung cancer in Denmark: a population-based study of general practice involvement, diagnostic activity and diagnostic intervals. BMC Health Serv Res. 2015;15:21.PubMedPubMedCentralCrossRefGoogle Scholar
  31. 31.
    Harlos C, Musto G, Lambert P, Ahmed R, Pitz MW. Androgen pathway manipulation and survival in patients with lung cancer. Horm Cancer. 2015;6:120–7.PubMedCrossRefGoogle Scholar
  32. 32.
    Jensen AB, Moseley PL, Oprea TI, Ellesøe SG, Eriksson R, Schmock H, et al. Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. Nat Commun. 2014;5:4022.PubMedPubMedCentralGoogle Scholar
  33. 33.
    Danielsson U, Bengs C, Lehti A, Hammarström A, Johansson EE. Struck by lightning or slowly suffocating-gendered trajectories into depression. BMC Fam Pract. 2009;10:56.PubMedPubMedCentralCrossRefGoogle Scholar
  34. 34.
    Jiwa M, Maujean E, Spilsbury K, Threlfal T. The trajectory of lung cancer patients in Western Australia—a data linkage study: still a grim tale. Lung Cancer. 2010;70:22–7.PubMedCrossRefGoogle Scholar
  35. 35.
    Schwartz CE, Quaranto BR, Healy BC, Benedict RH, Vollmer TL. Cognitive reserve and symptom experience in multiple sclerosis: a buffer to disability progression over time? Arch Phys Med Rehabil. 2013;94:1971–81.PubMedCrossRefGoogle Scholar
  36. 36.
    Sieberg CB, Simons LE, Edelstein MR, DeAngelis MR, Pielech M, Sethna N, et al. Pain prevalence and trajectories following pediatric spinal fusion surgery. J Pain. 2013;14:1694–702.PubMedCrossRefGoogle Scholar
  37. 37.
    Gebregziabher M, Egede LE, Lynch CP, Echols C, Zhao Y. Effect of trajectories of glycemic control on mortality in type 2 diabetes: a semiparametric joint modeling approach. Am J Epidemiol. 2010;171:1090–8.PubMedPubMedCentralCrossRefGoogle Scholar
  38. 38.
    Palmer WL, Bottle A, Davie C, Vincent CA, Aylin P. Meeting the ambition of measuring the quality of hospitals’ stroke care using routinely collected administrative data: a feasibility study. Int J Qual Health Care. 2013;25:429–36.PubMedPubMedCentralCrossRefGoogle Scholar
  39. 39.
    Aeyels D, Van Vugt S, Sinnaeve PR, Panella M, Van Zelm R, Sermeus W. Lack of evidence and standardization in care pathway documents for patients with ST-elevated myocardial infarction. Eur J Cardiovasc Nurs. 2015;15:45–51.CrossRefGoogle Scholar
  40. 40.
    Kesavan S, Kelay T, Collins RE, Cox B, Bello F, Kneebone RL, et al. Clinical information transfer and data capture in the acute myocardial infarction pathway: an observational study. J Eval Clin Pract. 2013;19:805–11.PubMedGoogle Scholar
  41. 41.
    Biffl WL, Smith WR, Moore EE, Gonzalez RJ, Morgan SJ, Hennessey T, et al. Evolution of a multidisciplinary clinical pathway for the management of unstable patients with pelvic fractures. Ann Surg. 2001;233:843–50.PubMedPubMedCentralCrossRefGoogle Scholar
  42. 42.
    Diaz RJ, Laughlin S, Nicolin G, Buncic JR, Bouffet E, Bartels U. Assessment of chemotherapeutic response in children with proptosis due to optic nerve glioma. Child’s Nerv Syst. 2008;24:707–12.CrossRefGoogle Scholar
  43. 43.
    Myers V, Drory Y, Gerber Y. Israel study group on first acute myocardial infarction. Clinical relevance of frailty trajectory post myocardial infarction. Eur J Prev Cardiol. 2014;21:758–66.PubMedCrossRefGoogle Scholar
  44. 44.
    Wang W, McKinnie SMK, Patel VB, Haddad G, Wang Z, Zhabyeyev P, et al. Loss of Apelin exacerbates myocardial infarction adverse remodeling and ischemia-reperfusion injury: therapeutic potential of synthetic Apelin analogues. J Am Heart Assoc. 2013;2:e000249.PubMedPubMedCentralGoogle Scholar
  45. 45.
    Ginzburg K, Solomon Z, Koifman B, Keren G, Roth A, Kriwisky M, et al. Trajectories of posttraumatic stress disorder following myocardial infarction: a prospective study. J Clin Psychiatry. 2003;64:1217–23.PubMedCrossRefGoogle Scholar
  46. 46.
    Kinsman LD, Rotter T, Willis J, Snow PC, Buykx P, Humphreys JS. Do clinical pathways enhance access to evidence-based acute myocardial infarction treatment in rural emergency departments? Aust J Rural Health. 2012;20:59–66.PubMedCrossRefGoogle Scholar
  47. 47.
    Kristoffersen DT, Helgeland J, Waage HP, Thalamus J, Clemens D, Lindman AS, et al. Survival curves to support quality improvement in hospitals with excess 30-day mortality after acute myocardial infarction, cerebral stroke and hip fracture: a before-after study. BMJ Open. 2015;5:e006741.PubMedPubMedCentralCrossRefGoogle Scholar
  48. 48.
    Bestul MB, McCollum M, Stringer KA, Burchenal J. Impact of a critical pathway on acute myocardial infarction quality indicators. Pharmacotherapy. 2004;24:173–8.PubMedCrossRefGoogle Scholar
  49. 49.
    Kucenic MJ, Meyers DG. Impact of a clinical pathway on the care and costs of myocardial infarction. Angiology. 2000;51:393–404.PubMedCrossRefGoogle Scholar
  50. 50.
    Mazzini MJ, Stevens GR, Whalen D, Ozonoff A, Balady GJ. Effect of an American Heart Association get with the guidelines program-based clinical pathway on referral and enrollment into cardiac rehabilitation after acute myocardial infarction. Am J Cardiol. 2008;101:1084–7.PubMedCrossRefGoogle Scholar
  51. 51.
    Pelliccia F, Cartoni D, Verde M, Salvini P, Mercuro G, Tanzi P. Critical pathways in the emergency department improve treatment modalities for patients with ST-elevation myocardial infarction in a European hospital. Clin Cardiol. 2004;27:698–700.PubMedCrossRefGoogle Scholar
  52. 52.
    Smith ORF, Kupper N, Denollet J, de Jonge P. Vital exhaustion and cardiovascular prognosis in myocardial infarction and heart failure: predictive power of different trajectories. Psychol Med. 2011;41:731–8.PubMedCrossRefGoogle Scholar
  53. 53.
    Hagiwara MA, Bremer A, Claesson A, Axelsson C, Norberg G, Herlitz J. The impact of direct admission to a catheterisation lab/CCU in patients with ST-elevation myocardial infarction on the delay to reperfusion and early risk of death: results of a systematic review including meta-analysis. Scand J Trauma Resusc Emerg Med. 2014;22:67.PubMedPubMedCentralCrossRefGoogle Scholar
  54. 54.
    O’Donnell S, Condell S, Begley C, Fitzgerald T. Prehospital care pathway delays: gender and myocardial infarction. J Adv Nurs. 2006;53:268–76.PubMedCrossRefGoogle Scholar
  55. 55.
    Lewis EF, Li Y, Pfeffer MA, Solomon SD, Weinfurt KP, Velazquez EJ, et al. Impact of cardiovascular events on change in quality of life and utilities in patients after myocardial infarction: a VALIANT study (Valsartan in acute myocardial infarction). JACC Heart Fail. 2014;2:159–65.PubMedCrossRefGoogle Scholar
  56. 56.
    Rankin SH, de Leon JF, Chen J-L, Butzlaff A, Carroll DL. Recovery trajectory of unpartnered elders after myocardial infarction: an analysis of daily diaries. Rehabil Nurs. 2002;27:95–102.PubMedCrossRefGoogle Scholar
  57. 57.
    Dharmarajan K, Hsieh AF, Kulkarni VT, Lin Z, Ross JS, Horwitz LI, et al. Trajectories of risk after hospitalization for heart failure, acute myocardial infarction, or pneumonia: retrospective cohort study. BMJ. 2015;350:h411.PubMedPubMedCentralCrossRefGoogle Scholar
  58. 58.
    Sundberg T, Petzold M, Kohls N, Falkenberg T. Opposite drug prescription and cost trajectories following integrative and conventional care for pain—a case-control study. PLoS ONE. 2014;9:e96717.PubMedPubMedCentralCrossRefGoogle Scholar
  59. 59.
    Couchoud C, Couillerot A-L, Dantony E, Elsensohn M-H, Labeeuw M, Villar E, et al. Economic impact of a modification of the treatment trajectories of patients with end-stage renal disease. Nephrol Dial Transpl. 2015;30:2054–68.CrossRefGoogle Scholar
  60. 60.
    Bossuyt N, Van Casteren V, Goderis G, Wens J, Moreels S, Vanthomme K, et al. Public Health Triangulation to inform decision-making in Belgium. Stud Health Technol Inf. 2015;210:855–9.Google Scholar
  61. 61.
    Thomas J, McNaught J, Ananiadou S. Applications of text mining within systematic reviews. Res Synth Methods. 2011;2:1–14.PubMedCrossRefGoogle Scholar
  62. 62.
    Frantzi K, Ananiadou S, Mima H. Automatic recognition of multi-word terms: the C-value/NC-value method. Int J Digit Libr. 2000;3:115–30.CrossRefGoogle Scholar
  63. 63.
    Frunza O, Inkpen D, Matwin S, Klement W, O’Blenis P. Exploiting the systematic review protocol for classification of medical abstracts. Artif Intell Med. 2011;51:17–25.PubMedCrossRefGoogle Scholar
  64. 64.
    Joachims T. Text categorization with support vector machines: learning with many relevant. In: ECML-98; 1998. p. 137–142.Google Scholar
  65. 65.
    Sebastiani F. Machine learning in automated text categorization. ACM Comput Surv. 2002;34:1–47.CrossRefGoogle Scholar
  66. 66.
    Mo Y, Kontonatsios G, Ananiadou S. Supporting systematic reviews using LDA-based document representations. Syst Rev. 2015;4:175.CrossRefGoogle Scholar
  67. 67.
    Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. J Mach Learn Res. 2003;3:993–1022.Google Scholar
  68. 68.
    Bada M. Mapping of biomedical text to concepts of lexicons, terminologies, and ontologies. Biomed Lit Min. 2014;1159:33–45.CrossRefGoogle Scholar
  69. 69.
    Bollegala D, Okazaki N, Ishizuka M. A bottom-up approach to sentence ordering for multi-document summarization. Inf Process Manag. 2010;46:89–109.CrossRefGoogle Scholar
  70. 70.
    Lin JM, Bohland JW, Andrews P, Burns GA, Allen CB, Mitra PP. An analysis of the abstracts presented at the annual meetings of the Society for Neuroscience from 2001 to 2006. PLoS ONE. 2008;3:e2052.PubMedPubMedCentralCrossRefGoogle Scholar
  71. 71.
    Leitner F, Valencia A. A text-mining perspective on the requirements for electronically annotated abstracts. FEBS Lett. 2008;582:1178–81.PubMedCrossRefGoogle Scholar
  72. 72.
    O’Mara-Eves A, Thomas J, McNaught J, Miwa M, Ananiadou S. Using text mining for study identification in systematic reviews: a systematic review of current approaches. Syst Rev. 2015;4:5.PubMedPubMedCentralCrossRefGoogle Scholar
  73. 73.
    Paynter R, Bañez LL, Berliner E, Erinoff E, Lege-Matsuura J, Potter S, et al. EPC methods: an exploration of the use of text-mining software in systematic reviews. Rockville: Agency for Healthcare Research and Quality (US); 2016.Google Scholar
  74. 74.
    Jonnalagadda SR, Goyal P, Huffman MD. Automating data extraction in systematic reviews: a systematic review. Syst Rev. 2015;4:78.PubMedPubMedCentralCrossRefGoogle Scholar
  75. 75.
    Lefebvre C, Glanville J, Wieland LS, Coles B, Weightman AL. Methodological developments in searching for studies for systematic reviews: past, present and future? Syst Rev. 2013;2:78.PubMedPubMedCentralCrossRefGoogle Scholar
  76. 76.
    Teich E, Fankhauser P. Exploring a corpus of scientific texts using data mining. Lang Comput. 2009;71:233–47.Google Scholar
  77. 77.
    Lebart L, Salem A, Berry L. Exploring textual data. Text Speech and Language Technology, vol. 4. Dordrecht: Kluwer; 1998.CrossRefGoogle Scholar
  78. 78.
    Van Eck NJ, Waltman L. Text mining and visualization using VOSviewer. ISSI NewLetter. 2011;4:51–4.Google Scholar
  79. 79.
    Greene D, O’Callaghan D, Cunningham P. How many topics? Stability analysis for topic models. In: Machine learning and knowledge discovery in databases. Springer, New York; 2014. p. 498–513.Google Scholar
  80. 80.
    Zhao W, Chen JJ, Perkins R, Liu Z, Ge W, Ding Y, et al. A heuristic approach to determine an appropriate number of topics in topic modeling. BMC Bioinform. 2015;16:S8.CrossRefGoogle Scholar
  81. 81.
    Defossez G, Rollet A, Dameron O, Ingrand P. Temporal representation of care trajectories of cancer patients using data from a regional information system: an application in breast cancer. BMC Med Inf Decis Mak. 2014;14:24.CrossRefGoogle Scholar
  82. 82.
    Skinner I, Smith C, Jaffray L. Realist review to inform development of the electronic advance care plan for the personally controlled electronic health record in Australia. Telemed J E-Health. 2014;20:1042–8.PubMedCrossRefGoogle Scholar
  83. 83.
    Dent M, Tutt D. Electronic patient information systems and care pathways: the organisational challenges of implementation and integration. Health Inf J. 2014;20:176–88.CrossRefGoogle Scholar
  84. 84.
    Waterson P, Eason K, Tutt D, Dent M. Using HIT to deliver integrated care for the frail elderly in the UK: current barriers and future challenges. Work. 2012;41(Suppl 1):4490–3.PubMedGoogle Scholar
  85. 85.
    Rabatel J, Bringay S, Poncelet P. Mining sequential patterns: a context-aware approach. Advanced knowledge discovery management. New York: Springer; 2013. p. 23–41.Google Scholar
  86. 86.
    Popp AJ, Scrime T, Cohen BR, Feustel PJ, Petronis K, Habiniak S, et al. Factors affecting profitability for craniotomy. Neurosurg Focus. 2002;12:e4.PubMedCrossRefGoogle Scholar
  87. 87.
    Ricciardi A, Largeron N, Giorgi Rossi P, Raffaele M, Cohet C, Federici A, et al. Incidence of invasive cervical cancer and direct costs associated with its management in Italy. Tumori. 2009;95:146–52.PubMedGoogle Scholar
  88. 88.
    Bettencourt-Silva JH, Clark J, Cooper CS, Mills R, Rayward-Smith VJ, de la Iglesia B. Building data-driven pathways from routinely collected hospital data: a case study on prostate cancer. JMIR Med Inf. 2015;3:e26.CrossRefGoogle Scholar
  89. 89.
    Jensen H, Sperling C, Sandager M, Vedsted P. Agreement between patients and general practitioners on quality deviations during the cancer diagnostic pathway and associations with time to diagnosis. Fam Pract. 2015;32:329–35.PubMedCrossRefGoogle Scholar
  90. 90.
    Palmer J, Bozas G, Stephens A, Johnson M, Avery G, O’Toole L, et al. Developing a complex intervention for the outpatient management of incidentally diagnosed pulmonary embolism in cancer patients. BMC Health Serv Res. 2013;13:235.PubMedPubMedCentralCrossRefGoogle Scholar
  91. 91.
    Thompson CA, Kurian AW, Luft HS. Linking electronic health records to better understand breast cancer patient pathways within and between two health systems. eGEMS. 2015;3:1127.PubMedPubMedCentralGoogle Scholar
  92. 92.
    Ellis E, Ballance K, Lunt H, Lewis D. Diabetes outpatient care before and after admission for diabetic foot complications. J Wound Care. 2010;19:150–2.PubMedCrossRefGoogle Scholar
  93. 93.
    Myklebust LH, Sørgaard KW, Bjorbekkmo S, Eisemann MR, Olstad R. Time-trends in the utilization of decentralized mental health services in Norway—a natural experiment: the VELO-project. Int J Ment Health Syst. 2010;4:5.PubMedPubMedCentralCrossRefGoogle Scholar
  94. 94.
    Song L, Yan H, Hu D, Yang J, Sun Y. Pre-hospital care-seeking in patients with acute myocardial infarction and subsequent quality of care in Beijing. Chin Med J (Engl). 2010;123:664–9.Google Scholar
  95. 95.
    Young W, McShane J, O’Connor T, Rewa G, Goodman S, Jaglal SB, et al. Registered nurses’ experiences with an evidence-based home care pathway for myocardial infarction clients. Can J Cardiovasc Nurs. 2004;14:24–31.PubMedGoogle Scholar
  96. 96.
    Arko FR, Bohannon WT, Mettauer M, Lee SD, Patterson DE, Manning LG, et al. Retroperitoneal approach for aortic surgery: is it worth it? Cardiovasc Surg Lond Engl. 2001;9:20–6.CrossRefGoogle Scholar
  97. 97.
    Baade PD, Youl PH, English DR, Mark Elwood J, Aitken JF. Clinical pathways to diagnose melanoma: a population-based study. Melanoma Res. 2007;17:243–9.PubMedCrossRefGoogle Scholar
  98. 98.
    Buckley CJ, Lee SD, Arko FR, Bohannon WT, Mettauer M, Patterson DE, et al. Economic considerations for aortic surgery: retroperitoneal approach—is it worth it? Acta Chir Belg. 2000;100:247–50.PubMedGoogle Scholar
  99. 99.
    Ghosh K, Downs LS, Padilla LA, Murray KP, Twiggs LB, Letourneau CM, et al. The implementation of critical pathways in gynecologic oncology in a managed care setting: a cost analysis. Gynecol Oncol. 2001;83:378–82.PubMedCrossRefGoogle Scholar
  100. 100.
    Goderis G, Van Casteren V, Declercq E, Bossuyt N, Van Den Broeke C, Vanthomme K, et al. Care trajectories are associated with quality improvement in the treatment of patients with uncontrolled type 2 diabetes: a registry based cohort study. Prim Care Diabetes. 2015;9:354–61.PubMedCrossRefGoogle Scholar
  101. 101.
    Kinsman LD, Buykx P, Humphreys JS, Snow PC, Willis J. A cluster randomised trial to assess the impact of clinical pathways on AMI management in rural Australian emergency departments. BMC Health Serv Res. 2009;9:83.PubMedPubMedCentralCrossRefGoogle Scholar
  102. 102.
    Krummenauer F, Guenther K-P, Kirschner S. Cost effectiveness of total knee arthroplasty from a health care providers’ perspective before and after introduction of an interdisciplinary clinical pathway—is investment always improvement? BMC Health Serv Res. 2011;11:338.PubMedPubMedCentralCrossRefGoogle Scholar
  103. 103.
    Miller PR, Fabian TC, Croce MA, Magnotti LJ, Elizabeth Pritchard F, Minard G, et al. Improving outcomes following penetrating colon wounds: application of a clinical pathway. Ann Surg. 2002;235:775–81.PubMedPubMedCentralCrossRefGoogle Scholar
  104. 104.
    Naqvi HA, Hussain S, Zaman M, Islam M. Pathways to care: duration of untreated psychosis from Karachi, Pakistan. PLoS ONE. 2009;4:e7409.PubMedPubMedCentralCrossRefGoogle Scholar
  105. 105.
    Tang W, Sun X, Zhang Y, Ye T, Zhang L. How to build and evaluate an integrated health care system for chronic patients: study design of a clustered randomised controlled trial in rural China. Int J Integr Care. 2015;15:e007.PubMedPubMedCentralCrossRefGoogle Scholar
  106. 106.
    van Hoeve J, de Munck L, Otter R, de Vries J, Siesling S. Quality improvement by implementing an integrated oncological care pathway for breast cancer patients. Breast Edinb Scotl. 2014;23:364–70.CrossRefGoogle Scholar
  107. 107.
    Park YS, Chung SP, Chung HS, Lee HS, You JS. Implementation of a clinical pathway based on a computerized physician order entry system for ischemic stroke attenuates off-hour and weekend effects in the ED. Am J Emerg Med. 2014;32:884–9.PubMedCrossRefGoogle Scholar
  108. 108.
    Laut KG, Foldspang A. The effects on length of stay of introducing a fast track patient pathway for myocardial infarction: a before and after evaluation. Health Serv Manag Res. 2012;25:31–4.CrossRefGoogle Scholar
  109. 109.
    Ahmed S, Mayo N, Scott S, Kuspinar A, Schwartz C. Using latent trajectory analysis of residuals to detect response shift in general health among patients with multiple sclerosis article. Qual Life Res Int J. 2011;20:1555–60.CrossRefGoogle Scholar
  110. 110.
    Cocchi A, Meneghelli A, Erlicher A, Pisano A, Cascio MT, Preti A. Patterns of referral in first-episode schizophrenia and ultra high-risk individuals: results from an early intervention program in Italy. Soc Psychiatry Psychiatr Epidemiol. 2013;48:1905–16.PubMedCrossRefGoogle Scholar
  111. 111.
    Dely C, Sellier P, Dozol A, Segouin C, Moret L, Lombrail P. Preventable readmissions of “community-acquired pneumonia”: usefulness and reliability of an indicator of the quality of care of patients’ care pathways. Presse Médicale. 1983;2012(41):e1–9.Google Scholar
  112. 112.
    Klinkhammer-Schalke M, Lindberg P, Koller M, Wyatt JC, Hofstädter F, Lorenz W, et al. Direct improvement of quality of life in colorectal cancer patients using a tailored pathway with quality of life diagnosis and therapy (DIQOL): study protocol for a randomised controlled trial. Trials. 2015;16:460.PubMedPubMedCentralCrossRefGoogle Scholar
  113. 113.
    Mastenbroek MH, Denollet J, Versteeg H, van den Broek KC, Theuns DAMJ, Meine M, et al. Trajectories of patient-reported health status in patients with an implantable cardioverter defibrillator. Am J Cardiol. 2015;115:771–7.PubMedCrossRefGoogle Scholar
  114. 114.
    Strömberg A, Fluur C, Miller J, Chung ML, Moser DK, Thylén I. ICD recipients’ understanding of ethical issues, ICD function, and practical consequences of withdrawing the ICD in the end-of-life. Pacing Clin Electrophysiol PACE. 2014;37:834–42.PubMedCrossRefGoogle Scholar
  115. 115.
    Veloso AG, Sperling C, Holm LV, Nicolaisen A, Rottmann N, Thayssen S, et al. Unmet needs in cancer rehabilitation during the early cancer trajectory—a nationwide patient survey. Acta Oncol Stockh Swed. 2013;52:372–81.CrossRefGoogle Scholar
  116. 116.
    Martens EJ, Smith ORF, Winter J, Denollet J, Pedersen SS. Cardiac history, prior depression and personality predict course of depressive symptoms after myocardial infarction. Psychol Med. 2008;38:257–64.PubMedGoogle Scholar
  117. 117.
    Noble SI, Nelson A, Fitzmaurice D, Bekkers M-J, Baillie J, Sivell S, et al. A feasibility study to inform the design of a randomised controlled trial to identify the most clinically effective and cost-effective length of anticoagulation with low-molecular-weight heparin in the treatment of Cancer-Associated Thrombosis (ALICAT). Health Technol Assess Winch Engl. 2015;19:1–94.CrossRefGoogle Scholar
  118. 118.
    Gerber Y, Benyamini Y, Goldbourt U, Drory Y. Israel Study Group on First Acute Myocardial Infarction. Prognostic importance and long-term determinants of self-rated health after initial acute myocardial infarction. Med Care. 2009;47:342–9.PubMedCrossRefGoogle Scholar
  119. 119.
    Jayanti A, Wearden AJ, Morris J, Brenchley P, Abma I, Bayer S, et al. Barriers to successful implementation of care in home haemodialysis (BASIC-HHD):1. Study design, methods and rationale. BMC Nephrol. 2013;14:197.PubMedPubMedCentralCrossRefGoogle Scholar
  120. 120.
    Sverrisson EF, Zens MS, Fei DL, Andrews A, Schned A, Robbins D, et al. Clinicopathological correlates of Gli1 expression in a population-based cohort of patients with newly diagnosed bladder cancer. Urol Oncol. 2014;32:539–45.PubMedPubMedCentralCrossRefGoogle Scholar
  121. 121.
    Myers V, Drory Y, Gerber Y. Israel Study Group on First Acute Myocardial Infarction. Sense of coherence predicts post-myocardial infarction trajectory of leisure time physical activity: a prospective cohort study. BMC Public Health. 2011;11:708.PubMedPubMedCentralCrossRefGoogle Scholar
  122. 122.
    Gerber Y, Myers V, Goldbourt U, Benyamini Y, Scheinowitz M, Drory Y. Long-term trajectory of leisure time physical activity and survival after first myocardial infarction: a population-based cohort study. Eur J Epidemiol. 2011;26:109–16.PubMedCrossRefGoogle Scholar
  123. 123.
    Rosenfeld AG. Treatment-seeking delay among women with acute myocardial infarction: decision trajectories and their predictors. Nurs Res. 2004;53:225–36.PubMedCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jessica Pinaire
    • 1
    • 2
    • 3
  • Jérôme Azé
    • 3
  • Sandra Bringay
    • 3
    • 4
  • Paul Landais
    • 1
    • 2
  1. 1.Biostatistics, Epidemiology and Public Health DepartmentNîmes University HospitalNîmesFrance
  2. 2.UPRES EA 2415, Clinical Research University InstituteMontpellierFrance
  3. 3.LIRMM, UMR 5506Montpellier UniversityMontpellier Cedex 5France
  4. 4.AMISPaul Valéry UniversityMontpellierFrance

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